model_evaluate_sensitivity..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fourth-glass",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import gcsfs\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import time\n",
    "from matplotlib.colors import LogNorm\n",
    "from IPython.display import clear_output\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "\n",
    "from intake import open_catalog\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "import pandas as pd\n",
    "from timeit import default_timer as timer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "coated-cause",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 14\n",
    "    , 'legend.markerscale': 1., 'axes.titlesize': 14, 'axes.labelsize' : 12,\n",
    "      'legend.fontsize' : 13,'legend.handlelength': 3})\n",
    "\n",
    "plt.rc('xtick', labelsize=10) \n",
    "plt.rc('ytick', labelsize=10)\n",
    "\n",
    "color2 = '#6495ed'\n",
    "color1 = '#ff6347'\n",
    "color5 = '#8470ff'\n",
    "color3 = '#3cb371'\n",
    "color4 = '#ffd700'\n",
    "color6 = '#ba55d3'\n",
    "# plt.rcParams['figure.figsize'] = (14.,5.)\n",
    "\n",
    "\n",
    "\n",
    "blue = cm.get_cmap('Blues', 256)\n",
    "green = cm.get_cmap('Greens', 256)\n",
    "red = cm.get_cmap('Reds', 256)\n",
    "grey = cm.get_cmap('Greys', 256)\n",
    "bone = cm.get_cmap('bone_r', 256)\n",
    "binary = cm.get_cmap('binary', 256)\n",
    "hot = cm.get_cmap('hot_r', 256)\n",
    "gray = cm.get_cmap('gray_r', 256)\n",
    "\n",
    "\n",
    "newcolors5 = blue(np.linspace(0, 1, 256))**2*grey(np.linspace(0, 1, 256))**0.2\n",
    "newcmp5 = ListedColormap(newcolors5[::-1], name='BlueGrey')\n",
    "\n",
    "newcolors7 = blue(np.linspace(0, 1, 256))**1.5*green(np.linspace(0, 1, 256))**0.3*grey(np.linspace(0, 1, 256))**0.03\n",
    "newcmp7 = ListedColormap(newcolors7[::-1], name='BluegreenGrey')\n",
    "\n",
    "newcmp6 = ListedColormap(newcolors5, name='BlueGrey_r')\n",
    "newcmp8 = ListedColormap(newcolors7, name='BlueGreenGrey_r')\n",
    "\n",
    "def plot_examples(cms):\n",
    "    \"\"\"\n",
    "    helper function to plot two colormaps\n",
    "    \"\"\"\n",
    "    np.random.seed(19680801)\n",
    "    data = np.random.randn(30, 30)\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(6, 3), constrained_layout=True)\n",
    "    for [ax, cmap] in zip(axs, cms):\n",
    "        psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)\n",
    "        fig.colorbar(psm, ax=ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "owned-competition",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = open_catalog(\"https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/CESM_POP.yaml\")\n",
    "ds  = cat[\"CESM_POP_hires_control\"].to_dask()\n",
    "# ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "artificial-interest",
   "metadata": {},
   "outputs": [
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       "    salt_to_mmday       float64 ...\n",
       "    salt_to_ppt         float64 ...\n",
       "    sea_ice_salinity    float64 ...\n",
       "    sflux_factor        float64 ...\n",
       "    sound               float64 ...\n",
       "    stefan_boltzmann    float64 ...\n",
       "  * time                (time) object 0046-01-02 00:00:00 ... 0087-01-01 00:0...\n",
       "    vonkar              float64 ...\n",
       "  * z_t                 (z_t) float32 500.0 1.5e+03 ... 5.625e+05 5.875e+05\n",
       "  * z_t_150m            (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
       "  * z_w                 (z_w) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n",
       "  * z_w_bot             (z_w_bot) float32 1e+03 2e+03 3e+03 ... 5.75e+05 6e+05\n",
       "  * z_w_top             (z_w_top) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    HMXL_2              (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    SFWF_2              (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    SHF_2               (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    SSH                 (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    SSS                 (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    SST                 (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    TAUX                (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    TAUY                (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    U                   (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    U2_2                (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    V                   (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    V2_2                (time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;\n",
       "    X                   (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    Y                   (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    Z                   (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    dx                  (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "    dy                  (nlat, nlon) float64 dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:   CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-curren...\n",
       "    calendar:      All years have exactly  365 days.\n",
       "    cell_methods:  cell_methods = time: mean ==&gt; the variable values are aver...\n",
       "    contents:      Diagnostic and Prognostic Variables\n",
       "    history:       none\n",
       "    nsteps_total:  13088337\n",
       "    revision:      $Id: tavg.F90 41939 2012-11-14 16:37:23Z mlevy@ucar.edu $\n",
       "    source:        CCSM POP2, the CCSM Ocean Component\n",
       "    start_time:    This dataset was created on 2013-03-09 at 12:25:55.7\n",
       "    tavg_sum:      86399.99999999962\n",
       "    title:         hybrid_v5_rel04_BC5_ne120_t12_pop62</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8c84d161-5ed5-46ed-9e6c-1ac5d033515d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8c84d161-5ed5-46ed-9e6c-1ac5d033515d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nlat</span>: 2400</li><li><span>nlon</span>: 3600</li><li><span class='xr-has-index'>time</span>: 14965</li><li><span class='xr-has-index'>z_t</span>: 62</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li><li><span class='xr-has-index'>z_w</span>: 62</li><li><span class='xr-has-index'>z_w_bot</span>: 62</li><li><span class='xr-has-index'>z_w_top</span>: 62</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6ca00b69-8add-4d94-aaeb-4afb3583e43e' class='xr-section-summary-in' type='checkbox'  ><label for='section-6ca00b69-8add-4d94-aaeb-4afb3583e43e' class='xr-section-summary' >Coordinates: <span>(59)</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>ANGLE</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-a3a29fcb-b5a2-463b-b09b-70463f0391b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3a29fcb-b5a2-463b-b09b-70463f0391b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6113572b-4410-4327-8c1f-757c874e5b63' class='xr-var-data-in' type='checkbox'><label for='data-6113572b-4410-4327-8c1f-757c874e5b63' 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>long_name :</span></dt><dd>angle grid makes with latitude line</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"80\" x2=\"120\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"80\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"40.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,40.000000)\">2400</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ANGLET</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-9235e560-c05d-4d80-9c48-273155b6e585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9235e560-c05d-4d80-9c48-273155b6e585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05e59654-9a95-4b03-b480-b224669c7269' class='xr-var-data-in' type='checkbox'><label for='data-05e59654-9a95-4b03-b480-b224669c7269' 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>long_name :</span></dt><dd>angle grid makes with latitude line on T grid</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <text x=\"140.000000\" y=\"40.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,40.000000)\">2400</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DXT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-a2820bf1-4819-4444-997f-53ee77a68664' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2820bf1-4819-4444-997f-53ee77a68664' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3f18787-2fd6-4156-9e4c-cc1545d76d0b' class='xr-var-data-in' type='checkbox'><label for='data-e3f18787-2fd6-4156-9e4c-cc1545d76d0b' 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>long_name :</span></dt><dd>x-spacing centered at T points</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DXU</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-f097cf08-f528-4d09-8c0a-67cee4d25db2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f097cf08-f528-4d09-8c0a-67cee4d25db2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e35cee6a-3108-4175-95b3-95db5a0d6c63' class='xr-var-data-in' type='checkbox'><label for='data-e35cee6a-3108-4175-95b3-95db5a0d6c63' 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>long_name :</span></dt><dd>x-spacing centered at U points</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DYT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-5bf888bc-6b43-4e8e-a2b7-a947c556d15c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf888bc-6b43-4e8e-a2b7-a947c556d15c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f705cb27-5a6f-4e26-9a4e-61e48a4a6a33' class='xr-var-data-in' type='checkbox'><label for='data-f705cb27-5a6f-4e26-9a4e-61e48a4a6a33' 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>long_name :</span></dt><dd>y-spacing centered at T points</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DYU</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-78809fab-f510-467c-8edd-9dd5eada9ae9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78809fab-f510-467c-8edd-9dd5eada9ae9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dddc040-36ab-43f3-8a69-0b55eeee4397' class='xr-var-data-in' type='checkbox'><label for='data-3dddc040-36ab-43f3-8a69-0b55eeee4397' 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>long_name :</span></dt><dd>y-spacing centered at U points</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-6355bcc8-65bb-4ac2-9d23-37677eb70697' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6355bcc8-65bb-4ac2-9d23-37677eb70697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff58dff4-2ce5-4a5c-ad0e-754ca2518411' class='xr-var-data-in' type='checkbox'><label for='data-ff58dff4-2ce5-4a5c-ad0e-754ca2518411' 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>long_name :</span></dt><dd>ocean depth at T points</dd><dt><span>units :</span></dt><dd>centimeter</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HTE</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-a3573d7a-d5d4-41d9-ba71-997470cb6055' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3573d7a-d5d4-41d9-ba71-997470cb6055' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d15333c-7319-485c-b26e-7f1a7dc3a44e' class='xr-var-data-in' type='checkbox'><label for='data-9d15333c-7319-485c-b26e-7f1a7dc3a44e' 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>long_name :</span></dt><dd>cell widths on East sides of T cell</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HTN</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-53930d8d-4ae1-4da1-8768-cffb71afb6de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53930d8d-4ae1-4da1-8768-cffb71afb6de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3430746-905b-4db7-9e3e-88a0f0519b5a' class='xr-var-data-in' type='checkbox'><label for='data-f3430746-905b-4db7-9e3e-88a0f0519b5a' 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>long_name :</span></dt><dd>cell widths on North sides of T cell</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HU</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-bca6931f-2a6d-49fd-a2bf-ad889a7112cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bca6931f-2a6d-49fd-a2bf-ad889a7112cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a13f8f13-68e7-487f-9dc2-56eaecf3a917' class='xr-var-data-in' type='checkbox'><label for='data-a13f8f13-68e7-487f-9dc2-56eaecf3a917' 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>long_name :</span></dt><dd>ocean depth at U points</dd><dt><span>units :</span></dt><dd>centimeter</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HUS</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-02329b47-137c-4783-a136-b5d9c3cee443' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02329b47-137c-4783-a136-b5d9c3cee443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad69b5b8-18dc-4599-a2d3-5f53de7bf5b7' class='xr-var-data-in' type='checkbox'><label for='data-ad69b5b8-18dc-4599-a2d3-5f53de7bf5b7' 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>long_name :</span></dt><dd>cell widths on South sides of U cell</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HUW</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-b48f2726-97b3-464d-80e4-20c7e84d63dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b48f2726-97b3-464d-80e4-20c7e84d63dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2258d51-8a24-4a4d-a149-0efe7134fd56' class='xr-var-data-in' type='checkbox'><label for='data-e2258d51-8a24-4a4d-a149-0efe7134fd56' 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>long_name :</span></dt><dd>cell widths on West sides of U cell</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KMT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-73bfb3e9-236f-428b-a5af-d9718077aeac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73bfb3e9-236f-428b-a5af-d9718077aeac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f061fef-2681-4352-808d-8099ea6363e5' class='xr-var-data-in' type='checkbox'><label for='data-8f061fef-2681-4352-808d-8099ea6363e5' 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>long_name :</span></dt><dd>k Index of Deepest Grid Cell on T Grid</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KMU</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-136aa0fb-b498-46f8-89b9-43eff0a33d90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-136aa0fb-b498-46f8-89b9-43eff0a33d90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3fd022b-cc00-474a-b893-dfeb02ff9c07' class='xr-var-data-in' type='checkbox'><label for='data-b3fd022b-cc00-474a-b893-dfeb02ff9c07' 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>long_name :</span></dt><dd>k Index of Deepest Grid Cell on U Grid</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>REGION_MASK</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-5b5cbf8a-d93c-4a8a-ae2e-7640eca1b42a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b5cbf8a-d93c-4a8a-ae2e-7640eca1b42a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81dde2d3-6d23-4a21-980f-3d9c3a0b2cc9' class='xr-var-data-in' type='checkbox'><label for='data-81dde2d3-6d23-4a21-980f-3d9c3a0b2cc9' 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>long_name :</span></dt><dd>basin index number (signed integers)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T0_Kelvin</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c61f515-37c7-43a8-abd3-b9f626553e3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c61f515-37c7-43a8-abd3-b9f626553e3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c42fbabb-998e-458c-be9f-adc78ce733c8' class='xr-var-data-in' type='checkbox'><label for='data-c42fbabb-998e-458c-be9f-adc78ce733c8' 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>long_name :</span></dt><dd>Zero Point for Celsius</dd><dt><span>units :</span></dt><dd>degK</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TAREA</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-1dc3ff57-c6fe-40cb-ac5b-35b83d56310a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dc3ff57-c6fe-40cb-ac5b-35b83d56310a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ba49abf-5100-4e35-97fd-d6d962d90fd9' class='xr-var-data-in' type='checkbox'><label for='data-2ba49abf-5100-4e35-97fd-d6d962d90fd9' 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>long_name :</span></dt><dd>area of T cells</dd><dt><span>units :</span></dt><dd>centimeter^2</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-4d3b190d-1984-435a-aab0-729a0819949d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d3b190d-1984-435a-aab0-729a0819949d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-320f4fd1-22ee-427f-a15c-c6583e889199' class='xr-var-data-in' type='checkbox'><label for='data-320f4fd1-22ee-427f-a15c-c6583e889199' 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>long_name :</span></dt><dd>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLONG</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-e71ff3b8-3a15-4445-b9aa-47ebae580a3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e71ff3b8-3a15-4445-b9aa-47ebae580a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e26fe833-c3e8-42c3-b4ae-5718354d151e' class='xr-var-data-in' type='checkbox'><label for='data-e26fe833-c3e8-42c3-b4ae-5718354d151e' 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>long_name :</span></dt><dd>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UAREA</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-5baa9874-180a-4af0-8f73-c49791ab7435' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5baa9874-180a-4af0-8f73-c49791ab7435' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60a94e08-6607-4bae-9d8b-3270f165d26d' class='xr-var-data-in' type='checkbox'><label for='data-60a94e08-6607-4bae-9d8b-3270f165d26d' 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>long_name :</span></dt><dd>area of U cells</dd><dt><span>units :</span></dt><dd>centimeter^2</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YU</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-2b78a58b-a9da-4f6a-940f-c7fd171976f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b78a58b-a9da-4f6a-940f-c7fd171976f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc5c657b-ecf5-4adb-9fb8-74d24f0f4d11' class='xr-var-data-in' type='checkbox'><label for='data-bc5c657b-ecf5-4adb-9fb8-74d24f0f4d11' 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>long_name :</span></dt><dd>array of u-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XU</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-aaf3c45c-5f29-45bc-9824-ebcb03f4c30d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aaf3c45c-5f29-45bc-9824-ebcb03f4c30d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-421bb855-1438-49bc-abf0-78e464ed0450' class='xr-var-data-in' type='checkbox'><label for='data-421bb855-1438-49bc-abf0-78e464ed0450' 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>long_name :</span></dt><dd>array of u-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp_air</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-24c83a8e-dffa-461f-ae10-aed61abb8aee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24c83a8e-dffa-461f-ae10-aed61abb8aee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96e7e377-1df0-48c8-ae15-57e9ba7c1abb' class='xr-var-data-in' type='checkbox'><label for='data-96e7e377-1df0-48c8-ae15-57e9ba7c1abb' 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>long_name :</span></dt><dd>Heat Capacity of Air</dd><dt><span>units :</span></dt><dd>joule/kg/degK</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp_sw</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84107a69-f4a5-4c9a-8fa4-6d0d17a380c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84107a69-f4a5-4c9a-8fa4-6d0d17a380c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aef27a9e-11c1-4c41-b1fa-f342b080f4f9' class='xr-var-data-in' type='checkbox'><label for='data-aef27a9e-11c1-4c41-b1fa-f342b080f4f9' 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>long_name :</span></dt><dd>Specific Heat of Sea Water</dd><dt><span>units :</span></dt><dd>erg/g/K</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>days_in_norm_year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c000963-d8c3-424f-a105-44aeceb5f39a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c000963-d8c3-424f-a105-44aeceb5f39a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50428430-69b7-40fb-9502-40a2bb9a6e9b' class='xr-var-data-in' type='checkbox'><label for='data-50428430-69b7-40fb-9502-40a2bb9a6e9b' 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>long_name :</span></dt><dd>Calendar Length</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(62,), meta=np.ndarray&gt;</div><input id='attrs-1b58b2f1-1241-4cab-b480-2b97050e4537' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b58b2f1-1241-4cab-b480-2b97050e4537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-231b9562-38d4-48e0-bba1-16b10408f4f7' class='xr-var-data-in' type='checkbox'><label for='data-231b9562-38d4-48e0-bba1-16b10408f4f7' 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>long_name :</span></dt><dd>thickness of layer k</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 248 B </td> <td> 248 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (62,) </td> <td> (62,) </td></tr>\n",
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       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dzw</span></div><div class='xr-var-dims'>(z_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(62,), meta=np.ndarray&gt;</div><input id='attrs-63b1e286-2dfa-414a-8777-b2de4736c9aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63b1e286-2dfa-414a-8777-b2de4736c9aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4054362c-b7f7-449c-9a11-715cdc151b8e' class='xr-var-data-in' type='checkbox'><label for='data-4054362c-b7f7-449c-9a11-715cdc151b8e' 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>long_name :</span></dt><dd>midpoint of k to midpoint of k+1</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Bytes </th><td> 248 B </td> <td> 248 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (62,) </td> <td> (62,) </td></tr>\n",
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       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fwflux_factor</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-140453db-a9f3-4844-a6ce-b3615bd32299' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-140453db-a9f3-4844-a6ce-b3615bd32299' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f5a67f-7165-4ddb-8172-d59a71129ccf' class='xr-var-data-in' type='checkbox'><label for='data-30f5a67f-7165-4ddb-8172-d59a71129ccf' 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>long_name :</span></dt><dd>Convert Net Fresh Water Flux to Salt Flux (in model units)</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grav</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-958dc34d-79f4-4fd7-9670-7a82291e13dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-958dc34d-79f4-4fd7-9670-7a82291e13dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e97203-49b0-418f-8d0c-c339febd77cf' class='xr-var-data-in' type='checkbox'><label for='data-53e97203-49b0-418f-8d0c-c339febd77cf' 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>long_name :</span></dt><dd>Acceleration Due to Gravity</dd><dt><span>units :</span></dt><dd>centimeter/s^2</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heat_to_PW</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5df495f-8dca-47d5-8da3-f00190938f1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5df495f-8dca-47d5-8da3-f00190938f1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27a7c472-b5dc-450b-9182-a55b5eb42b7b' class='xr-var-data-in' type='checkbox'><label for='data-27a7c472-b5dc-450b-9182-a55b5eb42b7b' 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>long_name :</span></dt><dd>Convert Heat Flux to Petawatts</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hflux_factor</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d6a39be-67a7-4a08-b862-4cb24f9c0b09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d6a39be-67a7-4a08-b862-4cb24f9c0b09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d79b3513-f557-43cb-af04-821284f6660d' class='xr-var-data-in' type='checkbox'><label for='data-d79b3513-f557-43cb-af04-821284f6660d' 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>long_name :</span></dt><dd>Convert Heat and Solar Flux to Temperature Flux</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latent_heat_fusion</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a99b9764-a695-415b-b40a-f94526ee16ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a99b9764-a695-415b-b40a-f94526ee16ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac0121e1-1eb0-4ccc-a92b-4a3488dc1c05' class='xr-var-data-in' type='checkbox'><label for='data-ac0121e1-1eb0-4ccc-a92b-4a3488dc1c05' 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>long_name :</span></dt><dd>Latent Heat of Fusion</dd><dt><span>units :</span></dt><dd>erg/g</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latent_heat_vapor</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-016c76bb-5a41-47f9-91c7-69c95ad44072' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-016c76bb-5a41-47f9-91c7-69c95ad44072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b49f91c-dc38-4344-a109-11b24330e3fd' class='xr-var-data-in' type='checkbox'><label for='data-2b49f91c-dc38-4344-a109-11b24330e3fd' 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>long_name :</span></dt><dd>Latent Heat of Vaporization</dd><dt><span>units :</span></dt><dd>J/kg</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_to_Sv</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-63ab64e9-6ccc-490c-838f-550db53f8706' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63ab64e9-6ccc-490c-838f-550db53f8706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7fdaa0d-ee59-42b5-adf8-cb2845966c71' class='xr-var-data-in' type='checkbox'><label for='data-c7fdaa0d-ee59-42b5-adf8-cb2845966c71' 