{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9dc175c8-f744-4655-9037-b313c486c951",
   "metadata": {},
   "source": [
    "This notebook is used for the creation of model boundary conditions for the Alaska North Slop model. Just as for the initial condition, the boundary condition vector quantities need to rotated on the curvilinear model domain. \n",
    "\n",
    "First, import packages necessary for this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "836f57c0-6983-48ce-b8a0-31c5b04463a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the modules for computation, plotting, and reading files\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc4\n",
    "import cmocean.cm as cm\n",
    "\n",
    "# import the necessary modules from eccoseas\n",
    "from eccoseas.ecco import io\n",
    "from eccoseas.ecco import grid as eeg\n",
    "from eccoseas.downscale import hFac\n",
    "from eccoseas.downscale import horizontal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea43d2c-8106-4b7e-9d71-dda71ff556e4",
   "metadata": {},
   "source": [
    "## Constructing the Boundary Conditions\n",
    "For this model, we will use a model state output from the ECCO-Darwin Version 5 state estimate. This output was generated using [diagnostic_vec](https://github.com/mhwood/diagnostics_vec) masks for output only around the edges of the model domain. Here, we will create the boundary conditions in the following steps:\n",
    "1. download the model output\n",
    "2. read the ECCO model grid\n",
    "3. read in the bathymetry for the regional model as well as its grid\n",
    "4. prepare the ECCO-Darwin fields for interpolation\n",
    "5. interpolate the ECCO-Darwin fields onto the regional model grid and store each as a binary file\n",
    "6. plot the interpolated fields to ensure they look as expected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5328af-a028-439b-a89a-4f39b72a9a65",
   "metadata": {},
   "source": [
    "### Step 1: Download the ECCO fields\n",
    "To begin, I downloaded the model output fields generated by the ECCO-Darwin state estimate. \n",
    "\n",
    "There are stored in the following directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34d9856c-ec0b-4f6e-9f9c-c18a73cd6858",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = '../../../configurations/run/north_atlantic'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d170a9e7-8862-4e25-b732-1268f2c77c5a",
   "metadata": {},
   "source": [
    "### Step 2: Read in the ECCO grid\n",
    "To read in the ECCO fields, we will rely on the `io` module from the `eccoseas.ecco` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd0a9154-f2c1-41ab-85b1-13ecc5f01602",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = '../../../data/north_atlantic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a925b6ec-93ab-429b-a830-d6591b15a327",
   "metadata": {},
   "outputs": [],
   "source": [
    "ecco_XC_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='XC')\n",
    "ecco_YC_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='YC')\n",
    "ecco_AngleCS_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='AngleCS')\n",
    "ecco_AngleSN_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='AngleSN')\n",
    "ecco_hFacC_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='hFacC')\n",
    "ecco_hFacW_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='hFacW')\n",
    "ecco_hFacS_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='hFacS')\n",
    "ecco_RF_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='RF')\n",
    "ecco_DRF_tiles = io.read_ecco_grid_tiles_from_nc(os.path.join(data_folder, 'GRID'), var_name='DRF')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bcf96ac-d0e5-4e53-884f-16e0fec32152",
   "metadata": {},
   "source": [
    "Note that in the previous notebook, we have already identified tiles 3, 7, and 11 as those pertaining to the regional domain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a135ac-a5bb-4766-83b5-60039dcc0c56",
   "metadata": {},
   "source": [
    "### Step 3: Read in the Model Grid and Generate a Mask\n",
    "Here, I will recreate the grid I will use in my model and read in the bathymetry file (see previous notebooks for details):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fe70cd2-1e3a-4045-8fc7-cd88e9777859",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the input directory (see previous notebook for details)\n",
