notebooks/_poc_flux_bottom_velocity_inputs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Compute input datasets for sedflux generation from model output\n",
    "\n",
    "Generate a set of files for each of the following groups:\n",
    "\n",
    "- POC-FLUX: ['POC_FLUX_IN']\n",
    "- Velocity: ['UVEL', 'VVEL']\n",
    "\n",
    "Steps:\n",
    "1. Produce files on a set of source grids\n",
    "1. Optionally generate on a set of destination grids\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "papermill": {
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     "end_time": "2020-06-18T18:34:28.833450",
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   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import shutil\n",
    "from glob import glob\n",
    "\n",
    "from itertools import product\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import esmlab\n",
    "import esmlab_regrid\n",
    "\n",
    "import pop_tools\n",
    "\n",
    "import util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "papermill": {
     "duration": 0.024983,
     "end_time": "2020-06-18T18:34:28.881952",
     "exception": false,
     "start_time": "2020-06-18T18:34:28.856969",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "clobber = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.015041,
     "end_time": "2020-06-18T18:34:28.912448",
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     "start_time": "2020-06-18T18:34:28.897407",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Build and test horizontal regridding\n",
    "\n",
    "### Generate SCRIP grid files for each of the supported grids.\n",
    "\n",
    "Ideally, a regridding workflow would support this in situ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "papermill": {
     "duration": 8.134762,
     "end_time": "2020-06-18T18:34:37.062212",
     "exception": false,
     "start_time": "2020-06-18T18:34:28.927450",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/mlevy/miniconda3/envs/forcing-Fe-sedflux/lib/python3.7/site-packages/numba/np/ufunc/parallel.py:365: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 6103. The TBB threading layer is disabled.\u001b[0m\n",
      "  warnings.warn(problem)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'POP_tx0.1v3': <xarray.Dataset>\n",
       " Dimensions:      (nlat: 2400, nlon: 3600, z_t: 62, z_w: 62, z_w_bot: 62)\n",
       " Coordinates:\n",
       "   * z_t          (z_t) float64 500.0 1.5e+03 2.5e+03 ... 5.625e+05 5.875e+05\n",
       "   * z_w          (z_w) float64 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05 5.75e+05\n",
       "   * z_w_bot      (z_w_bot) float64 1e+03 2e+03 3e+03 ... 5.5e+05 5.75e+05 6e+05\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables: (12/13)\n",
       "     TLAT         (nlat, nlon) float64 -78.47 -78.47 -78.47 ... 62.34 62.34 62.34\n",
       "     TLONG        (nlat, nlon) float64 250.0 250.2 250.2 ... 250.0 250.0 250.0\n",
       "     ULAT         (nlat, nlon) float64 -78.45 -78.45 -78.45 ... 62.34 62.34 62.34\n",
       "     ULONG        (nlat, nlon) float64 -109.9 -109.8 -109.7 ... -110.0 -110.0\n",
       "     DXT          (nlat, nlon) float64 1.116e+05 1.122e+05 ... 2.042e+03 927.1\n",
       "     DYT          (nlat, nlon) float64 4.699e+05 4.699e+05 ... 1.664e+03 794.8\n",
       "     ...           ...\n",
       "     DYU          (nlat, nlon) float64 4.699e+05 4.699e+05 ... 2.351e+05\n",
       "     TAREA        (nlat, nlon) float64 5.244e+10 5.274e+10 ... 7.369e+05\n",
       "     UAREA        (nlat, nlon) float64 1.046e+11 1.046e+11 ... 1.495e+08\n",
       "     KMT          (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
       "     REGION_MASK  (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
       "     dz           (z_t) float64 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04 2.5e+04\n",
       " Attributes:\n",
       "     lateral_dims:       [2400, 3600]\n",
       "     vertical_dims:      62\n",
       "     vert_grid_file:     tx0.1v3_vert_grid\n",
       "     horiz_grid_fname:   inputdata/ocn/pop/tx0.1v3/grid/horiz_grid_200709.ieeer8\n",
       "     topography_fname:   inputdata/ocn/pop/tx0.1v3/grid/topography_20170718.ie...\n",
       "     region_mask_fname:  inputdata/ocn/pop/tx0.1v3/grid/region_mask_20170718.i...\n",
       "     type:               tripole\n",
       "     title:              POP_tx0.1v3 grid,\n",
       " 'POP_gx3v7': <xarray.Dataset>\n",
       " Dimensions:      (nlat: 116, nlon: 100, z_t: 60, z_w: 60, z_w_bot: 60)\n",
       " Coordinates:\n",
       "   * z_t          (z_t) float64 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "   * z_w          (z_w) float64 0.0 1e+03 2e+03 3e+03 ... 4.75e+05 5e+05 5.25e+05\n",
       "   * z_w_bot      (z_w_bot) float64 1e+03 2e+03 3e+03 ... 5e+05 5.25e+05 5.5e+05\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables: (12/13)\n",
       "     TLAT         (nlat, nlon) float64 -79.5 -79.5 -79.5 ... 68.36 68.26 68.21\n",
       "     TLONG        (nlat, nlon) float64 323.3 326.9 330.5 ... 317.8 319.3 320.8\n",
       "     ULAT         (nlat, nlon) float64 -78.73 -78.73 -78.73 ... 68.49 68.42 68.4\n",
       "     ULONG        (nlat, nlon) float64 325.1 328.7 332.3 ... 318.7 320.1 321.5\n",
       "     DXT          (nlat, nlon) float64 6.842e+06 6.828e+06 ... 6.129e+06\n",
       "     DYT          (nlat, nlon) float64 1.686e+07 1.686e+07 ... 4.524e+06\n",
       "     ...           ...\n",
       "     DYU          (nlat, nlon) float64 1.686e+07 1.686e+07 ... 1.069e+07\n",
       "     TAREA        (nlat, nlon) float64 1.154e+14 1.151e+14 ... 2.773e+13\n",
       "     UAREA        (nlat, nlon) float64 1.318e+14 1.318e+14 ... 6.256e+13 6.27e+13\n",
       "     KMT          (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
       "     REGION_MASK  (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
       "     dz           (z_t) float64 1e+03 1e+03 1e+03 ... 2.499e+04 2.5e+04 2.5e+04\n",
       " Attributes:\n",
       "     lateral_dims:       [116, 100]\n",
       "     vertical_dims:      60\n",
       "     vert_grid_file:     gx3v7_vert_grid\n",
       "     horiz_grid_fname:   inputdata/ocn/pop/gx3v7/grid/horiz_grid_20030806.ieeer8\n",
       "     topography_fname:   inputdata/ocn/pop/gx3v7/grid/topography_20100105.ieeei4\n",
       "     region_mask_fname:  inputdata/ocn/pop/gx3v7/grid/region_mask_20090831.ieeei4\n",
       "     type:               dipole\n",
       "     title:              POP_gx3v7 grid,\n",
       " 'POP_gx1v7': <xarray.Dataset>\n",
       " Dimensions:      (nlat: 384, nlon: 320, z_t: 60, z_w: 60, z_w_bot: 60, nreg: 13)\n",
       " Coordinates:\n",
       "   * z_t          (z_t) float64 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "   * z_w          (z_w) float64 0.0 1e+03 2e+03 3e+03 ... 4.75e+05 5e+05 5.25e+05\n",
       "   * z_w_bot      (z_w_bot) float64 1e+03 2e+03 3e+03 ... 5e+05 5.25e+05 5.5e+05\n",
       "   * nreg         (nreg) int64 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables: (12/15)\n",
       "     TLAT         (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "     TLONG        (nlat, nlon) float64 320.6 321.7 322.8 ... 318.9 319.4 319.8\n",
       "     ULAT         (nlat, nlon) float64 -78.95 -78.95 -78.95 ... 72.42 72.41 72.41\n",
       "     ULONG        (nlat, nlon) float64 321.1 322.3 323.4 ... 319.2 319.6 320.0\n",
       "     DXT          (nlat, nlon) float64 1.894e+06 1.893e+06 ... 1.473e+06\n",
       "     DYT          (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06 5.046e+06\n",
       "     ...           ...\n",
       "     UAREA        (nlat, nlon) float64 1.423e+13 1.423e+13 ... 7.639e+12\n",
       "     KMT          (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
       "     REGION_MASK  (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
       "     dz           (z_t) float64 1e+03 1e+03 1e+03 ... 2.499e+04 2.5e+04 2.5e+04\n",
       "     region_name  (nreg) <U21 'Black Sea' 'Baltic Sea' ... 'Hudson Bay'\n",
       "     region_val   (nreg) int64 -13 -12 -5 1 2 3 4 6 7 8 9 10 11\n",
       " Attributes:\n",
       "     lateral_dims:       [384, 320]\n",
       "     vertical_dims:      60\n",
       "     vert_grid_file:     gx1v7_vert_grid\n",
       "     horiz_grid_fname:   inputdata/ocn/pop/gx1v7/grid/horiz_grid_20010402.ieeer8\n",
       "     topography_fname:   inputdata/ocn/pop/gx1v7/grid/topography_20161215.ieeei4\n",
       "     region_mask_fname:  inputdata/ocn/pop/gx1v7/grid/region_mask_20151008.ieeei4\n",
       "     type:               dipole\n",
       "     title:              POP_gx1v7 grid,\n",
       " 'MOM_tx0.66v1': <xarray.Dataset>\n",
       " Dimensions:  (nlat: 458, nlon: 540, z_t: 103)\n",
       " Coordinates:\n",
       "   * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     TAREA    (nlat, nlon) float64 1.929e+12 1.929e+12 ... 4.083e+11 7.824e+10\n",
       "     TLAT     (nlat, nlon) float64 ...\n",
       "     TLONG    (nlat, nlon) float64 73.33 74.0 74.67 75.33 ... 72.97 72.98 73.0\n",
       "     KMT      (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0\n",
       " Attributes:\n",
       "     filename:   mom_oob.mom6.static.nc\n",
       "     title:      MOM6 diagnostic fields table for CESM case: mom_oob\n",
       "     grid_type:  regular\n",
       "     grid_tile:  N/A\n",
       "     type:       tripole,\n",
       " 'MOM_tx2_3v2': <xarray.Dataset>\n",
       " Dimensions:  (nlat: 480, nlon: 540, z_t: 103)\n",
       " Coordinates:\n",
       "   * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     TAREA    (nlat, nlon) float32 1.184e+12 1.184e+12 ... 3.986e+11 4.974e+10\n",
       "     TLAT     (nlat, nlon) float32 ...\n",
       "     TLONG    (nlat, nlon) float32 73.33 74.0 74.67 75.33 ... 72.97 72.98 73.0\n",
       "     KMT      (nlat, nlon) int32 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0\n",
       " Attributes:\n",
       "     NumFilesInSet:  1\n",
       "     title:          MOM6 diagnostic fields table for CESM case: g.e23_b15.GJR...\n",
       "     grid_type:      regular\n",
       "     grid_tile:      N/A\n",