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>long_name :</span></dt><dd>Convert Mass Flux to Sverdrups</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>momentum_factor</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c3ba835-e1cb-4647-833f-8ff2fa9e6853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c3ba835-e1cb-4647-833f-8ff2fa9e6853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9a99827-22e6-4ba6-8d16-51481595bf02' class='xr-var-data-in' type='checkbox'><label for='data-b9a99827-22e6-4ba6-8d16-51481595bf02' 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>long_name :</span></dt><dd>Convert Windstress to Velocity Flux</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsurface_t</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6bc8cf59-3846-4745-8c0c-8d6611a30906' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bc8cf59-3846-4745-8c0c-8d6611a30906' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3431f41-a437-4749-be77-de59ad3bba32' class='xr-var-data-in' type='checkbox'><label for='data-b3431f41-a437-4749-be77-de59ad3bba32' 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>long_name :</span></dt><dd>Number of Ocean T Points at Surface</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsurface_u</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f64f0e92-c9ba-49cb-8765-8d4a11759875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f64f0e92-c9ba-49cb-8765-8d4a11759875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8ccb3d2-4e2f-43f3-ae21-50425f45e4de' class='xr-var-data-in' type='checkbox'><label for='data-d8ccb3d2-4e2f-43f3-ae21-50425f45e4de' 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>long_name :</span></dt><dd>Number of Ocean U Points at Surface</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocn_ref_salinity</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a2b63f3c-a8b5-4668-872c-0a00be938fd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2b63f3c-a8b5-4668-872c-0a00be938fd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d11ecfe3-5f4a-4ffb-8748-2274ec48fdd9' class='xr-var-data-in' type='checkbox'><label for='data-d11ecfe3-5f4a-4ffb-8748-2274ec48fdd9' 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>long_name :</span></dt><dd>Ocean Reference Salinity</dd><dt><span>units :</span></dt><dd>g/kg</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>omega</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9381d42-6b43-4da3-990d-4e987ab3ea3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9381d42-6b43-4da3-990d-4e987ab3ea3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01244b73-4787-45f9-a9c4-fdd32779b949' class='xr-var-data-in' type='checkbox'><label for='data-01244b73-4787-45f9-a9c4-fdd32779b949' 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>long_name :</span></dt><dd>Earths Angular Velocity</dd><dt><span>units :</span></dt><dd>1/second</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ppt_to_salt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1cf29b4-0d96-47ca-98ae-e92b9681d86f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1cf29b4-0d96-47ca-98ae-e92b9681d86f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-782315e4-818a-4890-b177-3285a61d2989' class='xr-var-data-in' type='checkbox'><label for='data-782315e4-818a-4890-b177-3285a61d2989' 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>long_name :</span></dt><dd>Convert Salt in g/kg to gram/gram</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radius</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13bb9161-2985-4e75-8d28-5a2726d2e2dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13bb9161-2985-4e75-8d28-5a2726d2e2dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1b46d58-9808-46cf-8219-d1a2cc4f123e' class='xr-var-data-in' type='checkbox'><label for='data-c1b46d58-9808-46cf-8219-d1a2cc4f123e' 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>long_name :</span></dt><dd>Earths Radius</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rho_air</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-53de8487-c285-44df-8e5a-f479f6961b7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53de8487-c285-44df-8e5a-f479f6961b7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48724a21-c3bd-41b7-bb50-33fc02e5c1c8' class='xr-var-data-in' type='checkbox'><label for='data-48724a21-c3bd-41b7-bb50-33fc02e5c1c8' 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>long_name :</span></dt><dd>Ambient Air Density</dd><dt><span>units :</span></dt><dd>kg/m^3</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rho_fw</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4cb05baa-5c88-4ef4-8259-026f232bb39b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cb05baa-5c88-4ef4-8259-026f232bb39b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dfa3f8a-9548-46e2-ab2f-5b0a1262774b' class='xr-var-data-in' type='checkbox'><label for='data-6dfa3f8a-9548-46e2-ab2f-5b0a1262774b' 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>long_name :</span></dt><dd>Density of Fresh Water</dd><dt><span>units :</span></dt><dd>gram/centimeter^3</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rho_sw</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-233d1caa-2909-41ba-9eee-10ccdb6dc61a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-233d1caa-2909-41ba-9eee-10ccdb6dc61a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f61b859f-b2db-49e9-a823-2bd81333fd48' class='xr-var-data-in' type='checkbox'><label for='data-f61b859f-b2db-49e9-a823-2bd81333fd48' 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>long_name :</span></dt><dd>Density of Sea Water</dd><dt><span>units :</span></dt><dd>gram/centimeter^3</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity_factor</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f9808291-f03e-400a-bc2a-c92497859e4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9808291-f03e-400a-bc2a-c92497859e4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-875cf271-4f27-4adf-8066-095cb0f9d047' class='xr-var-data-in' type='checkbox'><label for='data-875cf271-4f27-4adf-8066-095cb0f9d047' 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'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_to_Svppt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0dbd67e0-b93e-498d-8c20-c80873ea3e90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dbd67e0-b93e-498d-8c20-c80873ea3e90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e76fd2c6-ec2b-40ff-bf42-7d9021b73ed5' class='xr-var-data-in' type='checkbox'><label for='data-e76fd2c6-ec2b-40ff-bf42-7d9021b73ed5' 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>long_name :</span></dt><dd>Convert Salt Flux to Sverdrups*g/kg</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_to_mmday</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f34422df-001c-4e63-9e72-fd1c81a77eea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f34422df-001c-4e63-9e72-fd1c81a77eea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3823e50-4c3c-4d4a-a409-04a1ba906170' class='xr-var-data-in' type='checkbox'><label for='data-a3823e50-4c3c-4d4a-a409-04a1ba906170' 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>long_name :</span></dt><dd>Convert Salt to Water (millimeters/day)</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_to_ppt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af54ab08-81a1-4db1-a30f-47d59b68f631' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af54ab08-81a1-4db1-a30f-47d59b68f631' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9e1824a-df65-4f01-8b84-299b9e2ae5fc' class='xr-var-data-in' type='checkbox'><label for='data-a9e1824a-df65-4f01-8b84-299b9e2ae5fc' 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>long_name :</span></dt><dd>Convert Salt in gram/gram to g/kg</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_salinity</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9cd7cce-f4c6-451f-82d8-92f23d0e451e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9cd7cce-f4c6-451f-82d8-92f23d0e451e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c922595-2e37-4104-b073-a44fcb5937f3' class='xr-var-data-in' type='checkbox'><label for='data-2c922595-2e37-4104-b073-a44fcb5937f3' 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>long_name :</span></dt><dd>Salinity of Sea Ice</dd><dt><span>units :</span></dt><dd>g/kg</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sflux_factor</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-702c233b-6cb1-4644-a91f-c31560b5b206' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-702c233b-6cb1-4644-a91f-c31560b5b206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8b4b7ac-a436-48d6-8984-688fca1955ca' class='xr-var-data-in' type='checkbox'><label for='data-b8b4b7ac-a436-48d6-8984-688fca1955ca' 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>long_name :</span></dt><dd>Convert Salt Flux to Salt Flux (in model units)</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sound</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52ea98ad-0a7e-44f6-85a3-a323076d884b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52ea98ad-0a7e-44f6-85a3-a323076d884b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15e469da-9484-4d9e-9c71-6ed7ee17a8eb' class='xr-var-data-in' type='checkbox'><label for='data-15e469da-9484-4d9e-9c71-6ed7ee17a8eb' 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>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>units :</span></dt><dd>centimeter/s</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stefan_boltzmann</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b761b33-c023-4ce2-b92f-a338bed73b6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b761b33-c023-4ce2-b92f-a338bed73b6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44443847-823c-437c-8d11-457c70847ac1' class='xr-var-data-in' type='checkbox'><label for='data-44443847-823c-437c-8d11-457c70847ac1' 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>long_name :</span></dt><dd>Stefan-Boltzmann Constant</dd><dt><span>units :</span></dt><dd>watt/m^2/degK^4</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0046-01-02 00:00:00 ... 0087-01-...</div><input id='attrs-f123cf37-629e-48f1-a3f9-23b3a6fa1b0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f123cf37-629e-48f1-a3f9-23b3a6fa1b0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a78456cd-a1c1-401c-b299-4e9fb1464444' class='xr-var-data-in' type='checkbox'><label for='data-a78456cd-a1c1-401c-b299-4e9fb1464444' 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>bounds :</span></dt><dd>time_bound</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(46, 1, 2, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(46, 1, 3, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(46, 1, 4, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(86, 12, 30, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(86, 12, 31, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(87, 1, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vonkar</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-242409cf-8c6a-4f29-8752-67632f48fcaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-242409cf-8c6a-4f29-8752-67632f48fcaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1d71224-479c-4bf4-838c-a66872b3c2b5' class='xr-var-data-in' type='checkbox'><label for='data-e1d71224-479c-4bf4-838c-a66872b3c2b5' 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>long_name :</span></dt><dd>von Karman Constant</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><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'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.875e+05</div><input id='attrs-a0321c73-4062-46ae-a503-67667beb2550' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0321c73-4062-46ae-a503-67667beb2550' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0678453e-82bf-4fb8-9987-be69a8d83d38' class='xr-var-data-in' type='checkbox'><label for='data-0678453e-82bf-4fb8-9987-be69a8d83d38' 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>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>587499.0625</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05,\n",
       "       5.624991e+05, 5.874991e+05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-2b770d97-a248-4f51-aa0e-36e113f7ebc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b770d97-a248-4f51-aa0e-36e113f7ebc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-453e168f-13b0-4e30-99be-efd8e57df9b9' class='xr-var-data-in' type='checkbox'><label for='data-453e168f-13b0-4e30-99be-efd8e57df9b9' 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>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_w</span></div><div class='xr-var-dims'>(z_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1e+03 ... 5.5e+05 5.75e+05</div><input id='attrs-3bfe5a6d-b6ad-497e-bff2-6ce943b4842c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bfe5a6d-b6ad-497e-bff2-6ce943b4842c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38964008-1ed5-458a-ad4c-8053d1dd26d9' class='xr-var-data-in' type='checkbox'><label for='data-38964008-1ed5-458a-ad4c-8053d1dd26d9' 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>long_name :</span></dt><dd>depth from surface to top of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>574999.0625</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([     0.   ,   1000.   ,   2000.   ,   3000.   ,   4000.   ,   5000.   ,\n",