    "input_dir = '../../../configurations/north_atlantic/input'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f48b8cc3-eb40-40c3-b61e-1359d986109d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the grids that will be used in the model\n",
    "ds = nc4.Dataset(os.path.join(input_dir,'north_atlantic_grid.nc'))\n",
    "XC = ds.variables['XC'][:,:]\n",
    "YC = ds.variables['YC'][:,:]\n",
    "bathy = -1*ds.variables['Depth'][:,:]\n",
    "AngleCS = ds.variables['AngleCS'][:,:]\n",
    "AngleSN = ds.variables['AngleSN'][:,:]\n",
    "hFacC = ds.variables['HFacC'][:,:]\n",
    "hFacS = ds.variables['HFacS'][:,:]\n",
    "hFacW = ds.variables['HFacW'][:,:]\n",
    "delR = ds.variables['drF'][:]\n",
    "ds.close()\n",
    "\n",
    "# remove the extra row and col from hFacS and hFacW\n",
    "hFacS = hFacS[:,:-1,:]\n",
    "hFacW = hFacW[:,:,:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb82b3d-ffa2-4312-916f-3325a0089c6b",
   "metadata": {},
   "source": [
    "As in the initial condition notebook, we will make masks by setting all of the non-zero `hFac` points to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1dcfe0a-3641-43d7-99aa-88a48310eeda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the masks\n",
    "maskC = np.copy(hFacC)\n",
    "maskC[maskC>0] = 1\n",
    "\n",
    "maskS = np.copy(hFacS)\n",
    "maskS[maskS>0] = 1\n",
    "\n",
    "maskW = np.copy(hFacW)\n",
    "maskW[maskW>0] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37e0e64-13eb-4122-ae3c-0fead095fc9b",
   "metadata": {},
   "source": [
    "### Step 4: Prepare the grids for interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b332346e-acea-4d60-9494-8d850824b8f4",
   "metadata": {},
   "source": [
    "Next, we read in points from just the tiles overlapping out domain to use in interpolation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfd5f5ec-1a41-4da6-81aa-9d121dfa023a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the tile list\n",
    "tile_list = [3,7,11]\n",
    "\n",
    "# determine the number of points in each set\n",
    "total_points = 0\n",
    "for tile_number in tile_list:\n",
    "    total_points += np.size(ecco_XC_tiles[tile_number])\n",
    "\n",
    "# make empty arrays to fill in\n",
    "ecco_XC_points = np.zeros((total_points, ))\n",
    "ecco_YC_points = np.zeros((total_points, ))\n",
    "ecco_AngleCS_points = np.zeros((total_points, ))\n",
    "ecco_AngleSN_points = np.zeros((total_points, ))\n",
    "ecco_maskC_points = np.zeros((np.size(ecco_RF_tiles[1]) , total_points))\n",
    "ecco_maskW_points = np.zeros((np.size(ecco_RF_tiles[1]) , total_points))\n",
    "ecco_maskS_points = np.zeros((np.size(ecco_RF_tiles[1]) , total_points))\n",
    "ecco_hFacW_points = np.zeros((np.size(ecco_RF_tiles[1]) , total_points))\n",
    "ecco_hFacS_points = np.zeros((np.size(ecco_RF_tiles[1]) , total_points))\n",
    "\n",
    "# loop through the tiles and fill in the XC, YC, and mask points for interpolation\n",
    "points_counted = 0\n",
    "for tile_number in tile_list:\n",
    "    tile_N = np.size(ecco_XC_tiles[tile_number])\n",
    "    \n",
    "    ecco_XC_points[points_counted:points_counted+tile_N] = ecco_XC_tiles[tile_number].ravel()\n",
    "    ecco_YC_points[points_counted:points_counted+tile_N] = ecco_YC_tiles[tile_number].ravel()\n",
    "\n",
    "    ecco_AngleCS_points[points_counted:points_counted+tile_N] = ecco_AngleCS_tiles[tile_number].ravel()\n",
    "    ecco_AngleSN_points[points_counted:points_counted+tile_N] = ecco_AngleSN_tiles[tile_number].ravel()\n",
    "    \n",
    "    for k in range(np.size(ecco_RF_tiles[tile_number])):\n",
    "        level_hFacC = ecco_hFacC_tiles[tile_number][k, :, :]\n",
    "        if tile_number<7:\n",
    "            level_hFacW = ecco_hFacW_tiles[tile_number][k, :, :]\n",
    "            level_hFacS = ecco_hFacS_tiles[tile_number][k, :, :]\n",
    "        else:\n",
    "            level_hFacS = ecco_hFacW_tiles[tile_number][k, :, :] # these are switched due to the \n",
    "            level_hFacW = ecco_hFacS_tiles[tile_number][k, :, :] # assumptions about velocity - see note below\n",
    "        ecco_hFacW_points[k, points_counted:points_counted+tile_N] = level_hFacW.ravel()\n",
    "        ecco_hFacS_points[k, points_counted:points_counted+tile_N] = level_hFacS.ravel()\n",
    "        level_maskC = np.copy(level_hFacC)\n",
    "        level_maskC[level_maskC>0] = 1\n",
    "        level_maskW = np.copy(level_hFacW)\n",
    "        level_maskW[level_maskW>0] = 1\n",
    "        level_maskS = np.copy(level_hFacS)\n",
    "        level_maskS[level_maskS>0] = 1\n",
    "        ecco_maskC_points[k, points_counted:points_counted+tile_N] = level_maskC.ravel()\n",
    "        ecco_maskW_points[k, points_counted:points_counted+tile_N] = level_maskW.ravel()\n",
    "        ecco_maskS_points[k, points_counted:points_counted+tile_N] = level_maskS.ravel()\n",
    "    \n",
    "    points_counted += tile_N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4462677-8079-45e5-9faf-d39452350218",
   "metadata": {},
   "source": [
    "Next, we'll read in the real data fields and apply the modifications. First, create a dictionary to store the file names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4dbb96e5-bef6-4307-a5b3-f4441a930f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_file_dict(year):\n",
    "    file_prefix_dict = {'THETA':'THETA_'+str(year)+'.nc',\n",
    "                        'SALT':'SALT_'+str(year)+'.nc',\n",
    "                        'UVEL':'UVELMASS_'+str(year)+'.nc',\n",
    "                        'VVEL':'VVELMASS_'+str(year)+'.nc'}\n",
    "    variable_names = list(file_prefix_dict.keys())\n",
    "    return(variable_names, file_prefix_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf5206c-6f45-431e-9988-a1b007ce954e",
   "metadata": {},
   "source": [
    "Similarly, create a function to read in the variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6963b363-9245-4f9b-9f40-2b7318286460",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_init_grids(year, variable_names, file_prefix_dict):\n",
    "    # make a list to hold all of the ECCO grids\n",
    "    init_grids = []\n",
    "    timesteps = 12\n",
    "    \n",
    "    # loop through each variable to read in the grid\n",
    "    for variable_name in variable_names:\n",
    "    \n",
    "        print('  - Reading in the data for '+str(variable_name)+' in year '+str(year))\n",
    "        \n",
    "        if variable_name == 'ETAN' or variable_name in ['SIarea','SIheff','SIhsnow']:\n",
    "            ds = nc4.Dataset(os.path.join(data_folder,variable_name,file_prefix_dict[variable_name]))\n",
    "            grid = ds.variables[variable_name][:,:,:,:]\n",
    "            ds.close()\n",
    "        elif 'VEL' in variable_name:\n",
    "            ds = nc4.Dataset(os.path.join(data_folder,'UVELMASS','UVELMASS_'+str(year)+'.nc'))\n",
    "            u_grid = ds.variables['UVELMASS'][:,:,:,:,:]\n",
    "            ds.close()\n",
    "            ds = nc4.Dataset(os.path.join(data_folder,'VVELMASS','VVELMASS_'+str(year)+'.nc'))\n",
    "            v_grid = ds.variables['VVELMASS'][:,:,:,:,:]\n",
    "            ds.close()\n",
    "        elif 'ice' in variable_name:\n",
    "            ds = nc4.Dataset(os.path.join(data_folder,'SIuice','SIuice_'+str(year)+'.nc'))\n",
    "            u_grid = ds.variables['SIuice'][:,:,:,:]\n",
    "            ds.close()\n",
    "            ds = nc4.Dataset(os.path.join(data_folder,'SIvice','SIvice_'+str(year)+'.nc'))\n",
    "            v_grid = ds.variables['SIvice'][:,:,:,:]\n",
    "            ds.close()\n",
    "        else:\n",
    "            ds = nc4.Dataset(os.path.join(data_folder,variable_name,file_prefix_dict[variable_name]))\n",
    "            grid = ds.variables[variable_name][:,:,:,:,:]\n",
    "            ds.close()\n",
    "\n",
    "    \n",
    "        # rotate grids, if needed\n",
    "        if 'VEL' in variable_name:\n",
    "            grid = np.zeros_like(u_grid)\n",
    "            for timestep in range(timesteps):\n",
    "                for tile_number in tile_list:\n",
    "                    zonal_grid, meridional_grid = eeg.rotate_ecco_vel_grids_to_natural_grids(u_grid[timestep,:,tile_number-1,:,:], v_grid[timestep,:,tile_number-1,:,:],\n",
    "                                                                                         ecco_AngleCS_tiles[tile_number], ecco_AngleSN_tiles[tile_number])\n",