       "     type:           tripole}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grids = {}\n",
    "for grid_name in util.POP_grids:\n",
    "    grids[grid_name] = pop_tools.get_grid(grid_name) \n",
    "for grid_name in [\"MOM_tx0.66v1\", \"MOM_tx2_3v2\"]:\n",
    "    grids[grid_name] = util.get_MOM_grid(grid_name) \n",
    "\n",
    "\n",
    "# src_grid = \"POP_tx0.1v3\"\n",
    "# dst_grid = \"MOM_tx0.66v1\"\n",
    "# grids = {\n",
    "#     src_grid: pop_tools.get_grid(src_grid),\n",
    "#     dst_grid: util.get_MOM_grid(dst_grid)\n",
    "# }\n",
    "\n",
    "dst_grid = \"MOM_tx2_3v2\"\n",
    "grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.015868,
     "end_time": "2020-06-18T18:34:37.097193",
     "exception": false,
     "start_time": "2020-06-18T18:34:37.081325",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Construct weight file names and ensure they are present\n",
    "\n",
    "`esmlab_regrid` should support generating weight files, but I am getting and ESMF error about PIO not being available. Probably this requires some `module loads` before launching...but that's not acceptable if that's the case, there should be no dependencies outside of the `conda` environment.\n",
    "\n",
    "As a workaround, I wrote [esmf_gen_weights_pop2pop.pbs](esmf_gen_weights_pop2pop.pbs) and run this on the batch nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "papermill": {
     "duration": 0.02463,
     "end_time": "2020-06-18T18:34:37.137342",
     "exception": false,
     "start_time": "2020-06-18T18:34:37.112712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all weight files are present\n"
     ]
    }
   ],
   "source": [
    "method = 'conservative'\n",
    "get_key = lambda src, dst: f'{src}_to_{dst}_{method}'    \n",
    "\n",
    "weight_files = {}\n",
    "for src, dst in product(grids.keys(), grids.keys()): \n",
    "    if src == dst: continue\n",
    "    if src.startswith(\"MOM\"): continue\n",
    "    if src == \"POP_gx3v7\" and dst.startswith(\"MOM\"): continue\n",
    "    key = get_key(src, dst)\n",
    "    value = util.file_name_weight(src, dst, method)\n",
    "    weight_files[key] = value\n",
    "    \n",
    "missing = {k: f for k, f in weight_files.items() if not os.path.exists(f)}\n",
    "if missing:\n",
    "    missing_some = True\n",
    "    print(f'missing weight files:\\n{list(missing.keys())}\\n\\nrun the following:\\nqsub < esmf_gen_weights_POP_to_POP.pbs\\n')\n",
    "else:\n",
    "    print('all weight files are present')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "papermill": {
     "duration": 0.02769,
     "end_time": "2020-06-18T18:34:37.180727",
     "exception": false,
     "start_time": "2020-06-18T18:34:37.153037",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#%%bash\n",
    "# qsub not available from Casper\n",
    "#qsub < esmf_gen_weights_POP_to_POP.pbs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020605,
     "end_time": "2020-06-18T18:34:37.217951",
     "exception": false,
     "start_time": "2020-06-18T18:34:37.197346",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Construct dictionary of horizontal regridding operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "papermill": {
     "duration": 14.48531,
     "end_time": "2020-06-18T18:34:51.718844",
     "exception": false,
     "start_time": "2020-06-18T18:34:37.233534",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "regrid_operator = {}\n",
    "for src_grid, dst_grid in product(grids.keys(), grids.keys()): \n",
    "    if src_grid == dst_grid: continue\n",
    "    if src_grid.startswith(\"MOM\"): continue\n",
    "    if src_grid == \"POP_gx3v7\" and dst_grid.startswith(\"MOM\"): continue\n",
    "    key = get_key(src_grid, dst_grid)\n",
    "    regrid_operator[key] = esmlab_regrid.regridder(\n",
    "        name_grid_src=src_grid, \n",
    "        name_grid_dst=dst_grid, \n",
    "        method=method, \n",
    "        overwrite_existing=False\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.015781,
     "end_time": "2020-06-18T18:34:51.753167",
     "exception": false,
     "start_time": "2020-06-18T18:34:51.737386",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\"Test\" regridding with `TAREA` field from the grid. Here I am just aiming to pass a sniff test: does the remapped field look right.\n",
    "\n",
    "What's a better way to test this? The differing land-mask will mean integrals don't match....\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "papermill": {
     "duration": 17.2229,
     "end_time": "2020-06-18T18:35:08.991642",
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     "start_time": "2020-06-18T18:34:51.768742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TR5t4RlNW/rrJ1we+xdDiHzK08Hu1n7xUGSSUoUaPsc9j6MlzzYfZ4ioJJl0tJoz6UGCYXOJSAVdQOjb9fOBjqroZZkbQS+vOdA8xsPbh5j2wSitQ+Dda6bn7qyVyXPANGZp3vqn8LEMvFSsJAHPemgW+NldJWGn/wjyuYWOaiTD04oVm3ROG5p3P0DPnFR4swuxHzjLfroBKFStHqQjKuqJvoAgDH/gWU1Y7KPzutJNek+eetSg0koFx3zEtjMhHYujpaQxslB/JM/sRE9htyhYJI6eSXnaRwlYxRkimbHFKfrtHchw450OGJ5XHi2sCNy69ismj92XyCvshfX0ADP3jRwyM+TZDi525YiKVcVGlHqaL/K60DD0ikdfz1xn44PHm2VllL4lWfnAUJVfHCCRVvV0KZzCN7r/L+XkPhXMXjHjCys+VIwjfv6DSm7zlqZW9k8GzdN5LFcm3koNkIsx+4hwABtavNjJ0k/EiNyoSKi9D82cysN7RRoY8j6GnzYSaUzY9kdmP5qdzmP3QGabM7PHuuYpIeP+nbH6yzQ/m25akyAeKRRsaSVF6TZ5Ti0IMU1ban9mvX1a8wxOG5s8ECLXoISvkLrMfOoM5D55ufiSZy0vhPBVXKw8q04ENjmNo3vmFrR/3+v/+SeXXagJz3vk5c96aVVCGBUpCQKVl04R0gUVh6PkZQKG1KGD2fy43rbcWoMAy/MSlwRxAYbjqFMvQvPNNpRdYDyQvYwVKQrl3LcGaFUdgUQjzMH9msZXLYfIK+5W6hcYQfGtWP5iBD3yr2Aoy8SijOMw7P9w2ZeMYqyow58HTmXP/6UXnTrpmkVUwxko4sMFxDD073XR/OIrD7Nc6w1DWa/KcKgpxiDBl1W8YIVkjnEYgL7zByxoIuEeoRbvWhDn3n86cuacVveju4l6z4K+lyDrh7B96djqdTFz3TaBshXn3ImUgYrpcHjoj8e1MLMOia1H0sS/ALcsXL2RgrcPCftgpqx1UwR02DgWWqZ+4AKuJyFxnqSmDIrIL5sPSYc3XzmLoiXMKfROgUElwtkffR43KcjWKaxmZnrLy1xNb3g3Hzb+1qA6sfTgDE45g6PkZpqvE+mkBzH787AJrQuz5qikzL+ZYx+drYP1jQ0U/2D/wgW816Obro9fkOVUUymFfwMDkNjDxqKIksx+1joxETG0R4iq1UkJSsN091pO8mTLQqK0wD/3jRzXcZHOIs8oEylaR2T/yJhaZK50PTJSKP87ux8cqKG4+3D7hdrRM/BIL8JqqbuMsVWdQRDYDLgP2avCser1J0jsUqeziKHofoVhxLaNAhIqrQ2BWL+Vw3QiC6wz98xKGXrnYrL90EUMvXhhaVV2SrAlx1FRmUZwGxtCCQSO70e7hNtNL8pz6KMQReOI7L+PA2odDxguFJDriIfBPAPLdDg4aeflD/Px+UY31V3CPUz+fbmDSMQw9P8N4awf5XOMQ4xX8z0viz9EhxJos41pvwXZqLMOgzKC4r9imG5h4VGHLpA2oKkub2FK0s/VdA+yrqs807UI9RCDTk7c+lclbnxrv0JjU1PIj7xnEy7Xb755TI9NWNoZeuoiBcd8JZVpzOWb/+yfMefvKht1jKdwuTHdEwcDEowpHO9j7mLLZSQXfwbDMgDlzT2POfcUNqD22OyP/w8/LeFjCqqaM1LXK2v3BN3De+XbEipcfUdJmek2eU4tCDEFLeOgfPzKt9IXfM85O4hSXF1OhRZj8odOZ/KHTw5f/xntP4cZ7I10JXv4crkY95/7Tk03nkcos1KgD60eHKwlRZj9+dt4R6SHnwxFpvQEF5RWSUIZFlb67rcB6IwxMOCL0UwhaUK1EKdsCKUlcbHoROVhEDrZJTgHeB1xsY843f7xhr2Ctdep5+aGRwRK1EETe09Dq4JjOo1aFaEu6wGrpnLPdvkdgh4c7ox2GnjzXNJYeNTI8ZYtTmLylUazm3H+6+ZbNPS35hJGyccst7LZNKkNrSTUOlRnz7YvzhWoDvSbPPTvXwzbbbKONHnsdatLW4hCMxZ+y+cnMfvjMvLOTfaHjNOiAPba3Topu+ds3qCCGgqoZP+z7YTwAyQVxAXzwfYaenW406pwPw8NmWwd1QVTClM1OMh9jEeY8eDqTtzmtUEmIsTAU4Iy/BopjKARjsINyDMo05ztlaWJSJA1LqwURuV9VtymXbpPN+vX3f04eWr3h2osrOk8v0gxZrpY9tnV8Zkp0gwGFIxvcdy/4XYFMk8sVrHdCBTgw4QgQL299s0zZ9MTQ7B/KajkFIcLuO5xVUG433m1GOuyx7Rn576ETByWMqxDIsP0ONpuRKs9p10MJgiBBQwu/lw+05JrMnaFAgZJQ4N1bCQVmTOXGe4zFYfKHTi9UIhzzZYFJU6SwBeJ5HfFRqZYCk2WSklCqWyBsydnuBd8ZiiZCGMktPEe+/Ix5U6xNs319nLmaQlCmNJNQQchElIMSj0pF8q8bgKeoHzGpB+8f5GU62CVifY8UtHNayWGApQizHz2bKVucUruSsONZIIXlFuJ2HWaMqEoOq5goiheWWxCAKc6Hoh30kjynXQ8JDKxxSNjtMDB+qhGSwDQYjHaIM2NXyI13n8yNd58c64i3x3ZnlDZXBlaL0Ikxv68V0clahfvhiTqQlfWadsss+jvGfBkQjqdvMcZL2ktcUtpERsIuB/Vs57gjb0hxRFGzEFZ+6nlG0XDfwUyxOR1wflt5bqB1q24ShmNDsazuse0ZseliKSgHU3a773hWfl+krNQ+k1COPaPADD07HbKd0fbtNXnuvhy3iKFXLg4r3SBiW+hkZLscZj9yVqEmXSWBJh1+UKwQ3niP8WUopSwUeU9HxhN3NRETJrh9vSQuZfuDoyNJgr+BktXGloiPsJRM4pLSHlzlQDNBJUV+PeNUXsE+u9+t+DTunYypAAuU/g5pGYPtdvW84iiMAR55P46MubfddzgrPq3D//vYOYllt/tHzkY9W0YZR0GwipdREDJmPesxsNEJBRbfdtJr8pwqCpVSoSZd9TldU6YQCtce258Z78wYbYFErQo9gFumxQpCXOutsAwLFIYka0wJq0K78FUSl5T2cNPfTuKmv51kK6n8cvNtJxT8NhUa4Xq+5UtoYUhUFoLWcWB96JD30WXo+RmhkhA7FNJ19HRltgw333aCKbeEsguUJw3m3XAVhkBR8zxmP3q2We8gekmeO8NO0w3ECO6ULU4xJkWnlb/HdmeEfgal+H87nR1W9uK753aG8EHYv1m0LoJmMP11rlWhF4j72ASWhYRbjC1DYsqNwuFVQT9wuz/MirBUu6+lMVK4+bYT2G3Xcws3ijMEEvLvkH2lCP2IzDaxvouoIKJojvz7iWcc8wK/hKSWe5uZsvGJkDGjG9wRSqFlJVTqhZvurDC2QjSce+CnEPyNKb+b7jmp6DQlgz21mF6T51RRqIAgnnmU2Q+dETreRU3lZXHSaYZQOsQ3JjckpqLzPMA366FJHiRwxOvirodwGGlUSYgqCEmWnUgZ2q3xyoJbnr6fL8M2YoZT9Yii14Psutu00KERYNfdp6HZYExuJHE48gZHaSBf4eXsexk459mJSRSPOe5ESh1AdC4HbAu+qPUujjWgmu8gFJ/LLT8C5YCw/G6+PV4BmbzNaZDT2Dg2rabX5DlVFMowsN7RicNuokqCMSFWdt6o9Slo76rnVnYxFZ3bCrbbgxZIx00qUwXBUNI9tj+zQEmIKgilrHYVl2FU4bItunai2lstkF5Ds/kX769zjmXnKU6Lv1iY7XacoZESvomaybeOxYc595W3QLaSILgcmUz47Zuy8YmQ9fJKQqThVNBl4Cj4laBRJd0tP7BlaH+W+ABo1uPGuZ1Rlr0mz6miUAZXSQjGCwcTNc2Ze5rx7s0Ut4DLccutJ7DbLnlTpmuqFDWVXWxFZ02UbreDZjCzWHZYv2Yt3Hj3ydaJyW4Q8+GJllcciWUo+Y9yVFmY/WDntOD8HhpO1Wv8dU6hEn7r7GP52J7GcU7Jt37NhqApnO92CCs7p3V8822Vhz1uFXFKApD3pQicFh0rwO4fOTv04Sjwx6iAnQfOj7eEqv3uKaYRpqaL5y83HVec1lJJl28r6SV57h3bSCuwXr0FRJSESke+7LrbtLxHdbhIWDGq42WN4+STt1zkHXnyVo3eeDED02XgIBYoQLf89XjzEYo6kRWUY0wZuuezHzsVqT7mRRMxfZrZxCWl87jthmOMKT4jaNYuwYiI8N0E3+73s56xTFhnyE5jYK3DQiUBzyuc1TIc4mmUBLdSvunOE9GMl3c6zAh/uSW5QncJy80tP6cM/Uy+HEspCZ1Gr8lz9+W4xQxsdEJBhRxVrQIlIe/MU+GJw+5Ne4APCM4cBHlbQtAyvvFu48Czx3ZnMOfe0wpON2Wzkxh6vHSLu1sI+iB32+Xc0Ay5626mj/QvfzmeXXefVnSMIhWVISjqebHOUO3EjLvuHVPlSOG2Px4NwE572da3VWpDU7mPWVGMOUGlIyu8gbUOg2zGDjfMQMYoBFM2P9ko11mvZBAl9SRUwNWDnaecx62zy3eFFjWsgsZO4B/qlOPHPn4+t/05P4nbLpPPK7L0dAq9Js+pouDgmt2CMcPRcc5FrVDPabFaE3kl/OWm49hlD6efM2MquKDCExu5rcCRxxJnYnMjG/YKYd9loFSJmDKLDr0KzL6RMjRKQ+ebe8EoOrnUwNe1+BljJr/jD/koqTvtNT3fDeZbv6JOjZgfKAk2eFHQONKstVh6nvEf8oSb/pZXsnfdbRp/ueU4/nLLccZvw34Hb7vhmBIXy+PHDGl0u3GCKQaMw3b5YzuFXpPn3rmTOolTEoBCL1/PK4g4FvalBx6/VZj+d55yXt7M5pE3qQcOQdEujTKnTprauquJmHFNGdlFnCWhDPPj1QHpTHNvgGmBZBOXlM7mzmuOKlASAG6/7mhuv/5o/EyhiT3wbego4pQEG9xoztzT0KyHZozisNvO54SHBb4KOw+cH3YRVKIk7Pi5Gez4uRlFwatMV01+Cbb7GcHPFn4ENdvJikJvyXP35bgJRJWEIJ55OFFRIDg2QEpAgYdvFQ48UOjpK84gBlTt70JHqL/ccnzcacw0rT5IB2vXtfKXm44LvcsDM+bOA+fHKk3RMjR/C8uwk1GEXBcGYkkpzx3Xmu6Jj356BqrKHX+srLXdUpzuBjKZAovC7juexU13GSvCbrucW/Dt0oxVfLLBt7D0ZXb4/AX2es45nOZqYTyUvChLjCUmqjh0Er0mz02zKIjIWiLyVxF5UkQeF5HD7PZVReQmEZln/77XOeZ4EXlWRJ4WkcnO9q1F5FG773siVdTIZRhY+3BjCXA1as8LlYTZD51htOr7ToWMhLOagQnC4jrwaEbYZXL5QCkf++R0G6iJ4lZyQZS3vENeEjfecwp4VDUJSzdx6+xjC/o6bx06xpSbdSILytEtw1tnH+s4L5Yvw05AtbtbICKSEZEHReRP9nfVct7r3PGHo4qHAnYKgYy4DSM3xLTllr8eXyBLtw4dY0dE5L9XpdBMzOJYVv1sZF+2ME3BuSK/P/qp5s8eWSndLs9RmpnjYeBIVX1ARFYE7heRm4CvAbeo6jQROQ44DjhWRDYCvghsDIwFbhaR9VU1B/wIOAi4B7gBmAIMNSSXEUtCEBo06POf/KHTQ8FwlYQAt4ug0vHDt/3xaHb6v+kFWjU4AUZ8p3msVhhLcOM9p7DH9mfG5q8XKar0nd+iVhHzsH4JUlEZth/p9uFUhwFPAivZ38dRvZz3JNt/6QLu/tWR7PD5C5CM8JHPzsBEGtTQ2tBuNIiRkPXQYP6EcNRQpJWfET728fOtA3dQkTvfwBi2+/IF5Oe3yG//+8+PCNc//FUzt0VoPHAdGjNwzy/yaYN87PD5C7jrN0ey494X8LcOKUtD18tzAU2zKKjqYlV9wK6/ifmIjAP2AmbZZLOAT9n1vYCrVXWJqs4HngW2FZExwEqqercaz5YrnWPqJ0ZJwPOYvPWpoZKgnmeiJToEXvgFrVZPylZIH/3UdD76qekFWnTR4rSW1aOiPs2RoiQA3H696fstV4aara4M24lC1w6nEpE1gY8Dlzmbq5LzFmW15Xx435loRtjuyxegWdNi9q31sVOUBDDhjwuUhIwX+iVEHbRDfwvrO1DgR9AXXzma/XmLgW/LwsX1TShKGzOA4K5fH4nfB9vvcwF/++2RjSqKhtDN8hxHS3IsIuOBLYG/Ax9Q1cVglAkReb9NNg5jMQhYaLcts+vR7Y0hRkkoML3ZSVqirdjgd36sPwVDd5K449qjTYsiPBHF66r5IC7Wa9qlk4cFtRL3Q/uRz8zgzmvyzmShGdIpw+jwqk5CkW4eTnUhcAyworOtWjnvSf7+8yPY9mszEd+MYAoCgd3zy9IV284D5yM5NYuvSM5PDF3cKObcfzq773CWjWdgnLP9bHFb0s/kG0hB7BjN5C2qH953ZoGl4EP7z4SsY21wPqVbf2Mw/yPicyD2Owgw97JCa0KYl6yR7eg1202Xy3MRTR/1ICIrAL8HDlfV/5ZKGrNNS2yPu9ZBIjJXROb+85//rCyD2Ywxu1ltWt2/Vkm46c4TUU9CKwIQtlLVIz+LWQXsuPcFkWBBcS3iwv3BWO38tXvHpNUoXCUBjBJxx7VHF5RxpyoJYP0t1UtcOhUR+QTwqqreX+khMduK5LkmWW4DWx5iKrqtvjVYtG/rbwyyzYEzw9ZxsGjGVKZJ7LTXdPw+4+Xv9wl+X/Fog2ZhLAXGkuBnvbCbweWOa4/Gzwp/+91R+H2FVpLgXgO2OXCmuY9gfzY/gsHPSt4PIbpkCNP4GWGrbxaXL+QtDp2kJED3ynMSTc2xiPRhlIRfquo1dvM/bHcC9u+rdvtCYC3n8DWBRXb7mjHbi1DVS1V1G1XdZvXVV68ojwVDgdzpSz1jXgtmQIs68QTdA4FwlFMUdvj8BezwhQviTeTRKGRlnIM6eVhQp3HHH47ijmuP5vbrOsfMG0fQAklaWomI/LfM8qaIPGOT7wj8n4gsAK4GdhWRX1C9nBdQiyw3k82/W1xRbf7dQTQDW3xnkAd+NLVg35aHDNquBoks+Ypt2/2KlYUd977AVqCC9jlmfdsNUC6Meb3cfPuJJoqkVVQ0K7EK9l2/MRaR6H359p63Pmgw3B9U/r6jCPhVLMExAa5SlusrHjbZCXSxPMfStK4HOzLhp8CTqupKxPXAfsA0+/c6Z/tVIjIT4+Q0CbhXVXP2RrbDdF18Ffh+o/I5+9Gzmbz1qbFdDbFOPHue78yJTj7GQYl3dfsvXVBZSdsoZOIDKsUx5INk3aeQtpwdvnABd/3afMx23PsCgmGnaLH1oVPIdY7z03OqumWpBCLyIICqHg8cb7ftDBylql8RkelUIedNuIeGsdkRg5C1f12yRl4fmTmVzaYOhu9XsC/4HQT9Mn8F8Y21QbLwoa/PRHy494oj2O4rM5E+kJxJIzk78tonnEU+GD64287nVBzcrVqCyJE7/d/0sg0gP+M0aAJnRjv6aMtvG2UpeK0LvpPOaZMcIEPnbvtfoKyJ8y3VLPlpqTuMbpTnJJrpo7AjsC/wqIg8ZLedgPlw/EZEDgBeBPYGUNXHReQ3wBOYERPfdjyhvwVcAYzGjHZozIgHy5z7Tw+jjoUzoHnFisKtQ8ew0/9N5/brjzZjosPhjfEBkbb7cuGY4SBNgafvvjOLJpRRL/+RiY7//8hnZ3Bnh7eOO4G7fn0k2+9zAXdfdaRRrIKoeGW+KrtMPg/x1cSm8E3/8M23ncDuO+THkjcDVWFZ1LurCkTkciDoBtgkZr8AFwF7Av8DvhY4G8fw2QouWS5NLXLecWxy9CAEDUBXxiOvkWacV0tt+A5HSQgUBjPawW73Tf+6+OZAP2sUAaMQKCJWYfBtLDdf0Jyad7Svua2Fj35qOpqVskq1b+dnCJSDcKhyoBRIoYIQFluMwhDlkRnGSrPpkYPhgQI8PNNs3/xwo8B1YrTLXpPnpikKqnonya/BbgnHnA2cHbN9LlBUWI2kYChQMLFJJCb7TnsZDfsjn5lhJ2eSAuEoOmdEQYi/bv49NwqD5C0LCnf/qrDvzc8IO35uBn/7nRHgj35qekd5T3cKH953JmSE7b5iHKnu+eWRdpha8ocviG8RDh33gJxtvTVRSYBAJ6yrBXIF8APMqKA4BjCt90nAhzFDjj8cmxfV58tdLC6Nqt4K3GrX/0WVct5pbHz8INqXvP+Jc/LdDWGdECgHznqRVcEvVBoevPgItj7IWBgCxSBUGHLYWWLtdg98Mfub6dTsZ4sbSnHc/5OpxnIQKAeuwgDxykKckpCghG183GBYS4XlCmx6lFHgHr2gsMunU+gFeXZJjdiWm/52UoETT1wccT/wHQhDijp9bzHpg3CkoVewXT709XxPjLs9H7Y0f2zROSP+EJ0eSKhduGUaOI9VZEbNOv3CLewbVoRlfiZxKXu86u3Av0sk2Qu4Ug33AKsEPgRRbLC0q0XkDhE5wfoaBfuure7OupfHz51a4GDn9+WX6Ai3cHuQpoK+eM3CgxdPZYtDB+3xpr891yfkAifBPsHvF3L9EvbH+/1emLZZBI6KlfDgD6fm78t13OyLLNl8GWncvshxAI9Pm2rSZ/PpAB6dMbVjlQToPXlOFQUH412cryiiaEa48/dH5R1snHHE0We/zQEz85V+xigZ7rL1NwbZ+huDBdsKKzezRLn7V0fiZ83YYYA7f9+Z/e3txlXQfKdcg6AuUXb83Iy8Y6kzTtz9DTTN87wC56fVglEAdjmoykuMA15yfpcalng5xjLwHWAMcJuIvM/uW6fK63Y1T54xlSfPmBoqB67SELDBGYPh/qLKMVopZgsrxk2PGAx/5xwlJFAYXAdBvw9y/c5oiP7mfr7LDeF08Z1ycR0XXcWqSGFIKC+/D548cyobnm58Ep44a2p4nGvF6WR6TZ5TRcHhrzceG84dHzeyIAjqcfevjrSVe2FFtM0BrqXAaZU6SkDi4uGkldB6EUewPyWerb45WFT2wTP6+5VmXHuUfHryz9a+B8F5dtnjPDTTPJHx8RIX4LVgFIBdLq3y9BUPMwZWV9VLVPUhVf0OcDFwu4isW+KYnibOIgAw6dzBWE/+UpVgUms6OMZVGIx1QfIWhz4h109oYSiIydJGku41aoVJGtEQXcDpziGftpvoJXlOFYUIak165YbTRVv/QQW/1TcH2ergwfKKQXTJFm9LMi0G10xqHY8Etvz2YMHfcPshg0Vl6ipfwbj2D+0/M1QYPrzvTPw+yVuJnPHbocLQ54UtuYLpwRuEKizzvcSlASykgmGJlj4RWS6fN/0FJkTzHEyLZMSw7gzzjvh9apZ+u/RpuD3X7+yLKABaRnkosjY45vecsy9QGHKOwhB0SYQTLbWJD548WF456INcv90e/tV8uUaW9abPxO/P12F+Hzx9cndYE6D35DlVFCIElUPZdFmnArJjhfNdEsSGII0GXino13PTO+sBWx2crxDv+9kR+LZ1PBLZ/LBB/Kz5++APrQf0dwfZ/LuDRWVepHwVBH4x58s/AylQGAqVBqMw3Dr72Kb0DSvS7AAt1wNfFcN2wH+CyIkxXEbEMUpVb8aMXHisEZnpFgJnRu0D7Ve0zy79ysTvXZDvbw8qP1eZ6LdKRIwCEVux9kfSxKbLKww52xWRG9Ue6+KGpw6y4emDPHnm1MJuh4L85it/7csrWWGZ9Ccs9viJF85k4uBMnj22e5QE6D157jJjTvOpdIy9n5H8aAfHqzc6VliTPHujaPFfVRPMBUCiSkMHBhlpBpsfPlhQho8MTuXhi6ay2RGDPGKHSQXj3JPKEAq9z/HtPs/4ikiG8FmaMevOELWc5D23PTPKRLLCxz45vShiZj0osKyOD4iI/ArYGdP3uRA4FegDUNVLMJOp7YmZW+F/wP6JeVGNDYOnqg8Cu9ecyS7EzyrjL55hSzKOYLC/eUmD98yNgaK+EMbxCEY8hEMj89vEBzLOyIecs55xtmVsPAUfvJwivrGQ3fez1jQcNjzNjHIgm2/lP33yVNY7fxBE899E+xfRou9g0UgwKSzHgAXfrtxPIo4Nzhjk6VNar2T0mjynikKEYOx9OTQbUQ48Cob9hEOD3L8UC4gbQ+HRYNywDexSMBbbvnNbfMdUihKJr9CLbHpE8Rj2TY+yioPdHoxzd8sxGLceHqb5RdUMMQuGpuGZD3mgGITKQozCIL6jTPiN7qqXuloaqvqlMvsV+HZVORIZp6ov15ypLmf8pdOh36yXGuIMIFYjNe+eHRZtFdLi4ZFWcbD7JZdXGiRn09mhkFGlAQ+8QLnwjeUrSNMqnjptKpPOHWTe8UY5UE957qgjTFeBgHp54VNPQ0U7/z10hTOKFqyOv3Q6Cw6qTSGfNM0MOV3/rMGwfJ86tVVKQ2/Jc9r1EKFSs/KDP5xa6N0bhmS2Ju9ot0OQtsgPIb9sfKxRENzuC3fY5GZHDPLQ96eGx/U6pcrwselG4B+bPrWwHDP5Z6LZ4jIMntlDP8gP6cr3I7vdDe75gpj7hT4MDb1XNS2QpKXViMimwO9afuEOYfzPzod+H/p86PeRPh/pT16IWbTfR0c56/3FZnh1fB5y/Zrvx3fM8rno35j9uf7Wls+846cycXBmeA8Tvn+BvTc/7JoJ7lnt/ReUz6jI+qjINmcZ/7PkmV/H/yjZmdPP2rJ2vrEbnBnbuG44vSbPqaIQoZp+/4JRD1GnpTJevaW8fR87Pz9+OzjPozOn4mdMlLKHL5rKw9/rrj67atn4+MGSZRiw0UmDsfuTnoWfMd0Xmx4xWLCvyGM9IYa973ig7/CFxjmRKcKwn0lcWomI7IKZt2Hfll64Qxj/82lIXw7J+iz42rF4fbmiJdMf2WZ/G4UiZ463CoZbAeoou/SbCjVWaYjxa8jF9OMHfgrB/maz7oyZrDvTOHdO+P4FaNZRfPrVKkSaVwpG+WEZmCVny8YvKre43+5xRc/oCuNQrP0+61w2PTa/GpZhkM/WNbB6TZ5TRaEOopVRXZOfZOCJs6fywVMGw3O7M6uBaT13cpCRRuLef9LQtCBdUmWvUeuC/bvxsYN5a0MQFMu9VsY9Jh9cq0BhsEpDEM+iIfeMJC4t5npgb1V9ttUXbjcTrzoHL+vjZRUva8zgz+9zApk+n0zWLNm+HJlsjmxfzq7bfX0+mb6cc7yPl/WRjA8ZhYyPZBXJ+mhW0T4fstY5Mqv2vXNbwFrwLkctkNH3utmY6xnrgWZMnrGLZs29SNbcI/a+JeOH5RCUSVBGbpkWLH3uMX74HAAm/PJcJvziXFOewAsHHINklHUuL7Y6GAuv8tyRR/DcUUeYPLewju4leW6RftWbBBOeFDkyus46Fb4TgVNQEMglDDiipk9wpPHkmeaeNzhjMLEMNzjT8WGI4DoxurH2o2F0g/5gt184XPfdfZL3TQj22+2NQKHlLY0SXAWcIiL7qOoI8IYxrPebs8j0gXiKiCLAB/9wOgB9fWrCepdAFTTwT7CTuqmdY8T3xWzzJZzjwfgqCPhi3kdf0Jygvpr3KmPfU+ujQPAOOnPBSM5xhGwyfp+Cp8avJ2PWxfoghGXmqfEBEvPbC/bb8hTrn5D/G1+OAE986rSifX2jhvF94dnPn8TEX53D8186gQX7H8P4K6cVpZ1/aKGv2XNHtm6UWK/Jc2pRqJCNjxtk4+Pz/VsfPKXQNB4dGlkwjj9oBSQFI8mawC1AgcbrZ0amkuBSVIbZvDKVFAK2MIZCobXAHdcdfXbu8aFPRNH4dicyXtaYfhtzo4JfYmklqvpNzLCpX7T0wi1k4q9MhM1Jvz0LgA2vOYO+vhx9fcOMCpb+ZYzqH2b0qGWMHrWM5fqX8Z7+pYlLkG70qGUsZ9MvZ8/Rb5e+UWbJ9OfIjDKmduk33RRBP33QPWG6IOzfUYVLNCZBblTzymr8xTMY/+MZoa8Fo3IFXQpev+mKyY7Kke3PkXXud1T/cFgOyznlEyzRMlyuP78vyqbXn2qfkemK6Bs1zPq/OxOATH++/otTGlpOj8lzalGokMenmW6BDU8dBA+eOmMqG5w5GDtLWuEQSesNXcG7sd70mQXDsLotElmjWe+8QZ49YaopF4rL0HdMkgH5CXmCoSeFQyPVaX2F646FQIssCXaUhPvbsTI0Kk6hAsMdNH+4qp4lIl9tdz6aRbY/Z5UD8zKM6l+GCGQ8Hw8l4yn3TD6XHW86Nmz9eqJ4JR64T74SUFshBH9zdj3nC77vkfPz+/ycZywOvg0dnzMfEc0ZawO+2vVgqKTZJtaigJ26ulnoKN/IQCZvNfAying+nrUqeNZ6YP76ZKyFISNmmzh/g7IEisozMMvfvluh38F2c45ndL+gCjn12OLPJzOqT8j5Hh/8w+n09Qvr/+5MnvncyQVKQ7voNXke4VVR5Wx4Wn5udXfs8KRprrKg5bsfSgTeVPtf4DD03HEjM6BSgAbR72y5hwhMvKhQqYqWYTBcLRwSGZiF1THVqnNeRxFw10PFwSoLodnXHfveiHuFlrc0yqGqSTPXdT3PfPZktvjzyWGF9fAnzgyVgqz4YYW2Qv+SgkotWI8jeH6BwuAuOfXMX98zi6M4DOcy+H6M0pCTsHsirzQQrosPkoUgLkgzGD/rPKTPdilkFC+TVw4yjkKQzfhkxA+VrYzn44mSEfO3YKFQWYgrx4/f/l3+vNP3wm3L9y8pKseg/HLqheUI0Nc/HCoN6159Ds998QTG/3waC/Y9LvZ6zaDX5DlVFCrkqdOmsv7ZJtDIpGmDzDsu8CnQIv+EuAAjAaXiKLiV3fzv1BZoZMPTB1s4Vri5+H3GcSp8SxPKMakMw30q1k9BI9P8SqFFwTfdDmEQnBJWBqzCUKLeqArjJd05LZAAEdkMGI/zrVDVa9qWoQax7ewTGN1nKrSAQCnIis8fP/p9AFbqW2IrOJOuXEvYKAbmOQ7bSs1X82x9+4yHfa+gwhvO5QoquyKlIWcnJ/M9oyj4jtJgLQ6oMOH7F9T83UjC688Zi4FnlQRrOchmcmQ8owhk7O9AwerP5EKFIOvlFYVsWIZ+gZIQlKXr5Oer8IW7DzblgHDLLhfH5m+nW44m55uyHM7l2OqGk3jqM2ex8XWn8cE/nE6235wz0188cqKZ9Jo8p4pCFfh98OwxU1n3AhNWFAH6tDDQklUUCoItFSgNhR8YdWs8u2vBwUfVnseMdfKzFVq3+jiMv3hGsYKQUI5xZRgGWLLrQfcDjuIgvjrOjPnANUFQpaKuiYiy4HZj1I12lqkSQEQuBzYDHse0W8GUYNcrCvdOOYfdb51K1vP5+O3fpT+TY87Hfhju3/fvB5L1cqzcr9aSkK/cMgnN91xEWfAxXQ6+egyrx7CfMRWaGmUh+Lssl2FYvVilIZezc4xYJ0f1wc/Z6G6BomC7wVRh/I9nsOCbtX8/omT7c8Y6EFEOshmjZPV5ObKebxbJ//VEyXo5u+6bLogqy3HYz3DpNrMS87bXnYeyYl++HHMZj2V+hu3mHM9y/aY8fV/Y6NrT6OtrsWz1mDynikIFrDd9Js8efQTPHjOViRfOzCsHrmLghCsNKrRwHxR1OYgE3r2OsNjVdS6bzgsH1haNzO/LV2iIURq6aTKVkKwWli9UVYaoFCgHGvmbtx4UKw2hWTdQGmxXQ2CJiCoLjaATTZXAdqq6Ubsz0SxW6FtqK7cc/Z5pcR40dz+yXo5V+02ruE9yTsWWN6fHkbMVg69CDmtNwE4EpIGSkGGZnwkVh6CSW+pnYpWGZb6xMAznPHzbcvZzPup7qFUU1LcfIx9QYZ3Lz+eFrx9TV9lseM0ZeJ7Pcv1GSejzcqFykBWfvowps0IlwSgGfV6OrOTLr8+zcyaKhmUIxJajW4Znb1a6/rruIz8AYO+7vsWweizNZfjzTt9jt78ewTI/Qy4oz4zHA3ueVVd5VEuvyXOqKJRh3Zkz0T7MBDACZMmHJbXDgNT+NcOAnHUgH0PYIjGrGtlSovYZf+l08CXR6qB9Gg61Ciq79c8e5JkTu0dZMFHxGlCGjtKgCkF8fVTDlljhMElHaXDKsMiJsSk+Ch1pqrxbRDZS1SfanZFmsFLfu2GlNsob5qiHv8Cq/Tn6JGf61lH6vGEytiUc/AWjNEQxyoFn18UqB6abIVAUlmmGZWqC7ixTj2HNsCSXNRWdn2VpLq88LMll6fdzpsLzvVBZGPY8fPVRX4qGXQYWMzDe/wu+Wlu//Kj+ZWQzPn3W3yDj+YzKDIdKQX8mR783HCoIo7xh+ux6n+RCBSssS/HNeuif4CeWIcBhG95ccV5/u8OPAPjSPQcBcMsuM2u650bSa/KcKgplCH0QPMdq4GmoLIgdQxxWZnbMsPsXqGgMNvYUcYy/8jzwYcFBx7LOT+NDmk74/gXQZyq45w4zXSRBC7mryJq+zvlfOZ6JV53D8/ucEA5nK1WO0TIMnBc1sC44rS5VtYqDVRr8GKXBV0dBcKwM1qkxVBoahHZeC2QW5uPyCrAE7PQjqpu1N1uNYZW+/9kKLkefU8GZZRhPlH4ZNqMgxLSKM451IUoO46QIGEuCZkw3gl0PFId3/b68wqAZlnhZlqnHEj/L0kyWYT/DUj9Dv5cLW8pZP0MuYy0NdtSEb5WHcISFVXRVhXV/fTaZ/vLv04bXnBE6IwajFR7Y8yze078s7Frot/4Hy2XzisHozDKykguVg7AcJcdy3rJQMTDKgXF07JMcni23jLUuxJVhrfxqu0urPmbT60/FV+HxvU6r+bpJ9JI8p4pCCcb/8AJjAvcIlYMwmIgH4AQckcJAIxIqCK6yEF+rBC/UM589uWjfulefgyp42Xy6Fw44hvGzzgOFBV87NkwbdWRqZYCRRjDxqnNAINOfH0b1/D4nsP7vziTrhKiNK8eon4Jq/qOZd2YkbH0FzozGqiDxSoP1T1BfirsmnK6H9c8a5JmT6rPYaAf2aQKXY8K+PgoxX/UuZ5lmWJbrjKA4niijM8sYnSmOH1ArF2/1Cw554CtcvFX88PlDHvgKu46PP3bL1RZWdI0cQs7PsqSBsZGT8lsPSeXw6P+dziEPfKXh1+s1eU4VhQTGXzLD+CJYJSGMOEawbhUELzkCmRt9rNSwKkjuz8r2D9uWgqnsguE+C/Y7lvE/zwcWGf+z81mwf339ku0kKSqeiJLN5i0JlQxPC0dG2iFoSl5BUEdZCE21boS8wNLgq03j+iTY36r5kROB0tEAOrAF8qKqXt/uTDSDEx/5DO/t8a/fiY98hou3Su7nf2/2f4nHtatsyvkl1ErSvQb7TnzkMw3PQy/Jc4+LSu1onwkyYhSCvHJgtvlhl4MbSCQYUxxYFLxQUbD9cmWCtUTZ7I+n0J8NAreA73sovhNYxBnyk+2eBl+g7Ez67VnM2/skNrjmDBsiV8Nx2K5VpiBQSwVlGJhizTqhE1igJASBcMI+3iSlwYbS1SC2gmNlQB2loSGKghkq12E8JSJXAX/EmCoBun545IwnJ7NytuPKuinMeHIyvnocs9FQ0b6Vs++UPNYTv66pkjuJsze7hvOfGIgtB1cxOP+JAc5/YqBgf9wx5ekteU4VhRjGX3Ee0kdEQdBIFLJ8sJEgIllcJDKgYD2JOIvCcn3DpqIjCDCSCys9gGxfLl5p6HAyfZGoeH3DBYqWh3LfwDlsf6NxxIqL6BZHUlQ81Rw++QA3asevB45gfozSYLonzBj1sGvCsTIEIycCpWHihTN5/vDau3o61Et6NOaDsoezraLhVK3ksmc+yoHr31FROoBVOqO3oaXEldGq2beK0h24/h0FaYMyawWVPMN6iLvfKIFS4N53pe+XS6/Jc6ooxCBZv8B6EA1TGowrdsO9Bq3gINBIuO4oCZVUci7v6VsaKgn5aGQ57hs4h83/dDL9WePUBEZpCFrowWQpnUrgi7HFn09m66ETeeSTZ7PdnOOtspAfPrVcdllNZehGxFMbsCUumpuvRjkIlIZChcELx6ybqIx5/wV3uKQba6EulNARrlNQ1f3bnYdS/Pa5rQFYuUTFH6Qpl24ksnImb47fe937+e1zW/Pb57Zm5Uy+3A5c//6CMuxmVs78j98+tzV7r3t/yXRBGUS3VUWPyXOqKMTg9RWGKY0qBxnR2DClYRQyG5EMKApbGmyDQuXAR/jsXYfw+x0uZq87D8VXYfk+KQhjCiYS2Y43HcvoPi+s8EKlwfPZ8JozyPZ11gsax7azT2C5rImnDzC6b2lYhnM+diFgxrnHhX6F5DIMthUsONHxNAj5Giz5ELC+403uW8dG8QjHq8f7MuRjM9SDdqCpUkRmAYep6hv293uBC1T1623NGDBn/kas5BX+njyheNTXSt47TJ7wBHPm92w4iIqJllG0wlzJK+yKCMpt73ULy7VZZZn0DBvF3uveXzbv0feqVnpNnpumKNgoUJ8AXlXVTey204BvAP+0yU5Q1RvsvuOBA4Ac8F1VnWO3bw1cgTGb3IC50QYOSitk/d+dSbbPhhx1hgzFhSqNRiTLKwn5EKb5lnB+aFWUcEiVeuz79wO57iOXJeZvdHZZWOkFFd6yXIac+rZlbP5ueM0ZPPWZUxpfQA1gx5uOZbk+Pwy4MnD7YazQlw+bG7BS/7tFZQjElqNbhm7M/SAanhs+NxpK11UaTIAbP7Qy5Dwt2S1hfBXM3/E/mW5GohxUW7Cs5r3VNbNZ8FEBUNXXRWRLN4GILAfcDozCfE9+p6qnisiqwK8x4WIXAJ9X1dftMbGyXil3vTCRFSv8Bk+e8ERV6Xudu16YCMAO6zxftG9F792itCt6+WPy6ZqTt7g8NZoVvXe564WJRdcK7rGR99aN8pxEMy0KVwA/AKITUQyq6gx3g4hsBHwR2BgYC9wsIuurag74EXAQcA9GUZgC1OJdUpYNrzmDbNYoBJ5nw5Ra60FfxgQOiQtXmg9VaiKYuaFKM856gFvR5RxzOcBFW/6qZB5X6FsaVnJBYJY+O956OJcJW8jDXud+Gf+2+3kM3H5YGPt9hezSMGgKwP737U+f+KyYra0MffXIhRPzeCxTLwyja5SsTGIY3azvM5zzYmPvR7slQqUhY0NE1hGvInC6rAcRmQJcBGSAy1R1WmT/ypipZtfGyP4MVf1ZiVN6IvJep4JfleJvxhJgV1V9S0T6gDtFZAj4DHCLqk4TkeOA44Bjy8h6LO8sfSRcf/jFtVg+oZgffnEtNl/7paLty0vxkENPtBP7kFvGwy+uFa4HZRZUnu6+XmOHdZ4vur9HX1qT5csFuqmSeuW5CbIMlclzLE1TFFT1dhEZX2HyvYCrVXUJMF9EngW2FZEFwEqqejeAiFwJfIomKQp92VyighAEHcl6vg1dmguVhL4w8pj5beKaF4YsDVrPcV77gcn8tE2uK5vH3+9gJkf5wt0HM6xe+Hv3W6eyTPxQYUgKM9spvCe7jKz47Pv3A1nBzi9/8P370ic+K2X92LCvlZRhONlOJIRuoDAkhtD1/FABW+ZlbAhdj4yneYVBBPGMBSGvMGg4gsI4L9T+wamn4hKRDPBDYHdgIXCfiFwficL2beAJVf2kiKwOPC0iv1TVpQmnvQC4S0R+h+lc+TxwtpvAWvcCL7E+uyhGpne222cBtwLHkiDrwN2J9+asLyelnXaffmksG6y1qGBb0jEbrL2Ip18aW/J8I4FomZUr427Hvb+nXxpLP9QjtonUKs9NkmWoQJ6TaIePwqF2Xuy5wJFWuxmHsRgELLTbltn16Pam0N83TEbUKAfOhCf9BX+NgjDKG86HK/V8sjYCWRDhzQ35Wir0axCJrNphSL/e/hL2/fuB4e+bdh6s8+5bywrZJSbqm43odtTDX2CVvnwZlgqfC4XlGC1DY00wVoVlfhY/Y7oU4sLnLgui4FmlYWkuw1Lfr05hCII6+fmx0xN+cS7zv3J8VWXi1xdBc1vgWVV9HkBErsZUyu7HRYEVRUSAFYB/A8NJJ1TVK0VkLrAr5lP6mbjwr/bDdj+wHvBDVf27iHxAVRfb8ywWkffb5EmynoirGI6qIGjFgoVjGL/m4rLHLFg4hg3WWsyChWPKnrPXccuskjLuZjZYa1H4zEc10ahUhzw3XJahcnmOo9WKwo+AMzE3eSZGw/k68fpcUvMs8S0WkYMw3RSsvfbaiZkI+u83vf7UgrCl/ZlcgQXBVRD6rWIQxDQflRm2IUwDZWG4IGSpGy8+CP8aEGs2ryF06c8/nOzLUIqNrzutKSFLq2Gl7LuhopUPozscxoOPC6ELFISADYiWYRBbP4eH7wnLNGsqfas4LPGzodKwxM8yyioOS/0sWcnSr7mSCsNwzs5MFygMQQAnLx8+d/5XTqyqPNQO3yzBalbIAy5VVTdm7TjAtbsvBD4cOccPgOuBRcCKwBdUtaTpyX5ISn5MbLfBFiKyCvAHEdmkRPKKZNqV5XHj8i7o/RWaiBe9PJax4xaVPKbc/pFGUGbtKg/3mTUb9x7HjlvEopcba1mqU56bIstQmTzH0VJFQVX/EayLyE+AP9mfCwG342hNTAEstOvR7UnnvxS4FGCbbbaJVSgOnPs1PrK2+fvhyLux8aqvlL2HYRuf/R2/r2zaarhsmysaer5SbD9uQcuulcQ7fp+Z6rJDyDQhhG5VaFlT5Wuquk2J/ZVUwJOBhzAtinWBm0TkDlX9b8GJRB5Q1a1KZSYujaq+ISK3YvyI/iEiY6w1YQzwqk2WJOuFGXdkeYvN+/Wfi8ax+tiX6auhEks65p+LxpXcP1Jxy2P1sS8X7Q/KrZuJKiQNfwfqk+eGyTLULs8uLVUUgg+H/flp4DG7fj1wlYjMxDg4TQLuVdWciLwpItsBfwe+Cnw/et5KmfrQF1mxxB2vmH03eWcTGdzi6qacd+pDX4w992XbXJG4rxWUew69wtSHvlhVeq2v66GSCnh/YJr1K3hWROYDGwL3RtJ9UEQeIRkBVgaw/aPLrJIwGvh/wHkYmd4PmGb/Bg44sbJe7uY8++30quhM/teiNUse876xC8M0KYZ/LVqzbBkHykMvlV0171Wl1CHPjZRlqEKek2jm8MhfYZyZVhORhcCpwM4isgVGO1oAfBNAVR8Xkd9gTCLDwLcdL+hvkR8eOUQdjowrZJYk7jv50U+zQo8FZCl1v4NbXM3Jj36aMzf9QwtzZCiVr17h5Ec/zeAWf+BCfl3xMXUOp7oPmCQiE4CXMSML9omkeRHYDbhDRD4AbADEjUnbsILrBfI5Bphl/RQ84Deq+icRuRv4jYgcYK+7N5SV9VgEyNgP+fvGLuSNRY3xyn9j0VrheVPyrDK2eORIHM0ouzcWrVXx9Rt5zWbcSx3y3EhZhurkOZZmjnr4Uszmn5ZIfzYxHpiqOhco1ecZyyvvzivatmKmPRaDUtQWR7wyyt1vsD8a2xzam6+RiCpoHcOpVHVYRA4F5mCGVF1uK+WD7f5LMH5BV4jIo5j691hVfS3mXC9Ucd1HgKKx2Kr6L8yHLO6YWFkvhYfw30Vrs9LYF8u2/lYa+yL/XZTso+SmAypKO5IIyiMonyRWGftST5RdU6wJdchzI2XZpq9YnpPoWQNw3BC693jJLdlDN/wLP3hq12ZmqeW8x1vCD57alUM3/Evs/kAZOGajoZbd+w+e2pVjNuq9so6j2nusN0CLDV52Q2TbJc76IgrjvHcFgpBx+pAzFfQnV5Lm7cXrsPyYFypKOxIJyqcUvVB2zbqHeuS502S5ZxWFjPjMmrcD+026K9wWbcnuN+kuZs3bAYBZ83ZgxUzhtm7n0A3/UvG9RMsmWnZxBOcul85Nu2ImX9YpLlKvj0JP4zmjgrwKRghVkqaWtCmFLD/mBd5ZPKGh53xn8QRGj5nf0HOWojnPv7fkuWcVBQ8tsiDsN+mucHKPIM55nJXBTdftlLKiBPz2ua15T4yslJpAJemYaJp8Pspmo+0EE+O0jQ4bvi4iywPvqKovIutj+jqHVLWlQ0OEvHl4yeKJZU3FSxZPZPSY+SxZPLFo36gxz4dpKj1fSmmSyrrRBNcInmGjaNrz7yF57mFFwWf5mEoyui36+4b5m9jtsOeEx8Lf3cry3hJumL8Je054rGSaarhh/iYsX0HFv7y3pKvKsNL7agpa96iHZnA78FE7ecwtmCBpXwC+3NZcJTBqzPMtqbBSiml02S9ZPLElz7Np5+8xee5dRUGU5WUpty1YH4CPjX8GoKDCvG3B+olx4yvZ3w3sOeGxsAziqOUel5fCKKG3LVg/LN9oul4ow5bReXMPiKr+z45c+L6qni8iD7YhG2SkvAY3/Mp6jBrzPMOvrAeQeMzwK+tVdL6U6nDLvhEkPafgGtk1nq37Gk19D3pInntXUUB5j5ev0O57cTwfWntB+Bco2N/LvMdbWnDfAfe9OL7qLoGkY+LO/7Hxz3Dfi+OL0gbPISVCh5kqARGR7TEtjgPstq79ZgQVi4eHT2fPhZLSA/SQPHet0JfDQ1lOCkNfP/rSmiwn5u+may0sqNgefal3godE+dDaCwruL1hfrgKFNyirYL2SY1yiz6DW8/Q8nWmqPBw4HviDHZ41EfhrqzNhfBQqcGBcw1i1XIUgMW3qwNgUuqlc/VfWb15+e0yeK1IURGSUne2t5LZOwgOWKzGD4ryFJn7zpDVNwKtSaV0mrbkoPLabCCr7eQvH1lxJb7rWwpL3Hi3TSo5JcWhzCyQq06p6m4jcE2yzk9R8t20ZLIP/iuliCxQGl7htKaVJy6xOOsyioKq3Abc5vyuW50otCncD0TjQcds6BhFluSpmQYumXWvcYl56uXhWuZdeHtO1reF6817p8S+9PIa1xuVn76vmOYxkpP0tkLuBrUTkj+Q/czuJyO1uIlX9v5bnrEoCpQGKKzxvjWcKlAo3bUpKo+gAeQYgIs9FVCLPJRUFEVkDM5PVaBHZkvxkFSsB76k8q63HQ1iuAkeVYLIZN20Qy9yt7Nz0jSS4fitw76ea+4gro0qPA6o+bkSitK0FEiPnwbwMywObY2Z57SpKKQD+K+uHykOqJMTT69aEpj/3NspzDDPqPUE5i8Jk4GuYSSlmOtvfBE6o9+LNRIC+Cvuf3li0FquPfbmiGPKVnrPTqeU+Ki0jMGXaK2XVGgTa1wIpJedTrcmy50iVBENqVWkGbZXnAhohvyUVBVWdhZnw5bOq+vt6L9ZKBKGvypZsn3hlQ5auMvYl3l68Tj1ZK6KSUKmNptqyCfJY7rjlx7xQcflUk3ZE0CZH/Kic2/jxCrwPOE1ETrNJxSTXzdqT08pwLQaliKsg00qzPtLyc+iQgTWOPBftokJ5rshHwX48Pg5sDCznbD+jwry2HAGyVB4neMniiRWnDyr1ZgQYidsOjY1GVs29VnvcksUTWX7MC4ll4wZRqTUfPYnS9nHXjpz/ERjl7Ppem7LUEJIUhzglYSTS6Iq905WFluStA+TZ4RP1nqDSUQ+XYHwSdgEuAz5HBXPIt5fKgrTUQzMCjEQJ7mH4lfUaEmDEPWejya7xbMlgN0H+3X3uMSOdCgfeNO/68XL+ODBJVW8WkdF00ZDqaissV6Ho9Mqu00nLr/3yHODOHiki61CDPFdaY+ygql8FXlfV04HtgcZMCt8kgrHX1f6rllquUeu/RgheMHa4Gf+C8ogrl0BJiF6/mfnphH9dRlTOfwwM2L9gfBiubVPeaqJSp0XXmuAqCym1463xTFqGHYSIfAP4HTXIc6Wtg3fs3/+JyFjgX0BjpwzrUrpJc252PuNMvKWGpqUU0gHDqaJy/g3gVeAtAFWdJyLvb3WmFKqOpBioaK61yrdWOR8fzzmna9WKqnbeGs+MSItXI1XcZkXB9GuwsrbyWXaAPEf5NrAt8HeoTp4rfR/+JCKrANOBB4AFwNVVZ7PD6XTtt9Pz55KU11RJSEDLLK0hKucfxJFzEcm2NDd1kFQhuNuDSsbdFndco7r8Riojsvw6Q56jLFHVcN6CauS5UmfGM+3q70XkT8ByqvqfqrPZo3TDmOxW5S2wKgTXS7IyJDmSdXIZNpt292nGyPl5GCvDaBHZHTgE4+jY6pyR0+oLJwuJxwUfvrj9uRjH4iDdSJmhstGOKNk1nm1KuVWbz1reo1pptzzHcJuInEAN8lwu4NJnSuxDVa+pKps9TrMru0qHfcUd1yqi/btx104aklbu2J7vtmjTh6WEnN8B7AY8CnwTuAHj5NgVlKqYylVa0VFIwfpIUBJSGkTnKQrHYSaDqlqeyylkn4z8DswUYtc7VlGop1+zXprZr1lLv1wrZ8rznSlggzLwI2URHengO+ncewsUhqTz9BKiZmkTSXLeBwyr6t4AIpIBRgP/a2HerCzXVjijx8znncUTwr/1UO/xI51an2E35qHN8pzEaOByVf0JVCfP5QIu7W9PuBzwWWC8c0znFUMB1ZsrG2lua5apDTrb3BZes8S9V7LPbc0FykPPt+bqdH4SkSnARUAGuExVp8Wk2Rm4EKMEvKaqHysh5wcCs5zDRwM3AjvUldEaqFXZdYOZlTtHuQBgI2lq6mYEgeuE8mtpHuqQ51plucxpbwH+H9Y5mSrkudI651rgDYyD07t2W0crCgoMk6vqmFHlk1R3vjHPNyXyYLX5rLYcGoX7oam2HKL3OBIiONbTArGtgx8CuwMLgftE5HpVfcJJswpwMTBFVV+M8Xi+lkI594B/BztV9S0RafkcL4qS09oL57+L1malsS+y/JgXwvX/LloboGA9+Btsjx5fTx5S6Ijya2UeapXnBslyHMupaqAkVCXPlSoKa6rqlArTdgSKsqzKlvQbi9ZilbEvNSwPlc6L0GyqLYdGEXf/QfmWK5vgWQR/23UPLUPrdn7aFnjWTh2LiFwN7AU84aTZB7hGVV8EUNVXI+cokHPru3Cr83tr8kMoW0q9JmP3fUtaj0u/ytiXwmt3gum8m+mE8mtZHuqT50bIchxvi8hWqvqAPW/F8lyponCXiGyqqo9WmL7tKLCszaaudl8/oFPyAdXPWlntMV1N6W/YaiIy1/l9qape6vweB7ha7kLgw5FzrA/0icitwIrARap6pbM/KueHA78VkUX29xjgCxXcSUNRINfgD/z7xi7kX4vWDP8mEexLSuujeHTcePmOpNHPsOPzUPulGiHLcRxOjfJcqaLwEeBrIjIfWEIXTA7jo7zbxlboSy+Paeq546bATmL1sS83NT+NYK1xi4vyGLetlynTAnlNVbcpdXjMtuinKgtsjRnJMBq4W0TuUdVgKE2cnL8NfMuuP6Wqy8rdR6NRGm8Ve+XlsQV/qzkmSidUgM3glZfHssa4ReUTVniuZlBtHt83dmHT8hKljDyXUvwbIcvFJ1C9T0Q2BDagSnmuVFEYqDBdiIhcjpmM4lVV3cRuWxX4NcZZagHweVV93e47HjN0Iwd8V1Xn2O1bA1dgCuMG4DDV8h1NqsK7NUzKMW/hWCatWb9w1HLtSqklf25+Jq25iHkLWyMs1RAoP27emlmOHUd99c1CCsOqrwlEX5SFGIXjbYwZ8nZgcyD4uMTKuRsrPoqIrAVcCayBGRB2qapeVIusJ6Eoy1pUGUeV05GmrDaLVj2/SmhZXkpfppTi3whZjs+SUQweK5mzGCoNuFSL++sVwA8wH5GA44BbVHWaiBxnfx8rIhsBX8TMTjkWuFlE1lfVHPAj4CDgHoyiMAUYKndxH3hX2xdrv53XjiOan07J36ZrLeTRl/LmXLOez9ukNRcV7O9Z6h9OdR8wSUQmAC9j5GmfSJrrgB/YiGz9GHPmYJiF2uR8GDhSVR8QkRWB+0XkJuBrVC/rsajCshZ82yeuaRTVtcYt5vmFeeWgFdfuZdyybDTBM6uGljzP+uS5blluNE2bCU5VbxeR8ZHNewE72/VZGEepY+32q1V1CTBfRJ4FthWRBcBKqno3gIhcCXyKihQF4V2t7fbue3E8H1p7QU3HBtR67WZw34vjCR51cF+dkL8gL5uutTDctulaC21+83RCXltCHdZ1VR0WkUOBOZghVZer6uMicrDdf4mqPikis4FH7NUuU9Wi1oWIrB04SVVw3cXAYrv+pog8ieljrUrWgbsTr4E0VbHdaK2XeeKlvB+MWc9fr1OU6m6l08rPzU/02TeUGuW5kbIM1clzEq3+An/AflhQ1cXOkI5xGItBwEK7bZldj24vi4/wP7+/5ozetmB9Pja+9rkV6rl2I7ltwfoYhZOC+2l3/kqV7YfWXmDzbWh3XluBUH+AFlW9AWN1c7ddEvk9HTOXQymuBbaq9vq2YbAlZtKZamU9EWMdzFSbnYrYam3z/dxorZfDbRut9TIPvLh24u+RxAMvrh2WUa0069nVSpCf4L6akb965bmBsgw1yrNLpzTVkpw3KnHqyJ9E5CBMNwWrj+3jba2vgrlh/ibsOaHq7hxumL8JQeXcbuLKoJPyl4SrSNT7HLuC+odHNpKqHUNEZAXg98DhqvpfkcRTVCTTrix/YGyWt5ugLH50fHKE063WfpE7FuQjgTbj+t3CHQvWK1lW5eiksjPPtDA/Tclfl8tzlFYrCv8QkTG2hTEGM4UtJDtvLLTr0e2xWK/RSwHW3XR5fdtvdAilymjXdaP89rmtiQvP1O787b3u/VWl33PCY/ZeepzO6QsfJyLfS9qpqt91f4tIH0ZJ+KUz/0u1sh69RijLkzYdrW/qcjXfTK24lWOl18+pR6aDaohGUWujCSovu2ZjGkjFeWla/rpUnuNotaJwPbAfMM3+vc7ZfpWIzMQ4OE0C7lXVnIi8KSLbYcyZXwW+X8mFTNdDeyrEZl931rwd2G/SXTXno13lUkmek9h73fuZNa/lkYNbSgfVL+8AFWlzYkwHPwWeVNWZzq6qZL3UNXJ4vJFbvqobKMU+6/296mP2nPAYVz0bHcpeeM5S+0cqpkwa9+zizl/J84zmIzimmfnrRnlOommKgoj8CuPMtJqILAROxXw0fiMiBwAvAnsDWEeN32AiTw0D33a8oL9FfnjkEBU4MoLR7N/MtV6T/cFTuxKntbaDpPtvR7k0gm7Nd0WYmY86hX+p6qzyyQDYEdgXeFREHrLbTqA2WY8lpx5v5BoTOfqQDf5a87H7rPd3Ln56l9h9ZnvLo1t3PI16bvWS9Oyalr/uledYmjnq4UsJu3ZLSH82cHbM9rnAJtVe30d406+vYjl+oxvKJ7Kc+8Sedq1zKrOk+6+3XGqhmrJM4tAN/+KUc+/RQbPNLa00oareSXIfaFWynsSwZnh9uHmt0mo4ZIO/cvbjn2h3NjqawvLpjOdm8lScl2a+V90oz0l0ijNjw8mpx1vDrakQT3zkM3SSggDxeTLbKNreTbTqmbaDTjFVqup27c6DSw6P/wyPbnc2QjopL51Ip5XPMQ/vjTFIR7dRtL2R9JI896yi4Gt9wyOroROH73VanqY+9EUGt7i67vN02n01lM5pgXQUOfV4Y1lnmLABzt/8txw0d792Z6NjafWzOmjufly6TbJlvW3vTg/Jc+8qCni82aLWZ6uu43Lg3K9x2TZXJO5vR55aweAWV3Pg3K+1OxsNR+qPzNgwRGSCqs5vdz4Ccir8Z1nntFL3vutbeNI5+WkFv97+kvKJgC/cfTDNbKXXQjvenV6T555VFHIqvD3cmtZnq65TKV+65yA6PU5CrfTyvXVQC+R3wNYicouqxvoZtJKc7/HG0uYrvpNvO5w5H7uwbLr/LuuM4c+dxuTbDqdd3ZpJz66deeolee5ZRcFX4X8tqMD3uvNQOq3iasV9t4tevrdO6dMEPBE5FVhfRI6I7owMg2w6ORXeWtqcynnHm451fo1ix5uO5W+7n1fymGblpdvptHIxz7Z9eeolee5xRaGv6ddpxTWS2P3Wqdy082DRNmhfnmrB5Jmie4mjneXdVDorktsXMXOqZDFz3bcVXz3eXtoYBXHzP53Mw584ky3+fLLdUnzezf90MiLw0MfPLNpnjiufl4c+7l6jtwnKq90Npnw+AtqYnx6T595WFJY190UxGmv7Xsa4lk+z77mR5Ftz/eHvcq25brq/qukQU6WqPg2cJyKPqGpFcUuaia/CO0sapyBucM0Z1KpMV5qPeq7Rbby7dGTcZ9X0kDz3rKKgKrw73Jzb23roRO4fOLtp568mH8V07iMNyi2f7/i8Bunitnfy/dVLB7VAAu6yERR3sr9vA85Q1f+0MhPqw7IlrX/uE391Ds9/6YSCv9W8f89/6YTwPN1McB9JtOPZdAO9JM89+4R9laZpuu8u7WPj605jpLQYGkm5ciu1v9dbLp3iJe1wOfAY8Hn7e1/gZ8BnEo9oBir4S9szA+H4WecBmfBvrcf3Gua+Anrv/hpBL8lzzyoKqsKy4dpf4Gc+m9y/+Phep7H+74v7L1OaSzXP85nPntxdz6izQr4GrKuqn3V+n+6EaW4dvsC77a2MFnzzKMb/eEYN6XuvEu3V+2ooPSbPPawowHCTWiHGlJgKSjMJzL3u72rL/JnPntw1Zt96569vEu+IyEdsmGZEZEfMBDOtRUGW1j1Tbt1Umof53zmyqvSdzoTvXxDeExglaML3L2hjjjqfXpPnnlUUUMFf5jX0lOOvnGbXGnvelHjy5Q21lLk5vnuelfgd92U5GLhSRFa2v1/HzATZWhS8Je2tdNedMRMvcUqLPM8ddURV6buRXr63RtJL8tzDigLocGMriUafL6W5dNXz6qzhVACo6sPA5iKykv3933bkQxS84e6pmCZNG+ypinTecVMLfnfTs2gbPSbPPawoCLK0cRXF+EtmIF3UOu0lFhx8FOMvqaJ/2KbvuufVcQ0QQ7sUhHwGwKt7/rvW0U15LcfTpxQqCRucMdhtUtU+ekiee1hRAFnWOM13wcFpv1w7qfRZhv3DDXz2raLeFoiITAEuwjhzXKaq0xLSfQi4B/iCqv6uvqs2H/Eh0yWV70YnDfaU99JGJw3yxFl5ZeHpU6ay0UnlA6Ol1CfPnSbLva0oNNBENnFwJtJD5sRuotqy78pnVeckMiKSAX4I7A4sBO4TketV9YmYdOcBc2q/WotR8Ja0OxMjk8emF1oUNjk6tShURB3y3Imy3LOKgih4DWxVNvJcKc3h2WOmst753dk/LNRtUdgWeFZVnwcQkauBvYAnIum+A/we+FBF+RLZBNgIZ2YdVb2yrpxWiShklrXyiilxbH54b1lLmkmd8twUWbbnqkmee1ZRQMFr0Mdl/bNTLbrTeeZE0/Jp1DNvC1pXp+Y44CXn90Lgw24CERkHfBrYlQo+LnYimZ0xH5YbgAHgTqClikK3+Sj0ElseMsiDFzuy5byiD148lS0PSbshEqldnhsuy/aYmuW5txWF4cacqlHnSWkeG55uPlhdq9CV95JeTUTmOr8vVdVLnd9xZpTol+pC4FhVzYlUZHX5HLA58KCq7i8iHwAuq+TARiINVPpTaufBH05l62/kFYOtv5E2oBKpT56bIctQhzz3rKIggNRYwT95ZmG/3FOnTuWDJ6eac0pzkVzJ3a+p6jYl9i8E1nJ+rwksiqTZBrjaflhWA/YUkWFVvTbhnO+oqi8iw3ZI1avAxJK5bAYKmaUd6kI+Ath2v5ncO8vEh0ifQ+XUIc/NkGWoQ557VlFoZNfDxselmnNK86kzktt9wCQRmQC8jJladh83gapOCK8lcgXwpzIflrkisgrwE+B+4C3g3rpyWQPGRyGtoNrJ9vtcwN1XHZk+hyqoQ56bIctQhzynikIFpGbPlKaj9UVyU9VhETkU4wGdAS5X1cdF5GC7/5IaznmIXb1ERGYDK6nqIzVnslZ88NKWbNvZ8XMz0gZTpdQhz82QZXtczfLcs4qCKHilTT8VsfnhqTUhpUXUWReq6g0YJyV3W+xHRVW/Vu58YuyaXwYmquoZIrK2iGyrqi21Kohq2pJN6T7qeGUbLctQnzz3rKJg4ijUf5roOR76wVS2ODT1V0hpLKLaibHhL8bMgbcrcAbwJlUOx2oICpIqCildRK/Jc08rCrV2GWz1zUEe+HH8cLutvplaGFKaQwfONvdhVd1KRB4EUNXXRaS/1ZkwPgodFjg/JaUMvSTPbVEURGQBRpvJAcOquo2IrAr8GhgPLAA+r6qv2/THAwfY9N9V1bKRqMxEMvU/qQd+PJVtDphZ93lSUsrRaZPIAMts9DcFEJHVMS2SEBG5HPgE8KqqbmK3NVSWUUVSRSGly+hGeU6inRaFXVT1Nef3ccAtqjpNRI6zv48VkY0wXp8bA2OBm0VkfVUt64FQT/wDd0hQLee5d9YRbLtfqmCkVIgCuY5rgnwP+APwfhE5GzMO+6RImiuAH1AYtKWxsqzgpYpCSjfRvfIcSyd1PeyFiRoFMAu4FTjWbr9aVZcA80XkWUyIy7tLnaxRzoxQ23m2+8rMtIsipSo6yVQpIh4wHzgG2A0TmuRTqvqkm05VbxeR8ZHDGyrLAJJLFYVO48Z7TmGP7c5odzY6lm6U5yTapSgocKOIKPBjG5HqA6q6GEBVF4vI+23acZjZsQIW2m1lr+DV6QC1wxfMbJFphZ/SCjrJ+ckGZrlAVbcHnqry8IbKsqgiyxqk9ac0jMlbn8qN958erqcU0kPy3DZFYUdVXWQ/IDeJSKmMVxLO0iQUOQg4CGDU6FUa4qOQktISlE6cv/5GEfkscI1qfRNRWGqS5eX6VkY6z4w74pn9kLEmTNnilC6chq3J9Jg8t0VRUNVF9u+rIvIHjPnxHyIyxrZAxmDCS0Jl4SyD814KXAqw4ipravpxSekWBDqxMjwCWB4YFpF3MdlUVV2pzHENleWVR49RcqlFoZOY/ejZAEzZ9MQ256Qz6TF5br2iICLLA56qvmnX98CM6bwe2A+YZv9eZw+5HrhKRGZiHKAmUUnYyQZ0PaSktBJpSKO9cajqijUe2lhZprKP7tAT5xT8HtjohIoznFI9AxudkFoSStBD8twWi8IHgD/YySyywFWqOltE7gN+IyIHAC8CewPY0JW/wczFPQx8u5IRD9CRGl1KSjyq0EF9mgEi8l5Mhe7OX3+7s/9XGMfF1URkIXAqRkFonCwr4DozegK+MvT0NAY2OM78jiFQHAY2OI6hp6eZ9Q8eny/nyHmCNAEDGxxXNmsjjYIy6hEHU/eeGvbMu1Sek2i5oqCqz2Omuoxu/xfGGzPumLOBs6u5jhn10HkPKiUliU5yfgIQkQOBwzBdBA8B22FGKOwapFHVLyUc3jBZBjUfXsvQk/kPe7Ryj8NNM/TkuWXTxG0bWP/YinLayww9c17hhg5rMVfL0DPnFT3XoaenNexZd6M8J9FJwyMbi4Kkzowp3UL5+evbwWGY8K73qOouIrIhcHpbcuKbwhl6dnpbLj/0zHkMrHd0W67dKQysd3Rh+fud98JWSnAfRcpPwraqn32PyXPvKgpoOvY6pbvosBYI8K6qvisiiMgoVX1KRDZoeS4U00Xw/IyWX9rFrSQHJh7Vxpx0CJ33vsYy9PyMgudVy3sUPHuRKo7tvPKpWZ57V1FILQopXUanOT8BC+389ddihjG/TsIohWYyadM1GZrbXiUhSlDZDEw4os05aQ1D82OizGrnN8SCfA89P4OBCUfE30eT6CV57l1FAZAuNo2ljDA6MOSrqn7arp4mIn8FVgZmtzFLHcfQ/JkMjJ9qWo/WObLXGHrxwvgdrbLYesLQgspn7B0YPzU2fSuVhF6T555VFEQVGW7Oizxn7mkATN7mtKacP2XkIWgntkBCVPW2duehU3ErpYG1DmtjThrP0EsXJe9s4vta8rrljq1CqWgWvSbPPasomK6HxisKaTSylKaRWsC6nqGXLmJgze+2OxsNY2DN7zK08HvxO5tUESZer9voIXnuXUUBbeiDmv3IWeH6lM0qmnArJaVylAonfE3peDq4JTn08vfD9YFx36nvZE24Tzd/XU2PyXPvKgpN6iOasnEasjSO2Y/bkK5p+dRM6lPTI3TJc4xWygNjvl24f/EPGRjzbQbGfJuhxT+MPT56TM15iTl/t9NL8ty7igKN8zp1Q8N2cr9TtQw9cU7NYW6Tjg0Uhui+eq41ItD6LWAiMgW4CMgAl6nqtMj+L2OmewZ4C/iWqj5c10VTChhY45CWWxSG/vEjBj7wrfrPE6cMlKjAG3FNMPnvORogz51Eb8+grNqYpRnnbPMSKD9DT5xTe5kE54n5MA49cU64L+5aBfvsX3dpd/k05d0ph19iKYOIZIAfAgPARsCXRGSjSLL5wMdUdTPgTOykSymNYWD1g81IAF9bu2Aq22Apl7ZhNDD/PUl98jxFRJ4WkWdFpCiutIh8WUQesctdIrJ5YzNfSO9aFJSGvIRFoV174MWO3tPQk+dWHOM8LhxuUljcuH3u71LHu9tGSsz9Ok2V2wLP2hDpiMjVwF6YeRUAUNW7nPT3YEK5pjQIbXFcgdmvxet5Q/+8pOD3lNUOasr1673fpPz3CrXKs6P0746ZcfU+EbleVZ9wkgVK/+siMoBR+j9cZ5YT6V1FIRIfvlYG1j+2MKRnA87ZLuJCk4b7YmLdd0p8+0bGX+9Y6ldsxwEvOb8XUvrDcQAwVM8FU/JMWfUbLbvW7H//pLoDfGX2v3/ClFW/wZRVv1H98SXOm5JAffLccUp/DysKNKePqIv6nYaenR7GKK8pRn7kXtsVZx8KlZzejLmv5d6t1URkrvP7UlV1m2Rxo3Vjv1QisgtGUfhI1dlMiWX2v3/ClPce2Jxzv35ZfcdbxaBhCkLkvLXcd7331PnUJc8dp/T3rqLQgK6HaIxwqP+crSAML2vzXmuM/KFnp9d9jmbg5qunKG2tek1VtymxfyGwlvN7TWLCs4rIZsBlwICdsTWlAXSqktAKZr9+WdPuv6upXZ47TunvXUUBpZ5Y5G6M8MLTttei4IYhjYsz7+5vROXeSQqCS8/F2leFXK6eM9wHTBKRCcDLwBeBfdwEIrI2cA2wr6o+U8/FUiI0wdI4+z+XN/ycTaOLLK0toT557jilv4cVBZrjT9AiH4VKwpC2NHZ5hxLE2h9aMGhi7ncrSl2x81V1WEQOBeZghkderqqPi8jBdv8lwCnA+4CLRQRguIyVIqVSuth3qREESs2UlfYvn/a/P2t2dtpPffLccUp/7yoKdXQ9JE6CQu3nrPu6KYmESlUXdAuVpM7KRlVvAG6IbLvEWT8QSG3ETUB9nzlvzWLyCvuFf6tlzluzmpCz1jL7vz8ree+9cI8VU6M8d6LS37uKAnSVRaGeSVBSDEMvXtjFk/KUdX5K6WTsdyGoCIO/k5f/atuy1DYSvpFz3r6yxRlpJ/XJc6cp/T2sKOQfVLXxzQfGfSc55niDP+Y9E9u8U+jWylbp3rynJFaC0e2TR+8bn+6dnzc8T+0iuOekex0R9Jg8966ikPCg6o1v3siH34vxzdtONwtnN+c9pSJ6SSEox0i611h6SJ57V1GAiroJqo1v3qiuh6FXLm7IeVIitNipbOiVi018/7rp8XC2KSkjit6S5xGvKLT6nD05AUoH0KgJamohSemrKk8KWt/wyJSUlE6hx+S5hxWFJml0PaQl9hQtei7ROPol0/7jR4hUmL7+OAopKSmdQo/Jc88qCpM2X4ehuZV/1Ctl6J+XtDSue0p5mv08Gh36NpERPhY/JaWn6CF57llFoZnUGuO8G8KxdiVNEsjWPi/tKVNlSsrIprfk2Wt3Biql3Pzc7WD265cZz9YKllRJaCIVPoNqlpY/ryBAWNLSQ3SiLKekNJQek+euUBSc+bkHgI2AL4nIRu3NlWH2fy43LdqEZfZ/Lu+umO3dSKS8Sz2Pss8pOEerbwHj/JS09AqdLMspKY2i1+S5W7oeys7P3U5m//dnIzMCW4cQjR3v/q72ubQtDr3WN4lZF9HRspyS0hB6TJ67RVGodn7uljOywpN2D930XLqxpVEDHS/LKSmNoJfkuVsUhYrm5xaRg4CD7M8lIvJYU3PVeFYDXmt3JqokzXNp1qkk0Zu8Pudm/d1qJZJ0WxknMVJkuR66UabqpVvueUTKc7coChXNz62qlwKXAojI3G6bQjfNc2voxDyr6pR256FFjAhZroeRdr/Qe/fca/LcFc6MOPNzi0g/Zn7u69ucp5SUlOpJZTklpcvoCotC0vzcbc5WSkpKlaSynJLSfXSFogDx83OX4dJm5aWJpHluDd2Y555hhMhyPYy0+4WRec9dg2gPhZlMSUlJSUlJaSzd4qOQkpKSkpKS0gZ6TlHopPCwInK5iLzqDu0SkVVF5CYRmWf/vtfZd7zN99MiMtnZvrWIPGr3fU9E4oaYNSrPa4nIX0XkSRF5XEQO6/R8i8hyInKviDxs83x6p+c5pTI6SZ4bRSNlrFsQkYyIPCgif7K/e/ZeexJV7ZkF4xz1HDAR6AceBjZqY352ArYCHnO2nQ8cZ9ePA86z6xvZ/I4CJtj7yNh99wLbY8agDwEDTczzGGAru74i8IzNW8fm255/BbveB/wd2K6T85wuFT3XjpLnBt5Xw2SsWxbgCOAq4E/2d8/eay8uvWZRCMPDqupSIAgP2xZU9Xbg35HNewGz7Pos4FPO9qtVdYmqzgeeBbYVkTHASqp6txpJutI5phl5XqyqD9j1N4EnMdH0OjbfanjL/uyzi3ZynlMqoqPkuVE0SsZamuk6EJE1gY8D7kxrPXmvvUqvKQpx4WHHtSkvSXxAVReD+WAA77fbk/I+zq5HtzcdERkPbIlpoXd0vq1p8yHgVeAmVe34PKeUpRvkuS7qlLFu4ULgGMCd/KBX77Un6TVFoaLwsB1KUt7bck8isgLwe+BwVf1vqaQx21qeb1XNqeoWmEh/24rIJiWSd0SeU8rS08+jATLW8YjIJ4BXVfX+Sg+J2dYV99rL9JqiUFF42DbzD2vixv591W5PyvtCux7d3jREpA/zAfulql7TLfkGUNU3gFuBKXRJnlMS6QZ5rokGyVg3sCPwfyKyANN1tKuI/ILevNeepdcUhW4ID3s9sJ9d3w+4ztn+RREZJSITgEnAvdYs96aIbGc98L/qHNNw7DV+CjypqjO7Id8isrqIrGLXRwP/D3iqk/OcUhHdIM9V0ygZa1V+60FVj1fVNVV1POb5/UVVv0IP3mtP025vykYvwJ4YL+LngBPbnJdfAYuBZRhN+QDgfcAtwDz7d1Un/Yk230/jeNsD2wCP2X0/wAbKalKeP4Ix9T0CPGSXPTs538BmwIM2z48Bp9jtHZvndKn42XaMPDfwnhomY920ADuTH/XQ0/faa0samTElJSUlJSUlkV7rekhJSUlJSUlpIKmikJKSkpKSkpJIqiikpKSkpKSkJJIqCikpKSkpKSmJpIpCSkpKSkpKSiKpopASIiJXiMjn2p2PlJSU+khlOaWRpIpCSkpKSkpKSiKpojACEZHxIvKkiPxERB4XkRttREM3zW52/vhHReRyERllty8QkdNF5AG7b8P23EVKSkoqyymtIFUURi6TgB+q6sbAG8Bngx0ishxwBfAFVd0UyALfco59TVW3An4EHNWqDKekpMSSynJKU0kVhZHLfFV9yK7fD4x39m1g9z9jf88CdnL2X5NwXEpKSutJZTmlqaSKwshlibOew7Q0AuKmeo07NnpcSkpK60llOaWppIpCShxPAeNFZD37e1/gtjbmJyUlpTZSWU6pm1RRSClCVd8F9gd+KyKPAj5wSXtzlZKSUi2pLKc0gnT2yJSUlJSUlJREUotCSkpKSkpKSiKpopCSkpKSkpKSSKoopKSkpKSkpCSSKgopKSkpKSkpiaSKQkpKSkpKSkoiqaKQkpKSkpKSkkiqKKSkpKSkpKQkkioKKSkpKSkpKYn8fyhWF09rif2YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for src_grid, dst_grid in product(grids.keys(), grids.keys()): \n",
    "    if src_grid == dst_grid: continue\n",
    "    if src_grid.startswith(\"MOM\"): continue\n",
    "    if src_grid == \"POP_gx3v7\" and dst_grid.startswith(\"MOM\"): continue\n",
    "    key = get_key(src_grid, dst_grid)\n",
    "\n",
    "    da_src = grids[src_grid].TAREA.where(grids[src_grid].KMT > 0)\n",
    "    \n",
    "    da_dst = regrid_operator[key](\n",
    "        da_src,\n",
    "        renormalize=True, \n",
    "        apply_mask=True\n",
    "    )\n",
    "    \n",
    "    fig = plt.figure(figsize=(8, 3))\n",
    "    ax = fig.add_subplot(1, 2, 1)\n",
    "    da_src.plot()\n",
    "    ax.set_title(f'From grid: {src_grid}')\n",
    "    ax = fig.add_subplot(1, 2, 2)\n",
    "    da_dst.plot()\n",
    "    ax.set_title(f'To grid: {dst_grid}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.023037,
     "end_time": "2020-06-18T18:35:09.040770",
     "exception": false,
     "start_time": "2020-06-18T18:35:09.017733",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Define vertical interpolation\n",
    "\n",
    "Generate pseudo-data for each grid, a DataArray with 0:km-1 in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "papermill": {
     "duration": 6.7052,
     "end_time": "2020-06-18T18:35:15.766406",
     "exception": false,
     "start_time": "2020-06-18T18:35:09.061206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "da_test_src = {}\n",
    "for src_grid, grid in grids.items():\n",
    "    nk, nj, ni = len(grid.z_t), len(grid.nlat), len(grid.nlon)\n",
    "    k = xr.DataArray(np.arange(0, nk), dims=('z_t'), coords={'z_t': grids[src_grid].z_t})\n",
    "    ones = xr.DataArray(\n",
    "        np.ones((nk, nj, ni)), \n",
    "        dims=('z_t', 'nlat', 'nlon'), \n",
    "        coords={'z_t': grids[src_grid].z_t}\n",
    "    )\n",
    "    \n",
    "    da_test_src[src_grid] = k * ones "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.022027,
     "end_time": "2020-06-18T18:35:15.811575",
     "exception": false,
     "start_time": "2020-06-18T18:35:15.789548",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Test vertical remappiong function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "papermill": {
     "duration": 12.265846,
     "end_time": "2020-06-18T18:35:28.104582",
     "exception": false,
     "start_time": "2020-06-18T18:35:15.838736",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (z_t: 103, nlat: 2400, nlon: 3600)>\n",