       "         6000.   ,   7000.   ,   8000.   ,   9000.   ,  10000.   ,  11000.   ,\n",
       "        12000.   ,  13000.   ,  14000.   ,  15000.   ,  16000.   ,  17019.682,\n",
       "        18076.129,  19182.125,  20349.932,  21592.344,  22923.312,  24358.453,\n",
       "        25915.58 ,  27615.26 ,  29481.47 ,  31542.373,  33831.227,  36387.473,\n",
       "        39258.047,  42498.887,  46176.656,  50370.688,  55174.91 ,  60699.668,\n",
       "        67072.86 ,  74439.805,  82960.695,  92804.35 , 104136.82 , 117104.016,\n",
       "       131809.36 , 148290.08 , 166499.2  , 186301.44 , 207487.39 , 229803.9  ,\n",
       "       252990.4  , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2  ,\n",
       "       400101.16 , 425052.47 , 450026.06 , 475012.   , 500004.7  , 525000.94 ,\n",
       "       549999.06 , 574999.06 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_w_bot</span></div><div class='xr-var-dims'>(z_w_bot)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+03 2e+03 ... 5.75e+05 6e+05</div><input id='attrs-4886cfa8-f899-4e1d-a2d6-350366754266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4886cfa8-f899-4e1d-a2d6-350366754266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68be464d-3eff-49dd-aa9c-991b81efe54f' class='xr-var-data-in' type='checkbox'><label for='data-68be464d-3eff-49dd-aa9c-991b81efe54f' 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>long_name :</span></dt><dd>depth from surface to bottom of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>599999.0625</dd><dt><span>valid_min :</span></dt><dd>1000.0</dd></dl></div><div class='xr-var-data'><pre>array([  1000.   ,   2000.   ,   3000.   ,   4000.   ,   5000.   ,   6000.   ,\n",
       "         7000.   ,   8000.   ,   9000.   ,  10000.   ,  11000.   ,  12000.   ,\n",
       "        13000.   ,  14000.   ,  15000.   ,  16000.   ,  17019.682,  18076.129,\n",
       "        19182.125,  20349.932,  21592.344,  22923.312,  24358.453,  25915.58 ,\n",
       "        27615.26 ,  29481.47 ,  31542.373,  33831.227,  36387.473,  39258.047,\n",
       "        42498.887,  46176.656,  50370.688,  55174.91 ,  60699.668,  67072.86 ,\n",
       "        74439.805,  82960.695,  92804.35 , 104136.82 , 117104.016, 131809.36 ,\n",
       "       148290.08 , 166499.2  , 186301.44 , 207487.39 , 229803.9  , 252990.4  ,\n",
       "       276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2  , 400101.16 ,\n",
       "       425052.47 , 450026.06 , 475012.   , 500004.7  , 525000.94 , 549999.06 ,\n",
       "       574999.06 , 599999.06 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_w_top</span></div><div class='xr-var-dims'>(z_w_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1e+03 ... 5.5e+05 5.75e+05</div><input id='attrs-bfbea0c6-98ee-43f7-9c3f-16205450a7d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfbea0c6-98ee-43f7-9c3f-16205450a7d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0bc60af-e807-4510-b6cf-ec7aa625c490' class='xr-var-data-in' type='checkbox'><label for='data-b0bc60af-e807-4510-b6cf-ec7aa625c490' 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>long_name :</span></dt><dd>depth from surface to top of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>574999.0625</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([     0.   ,   1000.   ,   2000.   ,   3000.   ,   4000.   ,   5000.   ,\n",
       "         6000.   ,   7000.   ,   8000.   ,   9000.   ,  10000.   ,  11000.   ,\n",
       "        12000.   ,  13000.   ,  14000.   ,  15000.   ,  16000.   ,  17019.682,\n",
       "        18076.129,  19182.125,  20349.932,  21592.344,  22923.312,  24358.453,\n",
       "        25915.58 ,  27615.26 ,  29481.47 ,  31542.373,  33831.227,  36387.473,\n",
       "        39258.047,  42498.887,  46176.656,  50370.688,  55174.91 ,  60699.668,\n",
       "        67072.86 ,  74439.805,  82960.695,  92804.35 , 104136.82 , 117104.016,\n",
       "       131809.36 , 148290.08 , 166499.2  , 186301.44 , 207487.39 , 229803.9  ,\n",
       "       252990.4  , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2  ,\n",
       "       400101.16 , 425052.47 , 450026.06 , 475012.   , 500004.7  , 525000.94 ,\n",
       "       549999.06 , 574999.06 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-64b0422b-f0d2-42bb-ac36-09f865a45ae5' class='xr-section-summary-in' type='checkbox'  ><label for='section-64b0422b-f0d2-42bb-ac36-09f865a45ae5' class='xr-section-summary' >Data variables: <span>(17)</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>HMXL_2</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-75491335-d3d8-40d5-8165-a88e187d77c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75491335-d3d8-40d5-8165-a88e187d77c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c23e105e-aa2b-48cb-8764-b567b7ad5fd8' class='xr-var-data-in' type='checkbox'><label for='data-c23e105e-aa2b-48cb-8764-b567b7ad5fd8' 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>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>long_name :</span></dt><dd>Mixed-Layer Depth</dd><dt><span>units :</span></dt><dd>centimeter</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-10d5bcf0-5545-4fc0-9a98-11b9ca88d4f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10d5bcf0-5545-4fc0-9a98-11b9ca88d4f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df4f8382-bdc7-4c3b-ae63-48e9db40affb' class='xr-var-data-in' type='checkbox'><label for='data-df4f8382-bdc7-4c3b-ae63-48e9db40affb' 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>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>2221</dd><dt><span>long_name :</span></dt><dd>Zonal Velocity lvls 1-1</dd><dt><span>units :</span></dt><dd>centimeter/s</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dy</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2400, 3600), meta=np.ndarray&gt;</div><input id='attrs-c028ebdc-2cc3-435e-a4ef-9a2a28a219ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c028ebdc-2cc3-435e-a4ef-9a2a28a219ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cdee117-469f-4110-a77d-f268b9bbd39b' class='xr-var-data-in' type='checkbox'><label for='data-7cdee117-469f-4110-a77d-f268b9bbd39b' 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'></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 69.12 MB </td> <td> 69.12 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2400, 3600) </td> <td> (2400, 3600) </td></tr>\n",
       "    <tr><th> Count </th><td> 9 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-94bbcf77-c3da-470d-aba2-644458879e96' class='xr-section-summary-in' type='checkbox'  ><label for='section-94bbcf77-c3da-470d-aba2-644458879e96' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-current.htm</dd><dt><span>calendar :</span></dt><dd>All years have exactly  365 days.</dd><dt><span>cell_methods :</span></dt><dd>cell_methods = time: mean ==&gt; the variable values are averaged over the time interval between the previous time coordinate and the current one.          cell_methods  absent  ==&gt; the variable values are at the time given by the current time coordinate.</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>nsteps_total :</span></dt><dd>13088337</dd><dt><span>revision :</span></dt><dd>$Id: tavg.F90 41939 2012-11-14 16:37:23Z mlevy@ucar.edu $</dd><dt><span>source :</span></dt><dd>CCSM POP2, the CCSM Ocean Component</dd><dt><span>start_time :</span></dt><dd>This dataset was created on 2013-03-09 at 12:25:55.7</dd><dt><span>tavg_sum :</span></dt><dd>86399.99999999962</dd><dt><span>title :</span></dt><dd>hybrid_v5_rel04_BC5_ne120_t12_pop62</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (nlat: 2400, nlon: 3600, time: 14965, z_t: 62, z_t_150m: 15, z_w: 62, z_w_bot: 62, z_w_top: 62)\n",
       "Coordinates:\n",
       "    ANGLE               (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    ANGLET              (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    DXT                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    DXU                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    DYT                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    DYU                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    HT                  (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    HTE                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    HTN                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    HU                  (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    HUS                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    HUW                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    KMT                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    KMU                 (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    REGION_MASK         (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    T0_Kelvin           float64 ...\n",
       "    TAREA               (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    TLAT                (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    TLONG               (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    UAREA               (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    YU                  (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    XU                  (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    cp_air              float64 ...\n",
       "    cp_sw               float64 ...\n",
       "    days_in_norm_year   timedelta64[ns] ...\n",
       "    dz                  (z_t) float32 dask.array<chunksize=(62,), meta=np.ndarray>\n",
       "    dzw                 (z_w) float32 dask.array<chunksize=(62,), meta=np.ndarray>\n",
       "    fwflux_factor       float64 ...\n",
       "    grav                float64 ...\n",
       "    heat_to_PW          float64 ...\n",
       "    hflux_factor        float64 ...\n",
       "    latent_heat_fusion  float64 ...\n",
       "    latent_heat_vapor   float64 ...\n",
       "    mass_to_Sv          float64 ...\n",
       "    momentum_factor     float64 ...\n",
       "    nsurface_t          float64 ...\n",
       "    nsurface_u          float64 ...\n",
       "    ocn_ref_salinity    float64 ...\n",
       "    omega               float64 ...\n",
       "    ppt_to_salt         float64 ...\n",
       "    radius              float64 ...\n",
       "    rho_air             float64 ...\n",
       "    rho_fw              float64 ...\n",
       "    rho_sw              float64 ...\n",
       "    salinity_factor     float64 ...\n",
       "    salt_to_Svppt       float64 ...\n",
       "    salt_to_mmday       float64 ...\n",
       "    salt_to_ppt         float64 ...\n",
       "    sea_ice_salinity    float64 ...\n",
       "    sflux_factor        float64 ...\n",
       "    sound               float64 ...\n",
       "    stefan_boltzmann    float64 ...\n",
       "  * time                (time) object 0046-01-02 00:00:00 ... 0087-01-01 00:0...\n",
       "    vonkar              float64 ...\n",
       "  * z_t                 (z_t) float32 500.0 1.5e+03 ... 5.625e+05 5.875e+05\n",
       "  * z_t_150m            (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
       "  * z_w                 (z_w) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n",
       "  * z_w_bot             (z_w_bot) float32 1e+03 2e+03 3e+03 ... 5.75e+05 6e+05\n",
       "  * z_w_top             (z_w_top) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables:\n",
       "    HMXL_2              (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    SFWF_2              (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    SHF_2               (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    SSH                 (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    SSS                 (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    SST                 (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    TAUX                (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    TAUY                (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    U                   (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    U2_2                (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    V                   (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    V2_2                (time, nlat, nlon) float32 dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>\n",