    "                    if variable_name=='UVEL':\n",
    "                        grid[timestep,:,tile_number-1,:,:] = zonal_grid\n",
    "                    if variable_name=='VVEL':\n",
    "                        grid[timestep,:,tile_number-1,:,:] = meridional_grid\n",
    "        if 'ice' in variable_name:\n",
    "            grid = np.zeros_like(u_grid)\n",
    "            for timestep in range(timesteps):\n",
    "                for tile_number in tile_list:\n",
    "                    zonal_grid, meridional_grid = eeg.rotate_ecco_vel_grids_to_natural_grids(u_grid[timestep,tile_number-1,:,:], v_grid[timestep,tile_number-1,:,:],\n",
    "                                                                                         ecco_AngleCS_tiles[tile_number], ecco_AngleSN_tiles[tile_number], has_depth=False)\n",
    "                    if variable_name=='SIuice':\n",
    "                        grid[timestep,tile_number-1,:,:] = zonal_grid\n",
    "                    if variable_name=='SIvice':\n",
    "                        grid[timestep,tile_number-1,:,:] = meridional_grid\n",
    "    \n",
    "        # create a grid of zeros to fill in\n",
    "        N = np.shape(grid)[-1]*np.shape(grid)[-2]\n",
    "        if variable_name == 'ETAN'  or variable_name in ['SIarea','SIheff','SIhsnow','SIuice','SIvice']:\n",
    "            init_grid = np.zeros((timesteps, 1, N*len(tile_list)))\n",
    "        else:\n",
    "            init_grid = np.zeros((timesteps, np.size(ecco_RF_tiles[1]), N*len(tile_list)))\n",
    "\n",
    "        # loop through the tiles\n",
    "        points_counted = 0\n",
    "        for tile_number in tile_list:\n",
    "            if variable_name in ['ETAN','SIarea','SIheff','SIhsnow','SIuice','SIvice']:\n",
    "                for timestep in timesteps:\n",
    "                    init_grid[timestep, 0, points_counted:points_counted+N] = \\\n",
    "                         grid[timestep, tile_number-1, :, :].ravel()\n",
    "            else:\n",
    "                for timestep in range(timesteps):\n",
    "                    for k in range(np.size(ecco_RF_tiles[1])):\n",
    "                        init_grid[timestep, k, points_counted:points_counted+N] = \\\n",
    "                             grid[timestep, k, tile_number-1, :, :].ravel()\n",
    "            points_counted += N\n",
    "    \n",
    "        # apply some corrections to convert UVELMASS and VVELMASS to UVEL and VVEL\n",
    "        if variable_name == 'UVEL':\n",
    "            for timestep in range(timesteps):\n",
    "                for k in range(np.size(ecco_RF_tiles[1])):\n",
    "                    non_zero_indices = ecco_hFacW_points[k,:]!=0\n",
    "                    init_grid[timestep, k,non_zero_indices] = init_grid[timestep, k,non_zero_indices]/(ecco_hFacW_points[k,non_zero_indices])\n",
    "        if variable_name == 'VVEL':\n",
    "            for timestep in range(timesteps):\n",
    "                for k in range(np.size(ecco_RF_tiles[1])):\n",
    "                    non_zero_indices = ecco_hFacS_points[k,:]!=0\n",
    "                    init_grid[timestep, k,non_zero_indices] = init_grid[timestep, k,non_zero_indices]/(ecco_hFacS_points[k,non_zero_indices])\n",
    "        \n",
    "        init_grids.append(init_grid)\n",
    "    \n",
    "    return(init_grids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e32043-a3a3-49df-a724-de2be9f98708",
   "metadata": {},
   "source": [
    "### Step 5: Interpolate the Fields onto the Model Grid\n",
    "Next, we will interpolate the ECCO external fields I read in onto the regional model domain. We will use the `horizontal` module from the `eccoseas` package to accomplish this interpolation.\n",
    "\n",
    "First, define which boundaries we will need (all of them in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6c081d0-77ac-4aba-8af7-baeb749a9a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the boundary list for the model\n",
    "boundary_list = ['west','south','north','east']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2595d28-7bb5-4c4f-83ef-2cf83147d401",
   "metadata": {},
   "source": [
    "Then, proceed with the interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c1028da-5a5b-4662-b772-73dadd97bd2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'obcs' not in os.listdir(input_dir):\n",