      "array([[[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        ...,\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ]],\n",
      "\n",
      "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "...\n",
      "        [60.00003845, 60.00003845, 60.00003845, ..., 60.00003845,\n",
      "         60.00003845, 60.00003845],\n",
      "        [60.00003845, 60.00003845, 60.00003845, ..., 60.00003845,\n",
      "         60.00003845, 60.00003845],\n",
      "        [60.00003845, 60.00003845, 60.00003845, ..., 60.00003845,\n",
      "         60.00003845, 60.00003845]],\n",
      "\n",
      "       [[60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        ...,\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891]]])\n",
      "Coordinates:\n",
      "  * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
      "Dimensions without coordinates: nlat, nlon\n",
      "<xarray.DataArray (z_t: 103, nlat: 2400, nlon: 3600)>\n",
      "array([[[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        ...,\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ]],\n",
      "\n",
      "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "...\n",
      "        [60.00003845, 60.00003845, 60.00003845, ..., 60.00003845,\n",
      "         60.00003845, 60.00003845],\n",
      "        [60.00003845, 60.00003845, 60.00003845, ..., 60.00003845,\n",
      "         60.00003845, 60.00003845],\n",
      "        [60.00003845, 60.00003845, 60.00003845, ..., 60.00003845,\n",
      "         60.00003845, 60.00003845]],\n",
      "\n",
      "       [[60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        ...,\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891],\n",
      "        [60.99764891, 60.99764891, 60.99764891, ..., 60.99764891,\n",
      "         60.99764891, 60.99764891]]])\n",
      "Coordinates:\n",
      "  * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
      "Dimensions without coordinates: nlat, nlon\n",
      "<xarray.DataArray (z_t: 103, nlat: 384, nlon: 320)>\n",
      "array([[[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        ...,\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ]],\n",
      "\n",
      "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "...\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975]],\n",
      "\n",
      "       [[19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        ...,\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975]]])\n",
      "Coordinates:\n",
      "  * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
      "Dimensions without coordinates: nlat, nlon\n",
      "<xarray.DataArray (z_t: 103, nlat: 384, nlon: 320)>\n",
      "array([[[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        ...,\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ]],\n",
      "\n",
      "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
      "          0.        ,  0.        ],\n",
      "...\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975]],\n",
      "\n",
      "       [[19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        ...,\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975],\n",
      "        [19.66690975, 19.66690975, 19.66690975, ..., 19.66690975,\n",
      "         19.66690975, 19.66690975]]])\n",
      "Coordinates:\n",
      "  * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
      "Dimensions without coordinates: nlat, nlon\n"
     ]
    }
   ],
   "source": [
    "# unit test: apply remapping and confirm that values match\n",
    "da_dst = dict()\n",
    "\n",
    "for src_grid, dst_grid in product(grids.keys(), grids.keys()): \n",
    "    if src_grid == dst_grid: continue\n",
    "    if src_grid.startswith(\"MOM\"): continue\n",
    "    if src_grid == \"POP_gx3v7\" and dst_grid.startswith(\"MOM\"): continue\n",
    "    key = f\"{src_grid}_to_{dst_grid}\"\n",
    "\n",
    "    da_src = da_test_src[src_grid]\n",
    "\n",
    "    if not dst_grid.startswith(\"MOM\"):\n",
    "        da_dst[key] = util.remap_z_t(\n",
    "            da_src, \n",
    "            src_grid,\n",
    "            dst_grid,\n",
    "        )\n",
    "\n",
    "        nk_src, nk_dst = len(da_src.z_t), len(da_dst[key].z_t)\n",
    "        if nk_src < nk_dst:\n",
    "            assert (da_src[:, 0, 0] == da_dst[key][:nk_src, 0, 0]).all()\n",
    "            assert (da_src[-1, 0, 0] == da_dst[key][-(nk_dst-nk_src):, 0, 0]).all()        \n",
    "\n",
    "        elif nk_src > nk_dst:\n",
    "            assert (da_src[:nk_dst, 0, 0] == da_dst[key][:, 0, 0]).all()        \n",
    "\n",
    "        else:\n",
    "            assert (da_dst[key][:, 0, 0] == da_src[:, 0, 0]).all()\n",
    "    else:\n",
    "        da_dst[key] = util.reintegrate_z_t_to_MOM(da_src, src_grid)\n",
    "        print(da_dst[key])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (z_t: 103, nlat: 480, nlon: 540)&gt;\n",
       "array([[[  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        ...,\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       [[  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        ...,\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.]],\n",
       "\n",
       "       [[  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        [  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        [  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [100., 100., 100., ..., 100., 100., 100.],\n",
       "        [100., 100., 100., ..., 100., 100., 100.],\n",
       "        [100., 100., 100., ..., 100., 100., 100.]],\n",
       "\n",
       "       [[101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        ...,\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.]],\n",
       "\n",
       "       [[102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        ...,\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.]]])\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
       "Dimensions without coordinates: nlat, nlon</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>z_t</span>: 103</li><li><span>nlat</span>: 480</li><li><span>nlon</span>: 540</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ea744d8f-faef-40eb-ba0d-e2656e93732b' class='xr-array-in' type='checkbox' checked><label for='section-ea744d8f-faef-40eb-ba0d-e2656e93732b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 102.0 102.0 102.0 102.0 102.0 102.0</span></div><div class='xr-array-data'><pre>array([[[  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        ...,\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       [[  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        ...,\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.]],\n",
       "\n",
       "       [[  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        [  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        [  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [100., 100., 100., ..., 100., 100., 100.],\n",
       "        [100., 100., 100., ..., 100., 100., 100.],\n",
       "        [100., 100., 100., ..., 100., 100., 100.]],\n",
       "\n",
       "       [[101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        ...,\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.]],\n",
       "\n",
       "       [[102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        ...,\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-5d4e9bc0-901d-4289-9e93-b5b38e288604' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d4e9bc0-901d-4289-9e93-b5b38e288604' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 5.625e+05 5.875e+05</div><input id='attrs-76e23edd-767c-43f2-b83f-b40e7216a87f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76e23edd-767c-43f2-b83f-b40e7216a87f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ce44a58-7e7e-44df-8612-78fdb9778971' class='xr-var-data-in' type='checkbox'><label for='data-4ce44a58-7e7e-44df-8612-78fdb9778971' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>cm</dd></dl></div><div class='xr-var-data'><pre>array([2.500e+02, 7.500e+02, 1.250e+03, 1.750e+03, 2.250e+03, 2.750e+03,\n",
       "       3.250e+03, 3.750e+03, 4.250e+03, 4.750e+03, 5.250e+03, 5.750e+03,\n",
       "       6.250e+03, 6.750e+03, 7.250e+03, 7.750e+03, 8.250e+03, 8.750e+03,\n",
       "       9.250e+03, 9.750e+03, 1.125e+04, 1.375e+04, 1.625e+04, 1.875e+04,\n",
       "       2.125e+04, 2.375e+04, 2.625e+04, 2.875e+04, 3.125e+04, 3.375e+04,\n",
       "       3.625e+04, 3.875e+04, 4.125e+04, 4.375e+04, 4.625e+04, 4.875e+04,\n",
       "       5.250e+04, 5.750e+04, 6.250e+04, 6.750e+04, 7.250e+04, 7.750e+04,\n",
       "       8.250e+04, 8.750e+04, 9.250e+04, 9.750e+04, 1.025e+05, 1.075e+05,\n",
       "       1.125e+05, 1.175e+05, 1.225e+05, 1.275e+05, 1.325e+05, 1.375e+05,\n",
       "       1.425e+05, 1.475e+05, 1.525e+05, 1.575e+05, 1.625e+05, 1.675e+05,\n",
       "       1.725e+05, 1.775e+05, 1.825e+05, 1.875e+05, 1.925e+05, 1.975e+05,\n",
       "       2.050e+05, 2.150e+05, 2.250e+05, 2.350e+05, 2.450e+05, 2.550e+05,\n",
       "       2.650e+05, 2.750e+05, 2.850e+05, 2.950e+05, 3.050e+05, 3.150e+05,\n",
       "       3.250e+05, 3.350e+05, 3.450e+05, 3.550e+05, 3.650e+05, 3.750e+05,\n",
       "       3.850e+05, 3.950e+05, 4.050e+05, 4.150e+05, 4.250e+05, 4.350e+05,\n",
       "       4.450e+05, 4.550e+05, 4.650e+05, 4.750e+05, 4.850e+05, 4.950e+05,\n",
       "       5.050e+05, 5.150e+05, 5.250e+05, 5.350e+05, 5.450e+05, 5.625e+05,\n",
       "       5.875e+05])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f49b0d0-37f1-48a3-acfe-0a3637855b1b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6f49b0d0-37f1-48a3-acfe-0a3637855b1b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (z_t: 103, nlat: 480, nlon: 540)>\n",
       "array([[[  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        ...,\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       [[  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        ...,\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "        [  1.,   1.,   1., ...,   1.,   1.,   1.]],\n",
       "\n",
       "       [[  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        [  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        [  2.,   2.,   2., ...,   2.,   2.,   2.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [100., 100., 100., ..., 100., 100., 100.],\n",