       "    X                   (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    Y                   (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    Z                   (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    dx                  (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "    dy                  (nlat, nlon) float64 dask.array<chunksize=(2400, 3600), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:   CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-curren...\n",
       "    calendar:      All years have exactly  365 days.\n",
       "    cell_methods:  cell_methods = time: mean ==> the variable values are aver...\n",
       "    contents:      Diagnostic and Prognostic Variables\n",
       "    history:       none\n",
       "    nsteps_total:  13088337\n",
       "    revision:      $Id: tavg.F90 41939 2012-11-14 16:37:23Z mlevy@ucar.edu $\n",
       "    source:        CCSM POP2, the CCSM Ocean Component\n",
       "    start_time:    This dataset was created on 2013-03-09 at 12:25:55.7\n",
       "    tavg_sum:      86399.99999999962\n",
       "    title:         hybrid_v5_rel04_BC5_ne120_t12_pop62"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ds.rename({'U1_1':'U', 'V1_1':'V', 'TAUX_2':'TAUX', 'TAUY_2':'TAUY', 'SSH_2':'SSH', 'ULONG':'XU', 'ULAT':'YU'})\n",
    "\n",
    "def make_3d_coords(lon,lat):\n",
    "    X = np.sin(np.radians(lat))\n",
    "    Y = np.sin(np.radians(lon))*np.cos(np.radians(lat))\n",
    "    Z = -np.cos(np.radians(lon))*np.cos(np.radians(lat))\n",
    "    return (X,Y,Z)\n",
    "\n",
    "X, Y, Z = make_3d_coords(ds.XU.data, ds.YU.data)\n",
    "\n",
    "ds['X'] = ds.XU.dims, X\n",
    "ds['Y'] = ds.XU.dims, Y\n",
    "ds['Z'] = ds.XU.dims, Z\n",
    "\n",
    "def normalize_gridspacing(DX,DY):\n",
    "    dX = (DX - np.mean(DX))/np.std(DX)\n",
    "    dY = (DY - np.mean(DY))/np.std(DY)\n",
    "    \n",
    "    return (dX, dY)\n",
    "\n",
    "dX, dY = normalize_gridspacing(ds.DXT.data, ds.DYT.data)\n",
    "\n",
    "ds['dx'] = ds.XU.dims, dX\n",
    "ds['dy'] = ds.XU.dims, dY\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "solved-xerox",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reshape_stencil(data, ssize):\n",
    "    assert data.ndim == 2\n",
    "    ny, nx = data.shape\n",
    "    data_rs = data.reshape((ny//ssize, ssize,  nx//ssize, ssize))\n",
    "    # move stencil to end\n",
    "    data_mv = np.moveaxis(data_rs, 1, 2)\n",
    "    data_flat = data_mv.reshape((-1, 2, 2))\n",
    "    return data_flat\n",
    "\n",
    "def extract_data(ds, vname, stencil_size, indexer):\n",
    "    data = ds[vname].values\n",
    "    data_rs = reshape_stencil(data, stencil_size)\n",
    "    data_idx = data_rs[(slice(None),) + indexer]\n",
    "    return data_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "built-burner",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def prepare_data_2D(ds, apply_mask=True, randomize=True,stencil_size = 2,\n",
    "                 stencil_vars = ['SSH', 'SST', 'X', 'Y', 'Z'], stencil_index = (slice(None), slice(None)),\n",
    "                 point_vars = ['TAUX', 'TAUY'], point_index = (0, 0), \n",
    "                 target_vars = ['U', 'V'], target_index = (0, 0) ):\n",
    "    \n",
    "    stencil_data = [extract_data(ds, vname, stencil_size, stencil_index)\n",
    "                    for vname in stencil_vars]\n",
    "    point_data = [extract_data(ds, vname, stencil_size, point_index)\n",
    "                   for vname in point_vars]\n",
    "    target_data = [extract_data(ds, vname, stencil_size, target_index)\n",
    "                   for vname in target_vars]\n",
    "\n",
    "    if apply_mask:\n",
    "        mask = np.isnan(stencil_data[0]).sum(axis=(1, 2)) == 0\n",
    "    else:\n",
    "        mask = slice(None)\n",
    "    if randomize:\n",
    "        random_idx = np.random.permutation(mask.sum())\n",
    "    else:\n",
    "        random_idx = slice(None)\n",
    "\n",
    "    features_stencil = np.stack([data[mask][random_idx] for data in stencil_data],\n",
    "                                axis=-1)\n",
    "    features_point = np.stack([data[mask][random_idx] for data in point_data],\n",
    "                               axis=-1)\n",
    "    targets = np.stack([data[mask][random_idx] for data in target_data],\n",
    "                        axis=-1)\n",
    "    \n",
    "    return [features_stencil, features_point], targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "animated-mixer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reshape_stencil(data, ssize):\n",
    "    assert data.ndim == 2\n",
    "    ny, nx = data.shape\n",
    "    data_rs = data.reshape((ny//ssize, ssize,  nx//ssize, ssize))\n",
    "    # move stencil to end\n",
    "    data_mv = np.moveaxis(data_rs, 1, 2)\n",
    "    data_flat = data_mv.reshape((-1, 2, 2))\n",
    "    return data_flat\n",
    "\n",
    "def reshape_stencil_time(data, ssize):\n",
    "    assert data.ndim == 3\n",
    "    nt, ny, nx = data.shape\n",
    "    data_rs = data.reshape((nt, ny//ssize, ssize,  nx//ssize, ssize))\n",
    "    # move stencil to end\n",
    "    data_mv = np.moveaxis(np.moveaxis(data_rs, 2, 3), 0,4)\n",
    "    data_flat = data_mv.reshape((-1, 2, 2, 3))\n",
    "    return data_flat\n",
    "\n",
    "def extract_data_loc(ds, vname, stencil_size, indexer):\n",
    "    data = ds[vname].values\n",
    "    data_rs = reshape_stencil(data, stencil_size)\n",
    "    data_idx = data_rs[(slice(None),) + indexer]\n",
    "    return data_idx\n",
    "\n",
    "def extract_data_time(ds, vname, stencil_size, indexer):\n",
    "    data = ds[vname].values\n",
    "    data_rs = reshape_stencil_time(data, stencil_size)\n",
    "    data_idx = data_rs[(slice(None),) + indexer]\n",
    "    return data_idx\n",
    "\n",
    "def prepare_data_3dpointloc(ds, apply_mask=True, randomize=True, stencil_size = 2,\n",
    "                            stencil_time_vars = ['SSH',], stencil_time_index = (slice(None), slice(None), slice(None)),\n",
    "                            point_vars = ['TAUX', 'TAUY'], point_index = (1, 0, 0), \n",
    "                            loc_vars = ['X','dx','dy'], loc_index = (0, 0), \n",
    "                            target_vars = ['U', 'V'], target_index = (1, 0, 0) ):\n",
    "    \n",
    "    stencil_time_data = [extract_data_time(ds, vname, stencil_size, stencil_time_index)\n",
    "                    for vname in stencil_time_vars]    \n",
    "    loc_data = [extract_data_loc(ds, vname, stencil_size, loc_index)\n",
    "                    for vname in loc_vars]\n",
    "    point_data = [extract_data_time(ds, vname, stencil_size, point_index)\n",
    "                   for vname in point_vars]\n",
    "    target_data = [extract_data_time(ds, vname, stencil_size, target_index)\n",
    "                   for vname in target_vars]\n",
    "\n",
    "    if apply_mask:\n",
    "        mask = np.isnan(stencil_time_data[0]).sum(axis=(1, 2, 3)) == 0\n",
    "    else:\n",
    "        mask = slice(None)\n",
    "    if randomize:\n",
    "        random_idx = np.random.permutation(mask.sum())\n",
    "    else:\n",
    "        random_idx = slice(None)\n",
    "\n",
    "    features_stencil_time = np.stack([data[mask][random_idx] for data in stencil_time_data],\n",
    "                                axis=-1)\n",
    "    features_loc = np.stack([data[mask][random_idx] for data in loc_data],\n",
    "                                axis=-1)\n",
    "    \n",
    "    features_point = np.stack([data[mask][random_idx] for data in point_data],\n",
    "                               axis=-1)\n",
    "    targets = np.stack([data[mask][random_idx] for data in target_data],\n",
    "                        axis=-1)\n",
    "    return [features_stencil_time, features_point, features_loc], targets\n",
    "\n",
    "\n",
    "def prepare_data_3dloc(ds, apply_mask=True, randomize=True, stencil_size = 2,\n",
    "                            stencil_time_vars = ['SSH',], stencil_time_index = (slice(None), slice(None), slice(None)),\n",
    "                            loc_vars = ['X','dx','dy'], loc_index = (0, 0), \n",
    "                            target_vars = ['U', 'V'], target_index = (1, 0, 0) ):\n",
    "    \n",
    "    stencil_time_data = [extract_data_time(ds, vname, stencil_size, stencil_time_index)\n",
    "                    for vname in stencil_time_vars]    \n",
    "    loc_data = [extract_data_loc(ds, vname, stencil_size, loc_index)\n",
    "                    for vname in loc_vars]\n",
    "    target_data = [extract_data_time(ds, vname, stencil_size, target_index)\n",
    "                   for vname in target_vars]\n",
    "\n",
    "    if apply_mask:\n",
    "        mask = np.isnan(stencil_time_data[0]).sum(axis=(1, 2, 3)) == 0\n",
    "    else:\n",
    "        mask = slice(None)\n",
    "    if randomize:\n",
    "        random_idx = np.random.permutation(mask.sum())\n",
    "    else:\n",
    "        random_idx = slice(None)\n",
    "\n",
    "    features_stencil_time = np.stack([data[mask][random_idx] for data in stencil_time_data],\n",
    "                                axis=-1)\n",
    "    features_loc = np.stack([data[mask][random_idx] for data in loc_data],\n",
    "                                axis=-1)\n",
    "    targets = np.stack([data[mask][random_idx] for data in target_data],\n",
    "                        axis=-1)\n",
    "    return [features_stencil_time, features_loc], targets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "configured-newton",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = {}\n",
    "\n",
    "labels['00'] = 'SSH_2ps_pointTAUXTAUY_3l_402010_nf80'\n",
    "labels['01'] = 'SSH_2ps_pointX_3l_402010_nf80'\n",
    "labels['02'] = 'SSHSST_2ps_pointX_3l_402010_nf80'\n",
    "labels['03'] = 'SSH_2ps_pointXTAUXTAUY_3l_402010_nf80'\n",
    "labels['04'] = 'SSHSST_2ps_pointXTAUXTAUY_3l_402010_nf80'\n",
    "labels['05'] = 'SSHXYZ_2ps_pointTAUXTAUY_3l_402010_nf80'\n",
    "labels['06'] = 'SSHX_2ps_pointTAUXTAUY_3l_402010_nf80'\n",
    "labels['07'] = 'SSHSSTXYZ_2ps_pointTAUXTAUY_3l_402010_nf80'\n",
    "labels['08'] = 'SSHSSTX_2ps_pointTAUXTAUY_3l_402010_nf80'\n",
    "labels['09'] = 'SSHTAUXTAUY_2ps_pointX_3l_402010_nf80'\n",
    "labels['10'] = 'SSHSSTTAUXTAUY_2ps_pointX_3l_402010_nf80'\n",
    "labels['11'] = 'SSHSST_2ps_pointXdxdyTAUXTAUY_3l_402010_nf80'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "extreme-constitution",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2d_keys = ['00','01','02','03','04','05','06','07','08','09','10','11']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "artistic-garden",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels['12'] = 'SSHSSTSSS_2ps_pointXdxdyTAUXTAUY_3l_402010_nf80'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "executed-vertex",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = {}\n",
    "history = {}\n",
    "for r in m2d_keys:\n",
    "    model[r] = keras.models.load_model('models/' + labels[r])\n",
    "    history[r] = np.load('models/history_%s.npz' %labels[r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "serious-midnight",
   "metadata": {},
   "outputs": [],
   "source": [
    "for r in ['12']:\n",
    "    model[r] = keras.models.load_model('models/' + labels[r])\n",
    "    history[r] = np.load('models/history_%s.npz' %labels[r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "certain-chemical",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_pred = {}\n",
    "targets_true = {}\n",
    "features_test = {}\n",
    "targets_test = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "touched-filter",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_pred = {}\n",
    "targets_true = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "helpful-contribution",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.1 s, sys: 6.27 s, total: 25.4 s\n",
      "Wall time: 29.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\n",