    "    os.mkdir(os.path.join(input_dir,'obcs'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d0aae164-a3f8-470b-a973-7f7ddffdcef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in the data for year 1993\n",
      "  - Reading in the data for THETA in year 1993\n",
      "  - Reading in the data for SALT in year 1993\n",
      "  - Reading in the data for UVEL in year 1993\n",
      "  - Reading in the data for VVEL in year 1993\n",
      "Creating the boundary conditions\n",
      "  - Working on the west boundary\n",
      "        - Interpolating the THETA grid\n",
      "        - Interpolating the SALT grid\n",
      "        - Interpolating the UVEL grid\n",
      "        - Interpolating the VVEL grid\n",
      "  - Working on the south boundary\n",
      "        - Interpolating the THETA grid\n",
      "        - Interpolating the SALT grid\n",
      "        - Interpolating the UVEL grid\n",
      "        - Interpolating the VVEL grid\n",
      "  - Working on the north boundary\n",
      "        - Interpolating the THETA grid\n",
      "        - Interpolating the SALT grid\n",
      "        - Interpolating the UVEL grid\n",
      "        - Interpolating the VVEL grid\n",
      "  - Working on the east boundary\n",
      "        - Interpolating the THETA grid\n",
      "        - Interpolating the SALT grid\n",
      "        - Interpolating the UVEL grid\n",
      "        - Interpolating the VVEL grid\n",
      "Reading in the data for year 1994\n",
      "  - Reading in the data for THETA in year 1994\n",
      "  - Reading in the data for SALT in year 1994\n",
      "  - Reading in the data for UVEL in year 1994\n",
      "  - Reading in the data for VVEL in year 1994\n",
      "Creating the boundary conditions\n",
      "  - Working on the west boundary\n",
      "        - Interpolating the THETA grid\n",
      "        - Interpolating the SALT grid\n",
      "        - Interpolating the UVEL grid\n",
      "        - Interpolating the VVEL grid\n",
      "  - Working on the south boundary\n",
      "        - Interpolating the THETA grid\n",
      "        - Interpolating the SALT grid\n",
      "        - Interpolating the UVEL grid\n",
      "        - Interpolating the VVEL grid\n",
      "  - Working on the north boundary\n",
      "        - Interpolating the THETA grid\n",
      "        - Interpolating the SALT grid\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_file):\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(timesteps):\n\u001b[0;32m---> 58\u001b[0m         interpolated_grid \u001b[38;5;241m=\u001b[39m \u001b[43mhorizontal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownscale_3D_points\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumn_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mecco_XC_points\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mecco_YC_points\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m                                                           \u001b[49m\u001b[43minit_grid\u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mecco_mask_points\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m                                                           \u001b[49m\u001b[43mboundary_XC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mboundary_YC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mboundary_mask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(delR)):\n\u001b[1;32m     62\u001b[0m             output_grid[t,k,:] \u001b[38;5;241m=\u001b[39m interpolated_grid[k,:,:]\u001b[38;5;241m.\u001b[39mravel()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/mitgcm/lib/python3.10/site-packages/eccoseas/downscale/horizontal.py:666\u001b[0m, in \u001b[0;36mdownscale_3D_points\u001b[0;34m(L0_points, L0_var, L0_wet_grid, XC_subset, YC_subset, L1_wet_grid, L0_wet_grid_on_L1, mean_vertical_difference, fill_downward, printing, remove_zeros, testing, use_legacy, verbose)\u001b[0m\n\u001b[1;32m    663\u001b[0m     L0_values \u001b[38;5;241m=\u001b[39m L0_values[L0_values \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    665\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(L0_points)\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m4\u001b[39m:\n\u001b[0;32m--> 