       "        [100., 100., 100., ..., 100., 100., 100.],\n",
       "        [100., 100., 100., ..., 100., 100., 100.]],\n",
       "\n",
       "       [[101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        ...,\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.],\n",
       "        [101., 101., 101., ..., 101., 101., 101.]],\n",
       "\n",
       "       [[102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        ...,\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.],\n",
       "        [102., 102., 102., ..., 102., 102., 102.]]])\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float64 250.0 750.0 1.25e+03 ... 5.45e+05 5.625e+05 5.875e+05\n",
       "Dimensions without coordinates: nlat, nlon"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_test_src[src_grid]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.035465,
     "end_time": "2020-06-18T18:35:28.171324",
     "exception": false,
     "start_time": "2020-06-18T18:35:28.135859",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Define a fill procedure based on `pop_tools`. \n",
    "\n",
    "First, define a function to generate a 3D mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "papermill": {
     "duration": 0.448843,
     "end_time": "2020-06-18T18:35:28.642861",
     "exception": false,
     "start_time": "2020-06-18T18:35:28.194018",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_mask(ds_pop):\n",
    "    required_vars = ['z_t', 'KMT']\n",
    "    missing_vars = set(required_vars) - set(ds_pop.variables)\n",
    "    if missing_vars:\n",
    "        raise ValueError(f'missing required vars:\\n {missing_vars}')\n",
    "    \n",
    "    nk, nj, ni = len(ds_pop.z_t), len(ds_pop.nlat), len(ds_pop.nlon)\n",
    "\n",
    "    # make 3D array of 0:km\n",
    "    zero_to_km = xr.DataArray(np.arange(0, nk), dims=('z_t'))\n",
    "    ONES_3d = xr.DataArray(np.ones((nk, nj, ni)), dims=('z_t', 'nlat', 'nlon'))\n",
    "    ZERO_TO_KM = (zero_to_km * ONES_3d)\n",
    "\n",
    "    # mask out cells where k is below KMT\n",
    "    MASK = ZERO_TO_KM.where(ZERO_TO_KM < ds_pop.KMT)\n",
    "    MASK = xr.where(MASK.notnull(), True, False)\n",
    "\n",
    "    return MASK.assign_coords({'z_t': ds_pop.z_t})\n",
    "\n",
    "MASK = get_mask(grids['POP_gx3v7'])\n",
    "\n",
    "fig = plt.figure(figsize=(8, 3))\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "MASK.isel(z_t=0).plot()\n",
    "h = plt.title('Surface mask')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "MASK.isel(nlon=65).plot(yincrease=False)\n",
    "h = plt.title('Pacific transect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "papermill": {
     "duration": 2.370766,
     "end_time": "2020-06-18T18:35:31.038134",
     "exception": false,
     "start_time": "2020-06-18T18:35:28.667368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fill_unmapped_points(da, grid_name):\n",
    "    \"\"\"fill unmapped points\"\"\"\n",
    "    \n",
    "    ds_grid = grids[grid_name]\n",
    "    MASK = get_mask(ds_grid)\n",
    "    ltripole = (ds_grid.type == 'tripole')\n",
    "    \n",
    "    da_fill = pop_tools.lateral_fill(da.copy(), MASK, ltripole=ltripole)\n",
    "    \n",
    "    # propagate values down to fill vertically, if necessary\n",
    "    unfilled = da_fill.isnull() & (MASK == 1.) \n",
    "    if not unfilled.any():\n",
    "        return da_fill\n",
    "    \n",
    "    nk = len(da_fill.z_t)\n",
    "    for k in range(1, nk):\n",
    "        da_fill[k, :, :] = xr.where(\n",
    "            unfilled[k, :, :], \n",
    "            da_fill[k-1, :, :], \n",
    "            da_fill[k, :, :]\n",
    "        )    \n",
    "    return da_fill\n",
    "\n",
    "\n",
    "# Check to see if it works\n",
    "grid = 'POP_gx3v7'\n",
    "test_field = (get_mask(grids[grid]) * grids[grid].TAREA).where(get_mask(grids[grid]) > 0)\n",
    "test_field[:, 60:80, 60:80] = np.nan\n",
    "test_field_fill = fill_unmapped_points(test_field, grid)\n",
    "\n",
    "fig = plt.figure(figsize=(8, 3))\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "test_field.isel(z_t=0).plot()\n",
    "h = plt.title('Surface mask')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "test_field_fill.isel(z_t=0).plot()\n",
    "h = plt.title('Surface mask')\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(8, 3))\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "test_field.isel(nlon=65).plot(yincrease=False)\n",
    "h = plt.title('Pacific transect')\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "test_field_fill.isel(nlon=60).plot(yincrease=False)\n",
    "h = plt.title('Pacific transect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "papermill": {
     "duration": 0.034077,
     "end_time": "2020-06-18T18:35:31.109932",
     "exception": false,
     "start_time": "2020-06-18T18:35:31.075855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def ugrid_to_tgrid(arr_ugrid):\n",
    "    \"\"\"\n",
    "    Interpolates values at U points on a B grid to T points.\n",
    "    \"\"\"\n",
    "    with xr.set_options(keep_attrs=True):\n",
    "        return 0.25 * (\n",
    "            arr_ugrid +  # j, i\n",
    "            arr_ugrid.roll(nlat=1, roll_coords=False) + # j-1, i\n",
    "            arr_ugrid.roll(nlon=1, roll_coords=False) + # j, i-1\n",
    "            arr_ugrid.roll(nlat=1, nlon=1, roll_coords=False) #j-1, i-1\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025372,
     "end_time": "2020-06-18T18:35:31.160245",
     "exception": false,
     "start_time": "2020-06-18T18:35:31.134873",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Get model data and generate zarr archives "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "papermill": {
     "duration": 2.903589,
     "end_time": "2020-06-18T18:35:34.089336",
     "exception": false,
     "start_time": "2020-06-18T18:35:31.185747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9869c89064694742912b3158653b218c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(HTML(value='<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-outpu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ncar_jobqueue import NCARCluster\n",
    "cluster = NCARCluster()\n",
    "cluster.scale(36)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "papermill": {
     "duration": 13.220979,
     "end_time": "2020-06-18T18:35:47.341820",
     "exception": false,
     "start_time": "2020-06-18T18:35:34.120841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-058a5736-6c5d-11ee-b100-3cecef1acbfa</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_jobqueue.PBSCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/mlevy/MARBL_MOM/proxy/8787/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/mlevy/MARBL_MOM/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">PBSCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">f8b0bdf8</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/mlevy/MARBL_MOM/proxy/8787/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/mlevy/MARBL_MOM/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 0\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 0\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 0 B\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-cf60a193-046a-452c-b50c-2edbded0ff26</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://10.12.206.63:41473\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/mlevy/MARBL_MOM/proxy/8787/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/mlevy/MARBL_MOM/proxy/8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 0 B\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
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       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://10.12.206.63:41473' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "client = Client(cluster) # Connect this local process to remote workers\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "papermill": {
     "duration": 0.039606,
     "end_time": "2020-06-18T18:35:47.417093",
     "exception": false,
     "start_time": "2020-06-18T18:35:47.377487",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def ds_mean_time(ds, variables):\n",
    "    \"\"\"Compute the temporal mean of a dataset\n",
    "       using weights computed from `time_bound`.\n",
    "    \"\"\"\n",
    "    time_weights = ds.time_bound.diff('d2').squeeze()\n",
    "    time_total = time_weights.sum()\n",
    "    wgt = time_weights / time_total\n",
    "    np.testing.assert_allclose(wgt.sum(), 1.0, rtol=1e-7)\n",
    "\n",
    "    with xr.set_options(keep_attrs=True):\n",
    "        return (ds[variables] * wgt).sum('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.030019,
     "end_time": "2020-06-18T18:35:47.478042",
     "exception": false,
     "start_time": "2020-06-18T18:35:47.448023",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### empty markdown?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "papermill": {
     "duration": 0.36764,
     "end_time": "2020-06-18T18:35:47.871358",
     "exception": false,
     "start_time": "2020-06-18T18:35:47.503718",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "pth=/glade/campaign/cesm/development/omwg\n",
    "case=g.e20.G.TL319_t13.control.001_hfreq\n",
    "pth=${pth}/${case}/ocn/hist/mavg\n",
    "sc=/glade/scratch/mclong/campaign-mirror/${case}/ocn/hist/mavg\n",
    "\n",
    "# cp -v ${pth}/${case}.pop.h.004?-??.nc ${sc}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "papermill": {
     "duration": 0.057969,
     "end_time": "2020-06-18T18:35:47.974671",
     "exception": false,
     "start_time": "2020-06-18T18:35:47.916702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dsets = dict()\n",
    "\n",
    "grid_vars = ['TAREA', 'KMT', 'TLONG', 'TLAT', 'ULONG', 'ULAT', 'z_t'] #, 'time', 'time_bound']\n",
    "\n",
    "def get_data(src_grid, variables):\n",
    "    \"\"\"return a time-mean dataset\"\"\"\n",
    "    \n",
    "    if src_grid == 'POP_gx1v7':\n",
    "        \n",
    "        case = 'b.e21.B1850.f09_g17.CMIP6-piControl.001'\n",
    "        droot = f'/glade/campaign/collections/cmip/CMIP6/timeseries-cmip6/{case}'\n",