    "features_test['00'], targets_test['00'] = prepare_data_2D(ds.isel(time=365*10+70), stencil_vars = ['SSH',], point_vars=['TAUX','TAUY',],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['01'], targets_test['01'] = prepare_data_2D(ds.isel(time=365*10+70), stencil_vars = ['SSH',], point_vars=['X',],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['02'], targets_test['02'] = prepare_data_2D(ds.isel(time=365*10+70), stencil_vars = ['SSH','SST',], point_vars=['X',],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['03'], targets_test['03'] = prepare_data_2D(ds.isel(time=365*10+70), stencil_vars = ['SSH',], point_vars=['X','TAUX','TAUY',],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['04'], targets_test['04'] = prepare_data_2D(ds.isel(time=365*10+70), stencil_vars = ['SSH','SST',], point_vars=['X','TAUX','TAUY'],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['05'], targets_test['05'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','X','Y','Z'], point_vars=['TAUX','TAUY',],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['06'], targets_test['06'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','X',], point_vars=['TAUX','TAUY',],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['07'], targets_test['07'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','SST','X','Y','Z',], point_vars=['TAUX','TAUY'],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['08'], targets_test['08'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','SST','X'], point_vars=['TAUX','TAUY'],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['09'], targets_test['09'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','TAUX','TAUY',], point_vars=['X',],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['10'], targets_test['10'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','SST','TAUX','TAUY',], point_vars=['X'],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_test['11'], targets_test['11'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','SST',], point_vars=['X','dx','dy','TAUX','TAUY'],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "divine-theology",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test['12'], targets_test['12'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','SST','SSS'], point_vars=['X','dx','dy','TAUX','TAUY'],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "decimal-colleague",
   "metadata": {},
   "outputs": [],
   "source": [
    "meval = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "durable-court",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41967/41967 [==============================] - 52s 1ms/step - loss: 6.9734 - mean_absolute_error: 6.9734 - mean_squared_error: 220.0631 - accuracy: 0.7365\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 4.9167 - mean_absolute_error: 4.9167 - mean_squared_error: 57.0516 - accuracy: 0.8070\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 5.0111 - mean_absolute_error: 5.0111 - mean_squared_error: 65.4762 - accuracy: 0.7991\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.1116 - mean_absolute_error: 3.1116 - mean_squared_error: 35.1327 - accuracy: 0.9048\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 3.5714 - mean_absolute_error: 3.5714 - mean_squared_error: 40.7044 - accuracy: 0.8936\n",
      "41967/41967 [==============================] - 51s 1ms/step - loss: 3.2915 - mean_absolute_error: 3.2915 - mean_squared_error: 36.1079 - accuracy: 0.8626\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.5203 - mean_absolute_error: 3.5203 - mean_squared_error: 40.7876 - accuracy: 0.8537\n",
      "41967/41967 [==============================] - 53s 1ms/step - loss: 3.1757 - mean_absolute_error: 3.1757 - mean_squared_error: 38.7120 - accuracy: 0.9014\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.9767 - mean_absolute_error: 3.9767 - mean_squared_error: 43.3691 - accuracy: 0.8237\n",
      "41967/41967 [==============================] - 53s 1ms/step - loss: 3.3035 - mean_absolute_error: 3.3035 - mean_squared_error: 35.2817 - accuracy: 0.8696\n",
      "41967/41967 [==============================] - 53s 1ms/step - loss: 3.5088 - mean_absolute_error: 3.5088 - mean_squared_error: 36.3106 - accuracy: 0.8697\n",
      "41967/41967 [==============================] - 51s 1ms/step - loss: 3.4967 - mean_absolute_error: 3.4967 - mean_squared_error: 36.2822 - accuracy: 0.8989\n"
     ]
    }
   ],
   "source": [
    "for mnum in m2d_keys:\n",
    "    meval[mnum] = model[mnum].evaluate(features_test[mnum], targets_test[mnum])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "perceived-strength",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "present-little",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "downtown-shelter",
   "metadata": {},
   "outputs": [],
   "source": [
    "# targets_pred = {}\n",
    "# for mnum in model.keys():\n",
    "#     start = timer()\n",
    "#     targets_pred[mnum] = model[mnum].predict(features_pred[mnum])\n",
    "#     end = timer()\n",
    "#     print(mnum, end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moral-mongolia",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for mnum in model2.keys():\n",
    "#     start = timer()\n",
    "#     targets_pred[mnum] = model2[mnum].predict(features_pred[mnum])\n",
    "#     end = timer()\n",
    "#     print(mnum, end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "subjective-patio",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mae2 = np.array([7.7946, 5.1257, 5.1260, 3.1067, 3.2977, 3.0840, 3.2387, 3.3040, 3.7069, 3.6339, 3.2265, 4.0232])\n",
    "# mse2 = np.array([233.0203, 63.3984, 65.9020, 39.5688, 36.4640, 34.4702, 39.0651, 37.6900, 41.3248, 40.4397, 36.7599, 46.0133 ])\n",
    "# acc2 = np.array([0.6970, 0.7765, 0.7963, 0.9063, 0.8699, 0.8924, 0.9052, 0.8779, 0.8557, 0.8388, 0.8937, 0.9123 ])\n",
    "\n",
    "# mae3 = np.array([4.6787, 4.4783, 3.9713, 4.2278, 5.3598, 5.6873, 5.6739, 5.5928, 4.3509, 4.4022,])\n",
    "# mse3 = np.array([56.4924, 51.5324, 44.7217, 53.1741, 66.6272, 77.8924, 72.7270, 72.9671, 52.9664, 54.0769])\n",
    "# acc3 = np.array([0.8721, 0.8174, 0.8646, 0.8621, 0.7836, 0.7935, 0.7870, 0.8016, 0.8336, 0.8570])\n",
    "\n",
    "\n",
    "loss_ev = {}\n",
    "mse_ev = {}\n",
    "acc_ev = {}\n",
    "for r in meval.keys():\n",
    "    loss_ev[r] = meval[r][0]\n",
    "    mse_ev[r] = meval[r][2]\n",
    "    acc_ev[r] = meval[r][-1]\n",
    "# for i,r in enumerate(m2d_keys):\n",
    "#     loss_ev[r] = mae2[i]\n",
    "#     mse_ev[r] = mse2[i]\n",
    "#     acc_ev[r] = acc2[i]\n",
    "    \n",
    "# for j,r in enumerate(m3d_keys):\n",
    "#     loss_ev[r] = mae3[j]\n",
    "#     mse_ev[r] = mse3[j]\n",
    "#     acc_ev[r] = acc3[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "continental-things",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {'00': 'rosybrown', \n",
    "          '01' : 'royalblue', \n",
    "          '02' : 'mediumblue', \n",
    "          '03' : 'midnightblue', \n",
    "          '04' : 'slateblue', \n",
    "          '05' : 'mediumorchid', \n",
    "          '06': 'violet', \n",
    "          '07' : 'mediumvioletred', \n",
    "          '08' : 'crimson', \n",
    "          '09' : 'firebrick', \n",
    "          '10' : 'coral', \n",
    "          '11' : 'chocolate',\n",
    "          \n",
    "         }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dynamic-fighter",
   "metadata": {},
   "outputs": [],
   "source": [
    "# history_all = {}\n",
    "# for m2 in m2d_keys:\n",
    "#     history_all[m2] = history[m2]\n",
    "# for m3 in m3d_keys:\n",
    "#     history_all[m3] = history2[m3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "armed-husband",
   "metadata": {},
   "outputs": [],
   "source": [
    "plabels = {'00' : r'$\\eta$ (2s), $\\tau_x, \\tau_y$ (point)', \n",
    "           '01' : r'$\\eta$ (2s), $X$ (point)', \n",
    "           '02' : r'$\\eta, \\theta$ (2s), $X$ (point)', \n",
    "           '03' : r'$\\eta$ (2s), $X, \\tau_x, \\tau_y$ (point)', \n",
    "           '04' : r'$\\eta, \\theta$ (2s), $X, \\tau_x, \\tau_y$ (point)', \n",
    "           '05' : r'$\\eta, X, Y, Z$ (2s), $\\tau_x, \\tau_y$ (point)', \n",
    "           '06' : r'$\\eta, X$ (2s), $\\tau_x, \\tau_y$ (point)', \n",
    "           '07' : r'$\\eta, \\theta, X, Y, Z$ (2s), $\\tau_x, \\tau_y$ (point)', \n",
    "           '08' : r'$\\eta, \\theta, X$ (2s), $\\tau_x, \\tau_y$ (point)', \n",
    "           '09' : r'$\\eta, \\tau_x, \\tau_y$ (2s), $X$ (point)', \n",
    "           '10' : r'$\\eta, \\theta, \\tau_x, \\tau_y$ (2s), $X$ (point)', \n",
    "           '11' : r'$\\eta, \\theta$ (2s), $X, dx, dy, \\tau_x, \\tau_y$ (point)',  \n",
    "#            '000' : r'$\\eta$ (3t-2s), $X, dx, dy, \\tau_x, \\tau_y$ (point)', \n",
    "#            '001' : r'$\\eta, \\theta$ (3t-2s), $X, dx, dy, \\tau_x, \\tau_y$ (point)', \n",
    "#            '002' : r'$\\eta$ (3t-2s), $X, \\tau_x, \\tau_y$ (point)', \n",
    "#            '003' : r'$\\eta, \\theta$ (3t-2s), $X, \\tau_x, \\tau_y$ (point)', \n",
    "#            '004' : r'$\\eta$ (3t-2s), $X, dx, dy$ (point)', \n",
    "#            '005' : r'$\\eta, \\theta$ (3t-2s), $X, dx, dy$ (point)', \n",
    "#            '006' : r'$\\eta$ (3t-2s), $X$ (point)', \n",
    "#            '007' : r'$\\eta, \\theta$ (3t-2s), $X$ (point)', \n",
    "#            '008' : r'$\\eta, \\theta, S$ (3t-2s), $X, \\tau_x, \\tau_y$ (point)', \n",
    "#            '009' : r'$\\eta, \\theta, S$ (3t-2s), $X, dx, dy, \\tau_x, \\tau_y$ (point)',\n",
    "          }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bigger-funds",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "therapeutic-girlfriend",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "confused-oxide",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test['11'], targets_test['11'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','SST',], point_vars=['X','dx','dy','TAUX','TAUY'],\n",
    "                                                          apply_mask=True, randomize=False,)\n",
    "features_pred['11'], targets_true['11'] = prepare_data_2D(ds.isel(time=365*10+70), \n",
    "                                                          stencil_vars = ['SSH','SST',], point_vars=['X','dx','dy','TAUX','TAUY'],\n",
    "                                                          apply_mask=False, randomize=False,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "victorian-safety",
   "metadata": {},
   "outputs": [],
   "source": [
    "dvar = {}\n",
    "dvar['11_deta1s'] = 1*np.std(features_test['11'][0][:,:,:,0])\n",
    "dvar['11_deta2s'] = 2*np.std(features_test['11'][0][:,:,:,0])\n",
    "dvar['11_deta05s'] = 0.5*np.std(features_test['11'][0][:,:,:,0])\n",
    "\n",
    "dvar['11_dT1s'] = 1*np.std(features_test['11'][0][:,:,:,1])\n",
    "dvar['11_dT2s'] = 2*np.std(features_test['11'][0][:,:,:,1])\n",
    "dvar['11_dT05s'] = 0.5*np.std(features_test['11'][0][:,:,:,1])\n",
    "\n",
    "dvar['11_dX1s'] = 1*np.std(features_test['11'][1][:,0])\n",
    "dvar['11_dX2s'] = 2*np.std(features_test['11'][1][:,0])\n",
    "dvar['11_dX05s'] = 0.5*np.std(features_test['11'][1][:,0])\n",
    "\n",
    "dvar['11_ddx1s'] = 1*np.std(features_test['11'][1][:,1])\n",
    "dvar['11_ddx2s'] = 2*np.std(features_test['11'][1][:,1])\n",
    "dvar['11_ddx05s'] = 0.5*np.std(features_test['11'][1][:,1])\n",
    "\n",
    "dvar['11_ddy1s'] = 1*np.std(features_test['11'][1][:,2])\n",
    "dvar['11_ddy2s'] = 2*np.std(features_test['11'][1][:,2])\n",
    "dvar['11_ddy05s'] = 0.5*np.std(features_test['11'][1][:,2])\n",
    "\n",
    "dvar['11_dTAUX1s'] = 1*np.std(features_test['11'][1][:,3])\n",
    "dvar['11_dTAUX2s'] = 2*np.std(features_test['11'][1][:,3])\n",
    "dvar['11_dTAUX05s'] = 0.5*np.std(features_test['11'][1][:,3])\n",
    "\n",
    "dvar['11_dTAUY1s'] = 1*np.std(features_test['11'][1][:,4])\n",
    "dvar['11_dTAUY2s'] = 2*np.std(features_test['11'][1][:,4])\n",
    "dvar['11_dTAUY05s'] = 0.5*np.std(features_test['11'][1][:,4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "floral-briefs",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.81 s, sys: 935 ms, total: 3.75 s\n",
      "Wall time: 3.75 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "features_test['11_deta1s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_deta2s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_deta05s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dT1s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dT2s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dT05s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dX1s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dX2s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dX05s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dTAUX1s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dTAUX2s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dTAUX05s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dTAUY1s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dTAUY2s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_dTAUY05s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_ddx1s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_ddx2s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_ddx05s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_ddy1s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_ddy2s'] = copy.deepcopy(features_test['11'])\n",
    "features_test['11_ddy05s'] = copy.deepcopy(features_test['11'])\n",
    "\n",
    "features_test['11_deta1s'][0][:,:,:,0] = features_test['11'][0][:,:,:,0] + np.random.normal(0,1*np.std(features_test['11'][0][:,:,:,0]),features_test['11'][0][:,:,:,0].shape)\n",
    "features_test['11_deta2s'][0][:,:,:,0] = features_test['11'][0][:,:,:,0] + np.random.normal(0,2*np.std(features_test['11'][0][:,:,:,0]),features_test['11'][0][:,:,:,0].shape)\n",