666\u001b[0m     grid \u001b[38;5;241m=\u001b[39m \u001b[43mgriddata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mL0_points\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mL0_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mXC_subset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mYC_subset\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlinear\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    667\u001b[0m     \u001b[38;5;66;03m# grid = grid[:, :, 0]\u001b[39;00m\n\u001b[1;32m    668\u001b[0m     \u001b[38;5;66;03m# grid_nearest = griddata(L0_points, L0_values, (XC_subset, YC_subset), method='nearest', fill_value=0)\u001b[39;00m\n\u001b[1;32m    669\u001b[0m     \u001b[38;5;66;03m# grid_nearest[:,:,0]\u001b[39;00m\n\u001b[1;32m    670\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(grid\u001b[38;5;241m!=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/mitgcm/lib/python3.10/site-packages/scipy/interpolate/_ndgriddata.py:323\u001b[0m, in \u001b[0;36mgriddata\u001b[0;34m(points, values, xi, method, fill_value, rescale)\u001b[0m\n\u001b[1;32m    321\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ip(xi)\n\u001b[1;32m    322\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 323\u001b[0m     ip \u001b[38;5;241m=\u001b[39m \u001b[43mLinearNDInterpolator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m                              \u001b[49m\u001b[43mrescale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrescale\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    325\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ip(xi)\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcubic\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
      "File \u001b[0;32m_interpnd.pyx:302\u001b[0m, in \u001b[0;36mscipy.interpolate._interpnd.LinearNDInterpolator.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m_interpnd.pyx:93\u001b[0m, in \u001b[0;36mscipy.interpolate._interpnd.NDInterpolatorBase.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m_interpnd.pyx:306\u001b[0m, in \u001b[0;36mscipy.interpolate._interpnd.LinearNDInterpolator._calculate_triangulation\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m_qhull.pyx:1887\u001b[0m, in \u001b[0;36mscipy.spatial._qhull.Delaunay.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m_qhull.pyx:1596\u001b[0m, in \u001b[0;36mscipy.spatial._qhull._QhullUser.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m_qhull.pyx:1905\u001b[0m, in \u001b[0;36mscipy.spatial._qhull.Delaunay._update\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m_qhull.pyx:1625\u001b[0m, in \u001b[0;36mscipy.spatial._qhull._QhullUser._update\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/mitgcm/lib/python3.10/site-packages/numpy/core/_methods.py:43\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_amax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     40\u001b[0m           initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m umr_maximum(a, axis, \u001b[38;5;28;01mNone\u001b[39;00m, out, keepdims, initial, where)\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     44\u001b[0m           initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m umr_minimum(a, axis, \u001b[38;5;28;01mNone\u001b[39;00m, out, keepdims, initial, where)\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_sum\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     48\u001b[0m          initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "timesteps = 12\n",
    "years = np.arange(1993,2018).tolist() # showing 1992 for now, but this should be extended to 2017 for the full model run\n",
    "\n",
    "for year in years:\n",
    "\n",
    "    print('Reading in the data for year '+str(year))\n",
    "    variable_names, file_prefix_dict = make_file_dict(year)\n",
    "    init_grids = read_init_grids(year, variable_names, file_prefix_dict)\n",
    "\n",
    "    print('Creating the boundary conditions')\n",
    "    # loop through each boundary\n",
    "    for boundary in boundary_list:\n",
    "        \n",
    "        print('  - Working on the '+boundary+' boundary')\n",
    "\n",
    "        # loop through each variable and corresponding ECCO grid\n",
    "        for variable_name, init_grid in zip(variable_names, init_grids):\n",