    "        droot = f'{droot}/ocn/proc/tseries/month_1'\n",
    "        datestr = '060001-069912'\n",
    "\n",
    "        ds_grid = pop_tools.get_grid('POP_gx1v7')\n",
    "\n",
    "        ds = xr.Dataset()\n",
    "        input_files = []\n",
    "        for v in variables:\n",
    "            files = f'{droot}/{case}.pop.h.{v}.{datestr}.nc'\n",
    "            input_files += ','.join(files)+'; '\n",
    "            dsv = xr.open_dataset(\n",
    "                files, \n",
    "                decode_times=False,\n",
    "                decode_coords=False,\n",
    "                chunks={'time': 24, 'z_t': 20},\n",
    "            )\n",
    "            ds = xr.merge((ds, dsv))\n",
    "\n",
    "        with util.timer('computing time mean'):\n",
    "            ds = ds_mean_time(ds, variables).drop('z_t').compute()\n",
    "\n",
    "    elif src_grid == 'POP_tx0.1v3':\n",
    "       \n",
    "        case = 'g.e20.G.TL319_t13.control.001_hfreq'        \n",
    "        #droot = '/glade/campaign/cesm/development/omwg'        \n",
    "        droot = '/glade/scratch/mclong/campaign-mirror'\n",
    "        droot = f'{droot}/{case}/ocn/hist/mavg'\n",
    "\n",
    "        ds_grid = pop_tools.get_grid('POP_tx0.1v3')\n",
    "\n",
    "        years = np.arange(50, 60, 1)\n",
    "\n",
    "        print('computing each annual mean')    \n",
    "        ds_sum = xr.Dataset()\n",
    "        n = 0\n",
    "        input_files = []\n",
    "        for y in years:\n",
    "            files = sorted(glob(f'{droot}/{case}.pop.h.{y:04d}-??.nc'))        \n",
    "            if len(files) != 12:\n",
    "                raise ValueError('expected 12 files')                \n",
    "            \n",
    "            input_files.extend(files)\n",
    "            \n",
    "            print(f'averaging {len(files)} files')\n",
    "            with util.timer(f'year: {y:04d}'):                            \n",
    "                ds = xr.open_mfdataset(\n",
    "                    files, \n",
    "                    combine='nested', \n",
    "                    concat_dim='time',\n",
    "                    chunks={'time': 1, 'nlon': 900, 'nlat': 600},\n",
    "                    decode_times=False,\n",
    "                    decode_coords=False,\n",
    "                )\n",
    "                ds = ds_mean_time(ds, variables).drop('z_t').compute()\n",
    "\n",
    "                if n == 0:\n",
    "                    ds_sum = ds\n",
    "                else:\n",
    "                    ds_sum = ds_sum + ds\n",
    "                n += 1\n",
    "\n",
    "        with util.timer('computing long-term mean'):\n",
    "            ds = ds_sum / n\n",
    "\n",
    "    else:\n",
    "        raise ValueError(f'unknown grid: {grid}')\n",
    "        \n",
    "    ds.attrs['input_files'] = ','.join(input_files)\n",
    "    \n",
    "    return xr.merge((ds, ds_grid[grid_vars]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "papermill": {
     "duration": 0.046657,
     "end_time": "2020-06-18T18:35:48.053274",
     "exception": false,
     "start_time": "2020-06-18T18:35:48.006617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "var_groups = dict(\n",
    "    poc_flux=['POC_FLUX_IN'],\n",
    "    velocity=['UVEL', 'VVEL'],\n",
    ")\n",
    "\n",
    "        \n",
    "def proc_var_group(var_group, src_grid, dst_grid=None):\n",
    "    \"\"\"Generate output dataset    \n",
    "    \"\"\"\n",
    "\n",
    "    if dst_grid is None:\n",
    "        dst_grid = src_grid\n",
    "\n",
    "    variables = var_groups[var_group]\n",
    "    key = f'{src_grid}-{var_group}'\n",
    "       \n",
    "    # get the input dataset\n",
    "    if key in dsets:\n",
    "        print('cached dataset')               \n",
    "        ds_src = dsets[key]\n",
    "        \n",
    "    else:\n",
    "        print('generating dataset from files')       \n",
    "        ds_src = get_data(src_grid, variables)\n",
    "        \n",
    "        # put UVEL and VVEL on T-Grid\n",
    "        if var_group == 'velocity':\n",
    "            for v in variables:\n",
    "                ds_src[v] = ugrid_to_tgrid(ds_src[v])\n",
    "\n",
    "        dsets[key] = ds_src           \n",
    "       \n",
    "    # return dataset, performing regridding if necesssary\n",
    "    if src_grid == dst_grid:\n",
    "        return ds_src\n",
    "    \n",
    "    else:       \n",
    "        ds_dst = xr.Dataset()\n",
    "\n",
    "        for v in variables:\n",
    "            # perform lateral regridding\n",
    "            key = get_key(src_grid, dst_grid)\n",
    "            da_dst = regrid_operator[key](\n",
    "                ds_src[v], \n",
    "                renormalize=True, \n",
    "                apply_mask=True\n",
    "            )\n",
    "            \n",
    "            # perform vertical regridding\n",
    "            if not dst_grid.startswith(\"MOM\"):\n",
    "                da_dst = util.remap_z_t(da_dst, src_grid, dst_grid)\n",
    "            else:\n",
    "                da_dst = util.reintegrate_z_t_to_MOM(da_dst, src_grid)\n",
    "            \n",
    "            # fill missing values left on dst_grid\n",
    "            da_dst = fill_unmapped_points(da_dst, dst_grid)\n",
    "            \n",
    "            # copy DataArray to outgoing Dataset\n",
    "            ds_dst[v] = da_dst\n",
    "            \n",
    "        return ds_dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dst_grid = 'MOM_tx0.66v1'\n",
    "dst_grid = 'MOM_tx2_3v2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "papermill": {
     "duration": 0.059042,
     "end_time": "2020-06-18T18:35:48.137555",
     "exception": false,
     "start_time": "2020-06-18T18:35:48.078513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "poc_flux: POP_gx1v7 --> MOM_tx2_3v2\n",
      "generating dataset from files\n",
      "[computing time mean]: 134.90 sec\n",
      "xarray.Dataset {\n",
      "dimensions:\n",
      "\tz_t = 103 ;\n",
      "\tnlat = 480 ;\n",
      "\tnlon = 540 ;\n",
      "\n",
      "variables:\n",
      "\tfloat64 z_t(z_t) ;\n",
      "\t\tz_t:units = cm ;\n",
      "\tfloat64 POC_FLUX_IN(z_t, nlat, nlon) ;\n",
      "\t\tPOC_FLUX_IN:long_name = POC Flux into Cell ;\n",
      "\t\tPOC_FLUX_IN:units = mmol/m^3 cm/s ;\n",
      "\t\tPOC_FLUX_IN:coordinates = TLONG TLAT z_t time ;\n",
      "\t\tPOC_FLUX_IN:grid_loc = 3111 ;\n",
      "\t\tPOC_FLUX_IN:cell_methods = time: mean ;\n",
      "\t\tPOC_FLUX_IN:regrid_method = conservative ;\n",
      "\t\tPOC_FLUX_IN:history = \n",
      "2023-10-16 13:56:56.681266 esmlab.regrid <regrid>  ;\n",
      "\n",
      "// global attributes:\n",
      "}\n",
      "--------------------------------------------------------------------------------\n",
      "velocity: POP_gx1v7 --> MOM_tx2_3v2\n",
      "generating dataset from files\n",
      "[computing time mean]: 105.84 sec\n",
      "xarray.Dataset {\n",
      "dimensions:\n",
      "\tz_t = 103 ;\n",
      "\tnlat = 480 ;\n",
      "\tnlon = 540 ;\n",
      "\n",
      "variables:\n",
      "\tfloat64 z_t(z_t) ;\n",
      "\t\tz_t:units = cm ;\n",
      "\tfloat64 UVEL(z_t, nlat, nlon) ;\n",
      "\t\tUVEL:long_name = Velocity in grid-x direction ;\n",
      "\t\tUVEL:units = centimeter/s ;\n",
      "\t\tUVEL:coordinates = ULONG ULAT z_t time ;\n",
      "\t\tUVEL:grid_loc = 3221 ;\n",
      "\t\tUVEL:cell_methods = time: mean ;\n",
      "\t\tUVEL:regrid_method = conservative ;\n",
      "\t\tUVEL:history = \n",
      "2023-10-16 13:59:10.756431 esmlab.regrid <regrid>  ;\n",
      "\tfloat64 VVEL(z_t, nlat, nlon) ;\n",
      "\t\tVVEL:long_name = Velocity in grid-y direction ;\n",
      "\t\tVVEL:units = centimeter/s ;\n",
      "\t\tVVEL:coordinates = ULONG ULAT z_t time ;\n",
      "\t\tVVEL:grid_loc = 3221 ;\n",
      "\t\tVVEL:cell_methods = time: mean ;\n",
      "\t\tVVEL:regrid_method = conservative ;\n",
      "\t\tVVEL:history = \n",
      "2023-10-16 14:00:12.116091 esmlab.regrid <regrid>  ;\n",
      "\n",
      "// global attributes:\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "var_group_grids = dict(\n",
    "    poc_flux=[\n",
    "        ('POP_gx1v7', dst_grid), \n",
    "        # ('POP_gx1v7', 'MOM_tx0.66v1'), \n",
    "    ],\n",
    "    velocity=[\n",
    "        ('POP_gx1v7', dst_grid), \n",
    "        # ('POP_gx1v7', 'MOM_tx0.66v1'), \n",
    "#         ('POP_tx0.1v3', 'MOM_tx0.66v1'),\n",
    "    ],\n",
    ")\n",
    "\n",
    "for var_group, src_dst_list in var_group_grids.items():\n",
    "    for src_grid, dst_grid in src_dst_list:\n",
    "        print('-'*80)\n",
    "        print(f'{var_group}: {src_grid} --> {dst_grid}')\n",
    "        \n",
    "        file_out = f'{util.dirwork}/{var_group}.{src_grid}_to_{dst_grid}.zarr'\n",
    "        \n",
    "        if clobber and os.path.exists(file_out):\n",
    "            shutil.rmtree(file_out)            \n",
    "        \n",
    "        if os.path.exists(file_out):\n",
    "            print(f'existing file: {file_out}')\n",
    "            #dso = xr.open_zarr(file_out)\n",
    "        else: \n",
    "            dso = proc_var_group(var_group, src_grid, dst_grid)\n",
    "            dso.to_zarr(file_out)\n",
    "            dso.info()\n",
    "        print()            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()\n",
    "cluster.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some POC conservation tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_lateral_mean_vertical_sum_POC(ds, area):\n",
    "    return (ds[\"POC_FLUX_IN\"]*ds[area]).sum().data/1e4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cached dataset\n"
     ]
    }
   ],
   "source": [
    "dso = proc_var_group(\"poc_flux\", src_grid, dst_grid)\n",
    "ds_mom_grid = util.get_MOM_grid(dst_grid)\n",
    "dso[\"TAREA\"] = ds_mom_grid[\"TAREA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47340701001320.66"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_POC_sum = compute_lateral_mean_vertical_sum_POC(dsets[\"POP_gx1v7-poc_flux\"], \"TAREA\")\n",
    "pop_POC_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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