    "features_test['11_deta05s'][0][:,:,:,0] = features_test['11'][0][:,:,:,0] + np.random.normal(0,0.5*np.std(features_test['11'][0][:,:,:,0]),features_test['11'][0][:,:,:,0].shape)\n",
    "features_test['11_dT1s'][0][:,:,:,1] = features_test['11'][0][:,:,:,1]  + np.random.normal(0,1*np.std(features_test['11'][0][:,:,:,1]),features_test['11'][0][:,:,:,1].shape)\n",
    "features_test['11_dT2s'][0][:,:,:,1] = features_test['11'][0][:,:,:,1]  + np.random.normal(0,2*np.std(features_test['11'][0][:,:,:,1]),features_test['11'][0][:,:,:,1].shape)\n",
    "features_test['11_dT05s'][0][:,:,:,1] = features_test['11'][0][:,:,:,1]  + np.random.normal(0,0.5*np.std(features_test['11'][0][:,:,:,1]),features_test['11'][0][:,:,:,1].shape)\n",
    "\n",
    "\n",
    "features_test['11_dX1s'][1][:,0] = features_test['11'][1][:,0] + np.random.normal(0,1*np.std(features_test['11'][1][:,0]),features_test['11'][1][:,0].shape)\n",
    "features_test['11_dX2s'][1][:,0] = features_test['11'][1][:,0] + np.random.normal(0,2*np.std(features_test['11'][1][:,0]),features_test['11'][1][:,0].shape)\n",
    "features_test['11_dX05s'][1][:,0] = features_test['11'][1][:,0] + np.random.normal(0,0.5*np.std(features_test['11'][1][:,0]),features_test['11'][1][:,0].shape)\n",
    "\n",
    "features_test['11_ddx1s'][1][:,1] = features_test['11'][1][:,1] + np.random.normal(0,1*np.std(features_test['11'][1][:,1]),features_test['11'][1][:,1].shape)\n",
    "features_test['11_ddx2s'][1][:,1] = features_test['11'][1][:,1] +  np.random.normal(0,2*np.std(features_test['11'][1][:,1]),features_test['11'][1][:,1].shape)\n",
    "features_test['11_ddx05s'][1][:,1] = features_test['11'][1][:,1] + np.random.normal(0,0.5*np.std(features_test['11'][1][:,1]),features_test['11'][1][:,1].shape)\n",
    "\n",
    "features_test['11_ddy1s'][1][:,2] = features_test['11'][1][:,2] + np.random.normal(0,1*np.std(features_test['11'][1][:,2]),features_test['11'][1][:,2].shape)\n",
    "features_test['11_ddy2s'][1][:,2] = features_test['11'][1][:,2] + np.random.normal(0,2*np.std(features_test['11'][1][:,2]),features_test['11'][1][:,2].shape)\n",
    "features_test['11_ddy05s'][1][:,2] = features_test['11'][1][:,2] + np.random.normal(0,0.5*np.std(features_test['11'][1][:,2]),features_test['11'][1][:,2].shape)\n",
    "\n",
    "features_test['11_dTAUX1s'][1][:,3] = features_test['11'][1][:,3] + np.random.normal(0,1*np.std(features_test['11'][1][:,3]),features_test['11'][1][:,3].shape)\n",
    "features_test['11_dTAUX2s'][1][:,3] = features_test['11'][1][:,3] + np.random.normal(0,2*np.std(features_test['11'][1][:,3]),features_test['11'][1][:,3].shape)\n",
    "features_test['11_dTAUX05s'][1][:,3] = features_test['11'][1][:,3] + np.random.normal(0,0.5*np.std(features_test['11'][1][:,3]),features_test['11'][1][:,3].shape)\n",
    "\n",
    "features_test['11_dTAUY1s'][1][:,4] = features_test['11'][1][:,4] + np.random.normal(0,1*np.std(features_test['11'][1][:,4]),features_test['11'][1][:,4].shape)\n",
    "features_test['11_dTAUY2s'][1][:,4] = features_test['11'][1][:,4] + np.random.normal(0,2*np.std(features_test['11'][1][:,4]),features_test['11'][1][:,4].shape)\n",
    "features_test['11_dTAUY05s'][1][:,4] = features_test['11'][1][:,4] + np.random.normal(0,0.5*np.std(features_test['11'][1][:,4]),features_test['11'][1][:,4].shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "valid-fitting",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2d_dsigma_keys = ['11_deta1s', '11_deta2s', '11_deta05s', \n",
    "                   '11_dT1s', '11_dT2s', '11_dT05s', \n",
    "                   '11_dX1s', '11_dX2s', '11_dX05s', \n",
    "                   '11_ddx1s', '11_ddx2s', '11_ddx05s', \n",
    "                   '11_ddy1s', '11_ddy2s', '11_ddy05s', \n",
    "                   '11_dTAUX1s', '11_dTAUX2s', '11_dTAUX05s', \n",
    "                   '11_dTAUY1s', '11_dTAUY2s', '11_dTAUY05s', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "spread-symbol",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['11_deta1s',\n",
       " '11_deta2s',\n",
       " '11_deta05s',\n",
       " '11_dT1s',\n",
       " '11_dT2s',\n",
       " '11_dT05s',\n",
       " '11_dX1s',\n",
       " '11_dX2s',\n",
       " '11_dX05s',\n",
       " '11_ddx1s',\n",
       " '11_ddx2s',\n",
       " '11_ddx05s',\n",
       " '11_ddy1s',\n",
       " '11_ddy2s',\n",
       " '11_ddy05s',\n",
       " '11_dTAUX1s',\n",
       " '11_dTAUX2s',\n",
       " '11_dTAUX05s',\n",
       " '11_dTAUY1s',\n",
       " '11_dTAUY2s',\n",
       " '11_dTAUY05s']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2d_dsigma_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "activated-platinum",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41967/41967 [==============================] - 52s 1ms/step - loss: 397.3065 - mean_absolute_error: 397.3065 - mean_squared_error: 267283.2812 - accuracy: 0.5002\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 765.1913 - mean_absolute_error: 765.1913 - mean_squared_error: 990004.1250 - accuracy: 0.4998\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 227.9978 - mean_absolute_error: 227.9978 - mean_squared_error: 85277.2266 - accuracy: 0.5024\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 15.1151 - mean_absolute_error: 15.1151 - mean_squared_error: 405.4606 - accuracy: 0.6218\n",
      "41967/41967 [==============================] - 53s 1ms/step - loss: 26.0387 - mean_absolute_error: 26.0387 - mean_squared_error: 1148.8468 - accuracy: 0.5672\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 9.0537 - mean_absolute_error: 9.0537 - mean_squared_error: 161.0740 - accuracy: 0.6968\n",
      "41967/41967 [==============================] - 53s 1ms/step - loss: 7.6363 - mean_absolute_error: 7.6363 - mean_squared_error: 226.5975 - accuracy: 0.7447\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 10.1301 - mean_absolute_error: 10.1301 - mean_squared_error: 331.6674 - accuracy: 0.6379\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 5.6541 - mean_absolute_error: 5.6541 - mean_squared_error: 136.3901 - accuracy: 0.8364\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.8188 - mean_absolute_error: 3.8188 - mean_squared_error: 43.0090 - accuracy: 0.8867\n",
      "41967/41967 [==============================] - 53s 1ms/step - loss: 4.4799 - mean_absolute_error: 4.4799 - mean_squared_error: 60.7203 - accuracy: 0.8669\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.5880 - mean_absolute_error: 3.5880 - mean_squared_error: 37.9867 - accuracy: 0.8954\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.8448 - mean_absolute_error: 3.8448 - mean_squared_error: 47.4520 - accuracy: 0.8936\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 4.4330 - mean_absolute_error: 4.4330 - mean_squared_error: 68.9429 - accuracy: 0.8824\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.6059 - mean_absolute_error: 3.6059 - mean_squared_error: 39.6045 - accuracy: 0.8976\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 5.4329 - mean_absolute_error: 5.4329 - mean_squared_error: 85.2654 - accuracy: 0.8319\n",
      "41967/41967 [==============================] - 54s 1ms/step - loss: 7.9769 - mean_absolute_error: 7.9769 - mean_squared_error: 173.0767 - accuracy: 0.7787\n",
      "41967/41967 [==============================] - 55s 1ms/step - loss: 4.2321 - mean_absolute_error: 4.2321 - mean_squared_error: 53.0037 - accuracy: 0.8682\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 4.9170 - mean_absolute_error: 4.9170 - mean_squared_error: 60.1492 - accuracy: 0.8217\n",
      "41967/41967 [==============================] - 53s 1ms/step - loss: 6.8537 - mean_absolute_error: 6.8537 - mean_squared_error: 106.3389 - accuracy: 0.7679\n",
      "41967/41967 [==============================] - 52s 1ms/step - loss: 4.0175 - mean_absolute_error: 4.0175 - mean_squared_error: 43.4938 - accuracy: 0.8632\n"
     ]
    }
   ],
   "source": [
    "mev_dsigma = {}\n",
    "\n",
    "for mnum in m2d_dsigma_keys:\n",
    "    mev_dsigma[mnum] = model['11'].evaluate(features_test[mnum], targets_test['11'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "moving-integrity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41967/41967 [==============================] - 52s 1ms/step - loss: 3.4967 - mean_absolute_error: 3.4967 - mean_squared_error: 36.2822 - accuracy: 0.8989\n"
     ]
    }
   ],
   "source": [
    "mev_dsigma['11'] = model['11'].evaluate(features_test['11'], targets_test['11'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "quantitative-approach",
   "metadata": {},
   "outputs": [],
   "source": [
    "mev_ds_mae = {}\n",
    "mev_ds_mse = {}\n",
    "mev_ds_acc = {}\n",
    "for r in mev_dsigma.keys():\n",
    "    mev_ds_mae[r] = mev_dsigma[r][0]\n",
    "    mev_ds_mse[r] = mev_dsigma[r][2]\n",
    "    mev_ds_acc[r] = mev_dsigma[r][-1]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "concrete-demographic",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test['12_deta1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_deta2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_deta05s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dT1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dT2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dT05s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dS1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dS2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dS05s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dX1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dX2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dX05s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dTAUX1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dTAUX2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dTAUX05s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dTAUY1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dTAUY2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_dTAUY05s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_ddx1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_ddx2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_ddx05s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_ddy1s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_ddy2s'] = copy.deepcopy(features_test['12'])\n",
    "features_test['12_ddy05s'] = copy.deepcopy(features_test['12'])\n",
    "\n",
    "features_test['12_deta1s'][0][:,:,:,0] = features_test['12'][0][:,:,:,0] + np.random.normal(0,1*np.std(features_test['12'][0][:,:,:,0]),features_test['12'][0][:,:,:,0].shape)\n",
    "features_test['12_deta2s'][0][:,:,:,0] = features_test['12'][0][:,:,:,0] + np.random.normal(0,2*np.std(features_test['12'][0][:,:,:,0]),features_test['12'][0][:,:,:,0].shape)\n",
    "features_test['12_deta05s'][0][:,:,:,0] = features_test['12'][0][:,:,:,0] + np.random.normal(0,0.5*np.std(features_test['12'][0][:,:,:,0]),features_test['12'][0][:,:,:,0].shape)\n",
    "features_test['12_dT1s'][0][:,:,:,1] = features_test['12'][0][:,:,:,1]  + np.random.normal(0,1*np.std(features_test['12'][0][:,:,:,1]),features_test['12'][0][:,:,:,1].shape)\n",
    "features_test['12_dT2s'][0][:,:,:,1] = features_test['12'][0][:,:,:,1]  + np.random.normal(0,2*np.std(features_test['12'][0][:,:,:,1]),features_test['12'][0][:,:,:,1].shape)\n",
    "features_test['12_dT05s'][0][:,:,:,1] = features_test['12'][0][:,:,:,1]  + np.random.normal(0,0.5*np.std(features_test['12'][0][:,:,:,1]),features_test['12'][0][:,:,:,1].shape)\n",
    "features_test['12_dS1s'][0][:,:,:,1] = features_test['12'][0][:,:,:,1]  + np.random.normal(0,1*np.std(features_test['12'][0][:,:,:,2]),features_test['12'][0][:,:,:,2].shape)\n",
    "features_test['12_dS2s'][0][:,:,:,1] = features_test['12'][0][:,:,:,1]  + np.random.normal(0,2*np.std(features_test['12'][0][:,:,:,2]),features_test['12'][0][:,:,:,2].shape)\n",
    "features_test['12_dS05s'][0][:,:,:,1] = features_test['12'][0][:,:,:,1]  + np.random.normal(0,0.5*np.std(features_test['12'][0][:,:,:,2]),features_test['12'][0][:,:,:,2].shape)\n",
    "\n",
    "features_test['12_dX1s'][1][:,0] = features_test['12'][1][:,0] + np.random.normal(0,1*np.std(features_test['12'][1][:,0]),features_test['12'][1][:,0].shape)\n",
    "features_test['12_dX2s'][1][:,0] = features_test['12'][1][:,0] + np.random.normal(0,2*np.std(features_test['12'][1][:,0]),features_test['12'][1][:,0].shape)\n",
    "features_test['12_dX05s'][1][:,0] = features_test['12'][1][:,0] + np.random.normal(0,0.5*np.std(features_test['12'][1][:,0]),features_test['12'][1][:,0].shape)\n",
    "features_test['12_ddx1s'][1][:,1] = features_test['12'][1][:,1] + np.random.normal(0,1*np.std(features_test['12'][1][:,1]),features_test['12'][1][:,1].shape)\n",
    "features_test['12_ddx2s'][1][:,1] = features_test['12'][1][:,1] +  np.random.normal(0,2*np.std(features_test['12'][1][:,1]),features_test['12'][1][:,1].shape)\n",
    "features_test['12_ddx05s'][1][:,1] = features_test['12'][1][:,1] + np.random.normal(0,0.5*np.std(features_test['12'][1][:,1]),features_test['12'][1][:,1].shape)\n",
    "\n",