    "\n",
    "            if variable_name == 'UVEL':\n",
    "                mask = maskW\n",
    "                ecco_mask_points = ecco_maskW_points\n",
    "            elif variable_name == 'VVEL':\n",
    "                mask = maskS\n",
    "                ecco_mask_points = ecco_maskS_points\n",
    "            else:\n",
    "                mask = maskC\n",
    "                ecco_mask_points = ecco_maskC_points\n",
    "        \n",
    "            if boundary == 'west':\n",
    "                boundary_XC = XC[:,:1]\n",
    "                boundary_YC = YC[:,:1]\n",
    "                boundary_mask = mask[:,:,:1]\n",
    "            elif boundary == 'east':\n",
    "                boundary_XC = XC[:,-1:]\n",
    "                boundary_YC = YC[:,-1:]\n",
    "                boundary_mask = mask[:,:,-1:]\n",
    "            elif boundary == 'north':\n",
    "                boundary_XC = XC[-1:,:]\n",
    "                boundary_YC = YC[-1:,:]\n",
    "                boundary_mask = mask[:,-1:,:]\n",
    "            elif boundary == 'south':\n",
    "                boundary_XC = XC[:1,:]\n",
    "                boundary_YC = YC[:1,:]\n",
    "                boundary_mask = mask[:,:1,:]\n",
    "            else:\n",
    "                raise ValueError('Boundary '+boundary+' not recognized')\n",
    "\n",
    "            output_grid = np.zeros((timesteps, np.size(delR), np.size(boundary_XC)))\n",
    "\n",
    "            # print a message to keep track of which variable we are working on\n",
    "            print('        - Interpolating the '+variable_name+' grid')\n",
    "            output_file = os.path.join(input_dir,'obcs',variable_name+'_'+boundary+'_'+str(year))\n",
    "            \n",
    "            # only run the routine if the file hasn't been made yet\n",
    "            if not os.path.exists(output_file):\n",
    "    \n",
    "                for t in range(timesteps):\n",
    "                    interpolated_grid = horizontal.downscale_3D_points(np.column_stack([ecco_XC_points, ecco_YC_points]),\n",
    "                                                                       init_grid[t,:,:], ecco_mask_points, \n",
    "                                                                       boundary_XC, boundary_YC, boundary_mask)\n",
    "                    for k in range(len(delR)):\n",
    "                        output_grid[t,k,:] = interpolated_grid[k,:,:].ravel()\n",
    "    \n",
    "                # output the interpolated grid\n",
    "                output_grid.ravel('C').astype('>f4').tofile(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe30ee2-7b83-498d-813c-9b9ea152ed6f",
   "metadata": {},
   "source": [
    "### Step 6: Plotting the Boundary Fields\n",
    "Now that the fields have been generated, plot them to ensure they look as expected. First, generate some metadata for each one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7ac447d-91d1-4a85-b4c3-12529580db54",
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_dict = {'THETA':[6, 18, 'turbo', 'm'],\n",
    "            'SALT':[32, 35, 'viridis', 'm'],\n",
    "            'UVEL':[-0.1, 0.1, 'seismic', 'm'],\n",
    "            'VVEL':[-0.1, 0.1, 'seismic', 'm']}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c60e704-57ae-4d35-aca8-2dbc8ad5f84a",
   "metadata": {},
   "source": [
    "Then, create all of the subplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd252056-d8e7-4cce-a50d-43e20b50c301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,12))\n",
    "plot_year = 1992\n",
    "Z = np.cumsum(delR)\n",
    "plot_counter = 0\n",
    "for i in range(len(variable_names)):\n",
    "    variable_name = variable_names[i]\n",
    "    \n",
    "    for boundary in boundary_list:\n",
    "        \n",
    "        boundary_grid = np.fromfile(os.path.join(input_dir,'obcs',variable_name+'_'+boundary+'_'+str(plot_year)),'>f4')\n",
    "    \n",
    "        if boundary in ['west','east']:\n",
    "            boundary_grid = boundary_grid.reshape((timesteps, np.shape(delR)[0],np.shape(XC)[0]))\n",
    "            boundary_grid = boundary_grid[0, :, :] # choose just the first timestep for plotting\n",
    "            if boundary=='west':\n",
    "                x = YC[:,1]\n",
    "            if boundary=='east':\n",
    "                x = YC[:,-1]\n",
    "        else:\n",
    "            boundary_grid = boundary_grid.reshape((timesteps, np.shape(delR)[0],np.shape(XC)[1]))\n",
    "            boundary_grid = boundary_grid[0, :, :] # choose just the first timestep for plotting\n",
    "            if boundary=='north':\n",
    "                x = XC[-1,:]\n",
    "            if boundary=='south':\n",
    "                x = XC[1,:]\n",
    "\n",
    "        plot_counter += 1\n",
    "        plt.subplot(len(variable_names),len(boundary_list),plot_counter)\n",
    "        C = plt.pcolormesh(x, Z, boundary_grid,\n",
    "                           vmin=meta_dict[variable_names[i]][0],\n",
    "                           vmax=meta_dict[variable_names[i]][1],\n",
    "                           cmap=meta_dict[variable_names[i]][2])\n",
    "        plt.colorbar(C,fraction=0.26)\n",
    "        plt.gca().invert_yaxis()\n",
    "    \n",
    "        if plot_counter%3==1:\n",
    "            plt.ylabel(variable_name)\n",
    "        if plot_counter<4:\n",
    "            plt.title(boundary)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09ea7f2-2254-47f8-989b-04990088b89a",
   "metadata": {},
   "source": [
    "Looks good! Now, with the initial conditions, external forcing conditions, and boundary conditions we are nearly ready to start testing the model!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2c2c18-d5f5-41c2-8c84-fd6120766912",
   "metadata": {},
   "source": [
    "### An additional field for 1991\n",
    "When the model is initiated in 1992, the regional model will interpolate between times to compute the boundary conditions. However, this will require one timestep from the end of 1991 - which we don't have. To initiate the model, we can copy over the boundary conditions from the start of 1992 to the end of 1991 for consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f5638894-50bd-4183-8c43-47e9f90f957b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying THETA 1992 conditions for the west boundary in 1991\n",
      "Copying THETA 1992 conditions for the south boundary in 1991\n",
      "Copying THETA 1992 conditions for the north boundary in 1991\n",
      "Copying THETA 1992 conditions for the east boundary in 1991\n",
      "Copying SALT 1992 conditions for the west boundary in 1991\n",
      "Copying SALT 1992 conditions for the south boundary in 1991\n",
      "Copying SALT 1992 conditions for the north boundary in 1991\n",
      "Copying SALT 1992 conditions for the east boundary in 1991\n",
      "Copying UVEL 1992 conditions for the west boundary in 1991\n",
      "Copying UVEL 1992 conditions for the south boundary in 1991\n",
      "Copying UVEL 1992 conditions for the north boundary in 1991\n",
      "Copying UVEL 1992 conditions for the east boundary in 1991\n",
      "Copying VVEL 1992 conditions for the west boundary in 1991\n",
      "Copying VVEL 1992 conditions for the south boundary in 1991\n",
      "Copying VVEL 1992 conditions for the north boundary in 1991\n",
      "Copying VVEL 1992 conditions for the east boundary in 1991\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(variable_names)):\n",
    "    variable_name = variable_names[i]\n",
    "    \n",
    "    for boundary in boundary_list:\n",
    "        print('Copying '+variable_name+' 1992 conditions for the '+boundary+' boundary in 1991')\n",
    "\n",
    "        boundary_grid = np.fromfile(os.path.join(input_dir,'obcs',variable_name+'_'+boundary+'_1992'),'>f4')\n",
    "    \n",
    "        if boundary in ['west','east']:\n",
    "            boundary_grid = boundary_grid.reshape((timesteps, np.shape(delR)[0],np.shape(XC)[0]))\n",
    "        else:\n",
    "            boundary_grid = boundary_grid.reshape((timesteps, np.shape(delR)[0],np.shape(XC)[1]))\n",
    "\n",
    "        boundary_grid_1991 = np.copy(boundary_grid)\n",
    "        for timestep in range(12):\n",
    "            boundary_grid_1991[timestep,:,:] = boundary_grid[0,:,:]\n",
    "\n",
    "        output_file = os.path.join(input_dir,'obcs',variable_name+'_'+boundary+'_'+str(1991))\n",
    "        boundary_grid_1991.ravel('C').astype('>f4').tofile(output_file)\n"
   ]
  }
 ],
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