    "features_test['12_ddy1s'][1][:,2] = features_test['12'][1][:,2] + np.random.normal(0,1*np.std(features_test['12'][1][:,2]),features_test['12'][1][:,2].shape)\n",
    "features_test['12_ddy2s'][1][:,2] = features_test['12'][1][:,2] + np.random.normal(0,2*np.std(features_test['12'][1][:,2]),features_test['12'][1][:,2].shape)\n",
    "features_test['12_ddy05s'][1][:,2] = features_test['12'][1][:,2] + np.random.normal(0,0.5*np.std(features_test['12'][1][:,2]),features_test['12'][1][:,2].shape)\n",
    "\n",
    "features_test['12_dTAUX1s'][1][:,3] = features_test['12'][1][:,3] + np.random.normal(0,1*np.std(features_test['12'][1][:,3]),features_test['12'][1][:,3].shape)\n",
    "features_test['12_dTAUX2s'][1][:,3] = features_test['12'][1][:,3] + np.random.normal(0,2*np.std(features_test['12'][1][:,3]),features_test['12'][1][:,3].shape)\n",
    "features_test['12_dTAUX05s'][1][:,3] = features_test['12'][1][:,3] + np.random.normal(0,0.5*np.std(features_test['12'][1][:,3]),features_test['12'][1][:,3].shape)\n",
    "\n",
    "features_test['12_dTAUY1s'][1][:,4] = features_test['12'][1][:,4] + np.random.normal(0,1*np.std(features_test['12'][1][:,4]),features_test['12'][1][:,4].shape)\n",
    "features_test['12_dTAUY2s'][1][:,4] = features_test['12'][1][:,4] + np.random.normal(0,2*np.std(features_test['12'][1][:,4]),features_test['12'][1][:,4].shape)\n",
    "features_test['12_dTAUY05s'][1][:,4] = features_test['12'][1][:,4] + np.random.normal(0,0.5*np.std(features_test['12'][1][:,4]),features_test['12'][1][:,4].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "usual-snake",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2d_dsigma_keys2 = ['12_deta1s', '12_deta2s', '12_deta05s',\n",
    "                   '12_dT1s', '12_dT2s', '12_dT05s', \n",
    "                   '12_dS1s', '12_dS2s', '12_dS05s', \n",
    "                   '12_dX1s', '12_dX2s', '12_dX05s',\n",
    "                   '12_ddx1s', '12_ddx2s', '12_ddx05s',\n",
    "                   '12_ddy1s', '12_ddy2s', '12_ddy05s',\n",
    "                   '12_dTAUX1s', '12_dTAUX2s', '12_dTAUX05s',\n",
    "                   '12_dTAUY1s', '12_dTAUY2s', '12_dTAUY05s', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "prescribed-garage",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41967/41967 [==============================] - 56s 1ms/step - loss: 288.4011 - mean_absolute_error: 288.4011 - mean_squared_error: 157187.7031 - accuracy: 0.4975\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 520.0598 - mean_absolute_error: 520.0598 - mean_squared_error: 543124.0000 - accuracy: 0.4973\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 174.3859 - mean_absolute_error: 174.3859 - mean_squared_error: 54767.6406 - accuracy: 0.5006\n",
      "41967/41967 [==============================] - 55s 1ms/step - loss: 12.1398 - mean_absolute_error: 12.1398 - mean_squared_error: 302.9176 - accuracy: 0.6424\n",
      "41967/41967 [==============================] - 55s 1ms/step - loss: 21.2244 - mean_absolute_error: 21.2244 - mean_squared_error: 886.3350 - accuracy: 0.5788\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 7.3800 - mean_absolute_error: 7.3800 - mean_squared_error: 121.5279 - accuracy: 0.7224\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 4.8232 - mean_absolute_error: 4.8232 - mean_squared_error: 57.8876 - accuracy: 0.8046\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 6.9162 - mean_absolute_error: 6.9162 - mean_squared_error: 108.2833 - accuracy: 0.7341\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 3.9227 - mean_absolute_error: 3.9227 - mean_squared_error: 42.6960 - accuracy: 0.8438\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 6.9145 - mean_absolute_error: 6.9145 - mean_squared_error: 205.8620 - accuracy: 0.7369\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 8.8276 - mean_absolute_error: 8.8276 - mean_squared_error: 297.8774 - accuracy: 0.6437\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 5.2384 - mean_absolute_error: 5.2384 - mean_squared_error: 125.4123 - accuracy: 0.8121\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 3.6922 - mean_absolute_error: 3.6922 - mean_squared_error: 42.6075 - accuracy: 0.8541\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 4.1145 - mean_absolute_error: 4.1145 - mean_squared_error: 54.7184 - accuracy: 0.8412\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 3.5363 - mean_absolute_error: 3.5363 - mean_squared_error: 38.7959 - accuracy: 0.8593\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 3.7877 - mean_absolute_error: 3.7877 - mean_squared_error: 47.9910 - accuracy: 0.8513\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 4.3453 - mean_absolute_error: 4.3453 - mean_squared_error: 68.6170 - accuracy: 0.8341\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 3.5655 - mean_absolute_error: 3.5655 - mean_squared_error: 40.4385 - accuracy: 0.8585\n",
      "41967/41967 [==============================] - 58s 1ms/step - loss: 5.3513 - mean_absolute_error: 5.3513 - mean_squared_error: 78.3989 - accuracy: 0.8108\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 7.8400 - mean_absolute_error: 7.8400 - mean_squared_error: 152.8369 - accuracy: 0.7557\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 4.1605 - mean_absolute_error: 4.1605 - mean_squared_error: 50.9749 - accuracy: 0.8435\n",
      "41967/41967 [==============================] - 56s 1ms/step - loss: 5.0899 - mean_absolute_error: 5.0899 - mean_squared_error: 66.7861 - accuracy: 0.8091\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 7.2811 - mean_absolute_error: 7.2811 - mean_squared_error: 129.5501 - accuracy: 0.7602\n",
      "41967/41967 [==============================] - 57s 1ms/step - loss: 4.0523 - mean_absolute_error: 4.0523 - mean_squared_error: 45.4709 - accuracy: 0.8394\n"
     ]
    }
   ],
   "source": [
    "mev_dsigma2 = {}\n",
    "\n",
    "for mnum in m2d_dsigma_keys2:\n",
    "    mev_dsigma2[mnum] = model['12'].evaluate(features_test[mnum], targets_test['12'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "adapted-technology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41967/41967 [==============================] - 57s 1ms/step - loss: 3.4722 - mean_absolute_error: 3.4722 - mean_squared_error: 37.2861 - accuracy: 0.8612\n"
     ]
    }
   ],
   "source": [
    "mev_dsigma2['12'] = model['12'].evaluate(features_test['12'], targets_test['12'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "asian-listening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['12_deta1s', '12_deta2s', '12_deta05s', '12_dT1s', '12_dT2s', '12_dT05s', '12_dS1s', '12_dS2s', '12_dS05s', '12_dX1s', '12_dX2s', '12_dX05s', '12_ddx1s', '12_ddx2s', '12_ddx05s', '12_ddy1s', '12_ddy2s', '12_ddy05s', '12_dTAUX1s', '12_dTAUX2s', '12_dTAUX05s', '12_dTAUY1s', '12_dTAUY2s', '12_dTAUY05s', '12'])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mev_dsigma2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "norwegian-victoria",
   "metadata": {},
   "outputs": [],
   "source": [
    "mev_ds_mae2 = {}\n",
    "mev_ds_mse2 = {}\n",
    "mev_ds_acc2 = {}\n",
    "dvar2 = {}\n",
    "dvar2['12_deta1s'] = 1*np.std(features_test['12'][0][:,:,:,0])\n",
    "dvar2['12_deta2s'] = 2*np.std(features_test['12'][0][:,:,:,0])\n",
    "dvar2['12_deta05s'] = 0.5*np.std(features_test['12'][0][:,:,:,0])\n",
    "\n",
    "dvar2['12_dT1s'] = 1*np.std(features_test['12'][0][:,:,:,1])\n",
    "dvar2['12_dT2s'] = 2*np.std(features_test['12'][0][:,:,:,1])\n",
    "dvar2['12_dT05s'] = 0.5*np.std(features_test['12'][0][:,:,:,1])\n",
    "\n",
    "dvar2['12_dS1s'] = 1*np.std(features_test['12'][0][:,:,:,2])\n",
    "dvar2['12_dS2s'] = 2*np.std(features_test['12'][0][:,:,:,2])\n",
    "dvar2['12_dS05s'] = 0.5*np.std(features_test['12'][0][:,:,:,2])\n",
    "\n",
    "dvar2['12_dX1s'] = 1*np.std(features_test['12'][1][:,0])\n",
    "dvar2['12_dX2s'] = 2*np.std(features_test['12'][1][:,0])\n",
    "dvar2['12_dX05s'] = 0.5*np.std(features_test['12'][1][:,0])\n",
    "\n",
    "dvar2['12_ddx1s'] = 1*np.std(features_test['12'][1][:,1])\n",
    "dvar2['12_ddx2s'] = 2*np.std(features_test['12'][1][:,1])\n",
    "dvar2['12_ddx05s'] = 0.5*np.std(features_test['12'][1][:,1])\n",
    "\n",
    "dvar2['12_ddy1s'] = 1*np.std(features_test['12'][1][:,2])\n",
    "dvar2['12_ddy2s'] = 2*np.std(features_test['12'][1][:,2])\n",
    "dvar2['12_ddy05s'] = 0.5*np.std(features_test['12'][1][:,2])\n",
    "\n",
    "dvar2['12_dTAUX1s'] = 1*np.std(features_test['12'][1][:,3])\n",
    "dvar2['12_dTAUX2s'] = 2*np.std(features_test['12'][1][:,3])\n",
    "dvar2['12_dTAUX05s'] = 0.5*np.std(features_test['12'][1][:,3])\n",
    "\n",
    "dvar2['12_dTAUY1s'] = 1*np.std(features_test['12'][1][:,4])\n",
    "dvar2['12_dTAUY2s'] = 2*np.std(features_test['12'][1][:,4])\n",
    "dvar2['12_dTAUY05s'] = 0.5*np.std(features_test['12'][1][:,4])\n",
    "\n",
    "\n",
    "for r in mev_dsigma2.keys():\n",
    "    mev_ds_mae2[r] = mev_dsigma2[r][0]\n",
    "    mev_ds_mse2[r] = mev_dsigma2[r][2]\n",
    "    mev_ds_acc2[r] = mev_dsigma2[r][-1]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "original-mirror",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'12_deta1s': 80.69599914550781,\n",
       " '12_deta2s': 161.39199829101562,\n",
       " '12_deta05s': 40.347999572753906,\n",
       " '12_dT1s': 10.82296085357666,\n",
       " '12_dT2s': 21.64592170715332,\n",
       " '12_dT05s': 5.41148042678833,\n",
       " '12_dS1s': 2.4386284351348877,\n",
       " '12_dS2s': 4.877256870269775,\n",
       " '12_dS05s': 1.2193142175674438,\n",
       " '12_dX1s': 0.6747155077427388,\n",
       " '12_dX2s': 1.3494310154854776,\n",
       " '12_dX05s': 0.3373577538713694,\n",
       " '12_ddx1s': 0.6097667951914343,\n",
       " '12_ddx2s': 1.2195335903828686,\n",
       " '12_ddx05s': 0.30488339759571714,\n",
       " '12_ddy1s': 0.5708296645318798,\n",
       " '12_ddy2s': 1.1416593290637596,\n",
       " '12_ddy05s': 0.2854148322659399,\n",
       " '12_dTAUX1s': 1.2998861340710572,\n",
       " '12_dTAUX2s': 2.5997722681421145,\n",
       " '12_dTAUX05s': 0.6499430670355286,\n",
       " '12_dTAUY1s': 1.0634313247992315,\n",
       " '12_dTAUY2s': 2.126862649598463,\n",
       " '12_dTAUY05s': 0.5317156623996158}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dvar2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "characteristic-sleep",
   "metadata": {},
   "outputs": [],
   "source": [
    "dMdvar_0 = {}\n",
    "dMdvar_1 = {}\n",
    "dMdvar_2 = {}\n",
    "for r in m2d_dsigma_keys2:\n",
    "    dMdvar_0[r] = (mev_ds_mae2[r] - mev_ds_mae2['12'])/ dvar2[r]    \n",
    "    dMdvar_1[r] = (mev_ds_mse2[r] - mev_ds_mse2['12'])/ dvar2[r]    \n",
    "    dMdvar_2[r] = (mev_ds_acc2[r] - mev_ds_acc2['12'])/ dvar2[r]    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "catholic-bottle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.23598976, 0.72213612, 0.36953178, 5.23552763, 0.21019904,\n",
       "       0.32717616, 1.05913416, 1.09113665])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(list(dMdvar_0.values()))[2::3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "frozen-milton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0691383ca0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x750 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5), tight_layout=True, dpi = 150)\n",
    "xlabs = [r'$ \\eta$', r'$ \\theta$', r'$S$', r'$ X$ $(=\\frac{f}{2\\Omega})$', r'$ dx$', r'$ dy$', r'$ \\tau_x$', r'$ \\tau_y$' ]\n",
    "ylabs = [r'$\\Delta(x_i) = 0.5\\sigma(x_i)$', r'$\\Delta(x_i) = \\sigma(x_i)$', r'$\\Delta(x_i) =2 \\sigma(x_i)$', ]\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(np.array(list(mev_ds_mae2.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1)\n",
    "plt.plot(np.array(list(mev_ds_mae2.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2)\n",
    "plt.plot(np.array(list(mev_ds_mae2.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3)\n",
    "plt.hlines(np.array(list(mev_ds_mae2.values()))[-1], -0.5, 7.5, linestyle = 'dashed', linewidth=3, alpha=0.6, color = colors['11'])\n",
    "plt.xticks([0,1,2,3,4,5,6,7], xlabs)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.ylabel(r'Mean Absolute Error $[cm/s]$ ')\n",
    "plt.yscale('log')\n",
    "plt.xlim([-0.5,7.5])\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'$ \\mathbf{M} =$ Mean Absolute Error $[cm/s]$')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(np.array(list(dMdvar_0.values()))[2::3], 'v', label= ylabs[0], color=color1,)\n",
    "plt.plot(np.array(list(dMdvar_0.values()))[::3], 'o', label= ylabs[1], color=color2,)\n",
    "plt.plot(np.array(list(dMdvar_0.values()))[1::3], '^', label= ylabs[2], color=color3, )\n",
    "plt.xticks([0,1,2,3,4,5,6,7], xlabs)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.ylabel(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta (x_i)}$ ')\n",
    "plt.title(r'$\\frac{\\Delta \\mathbf{M}(x_i,x_j)}{\\Delta (x_i)}$; $(x_i, x_j) \\in \\{\\eta,\\theta,S,X,dx,dy,\\tau_x,\\tau_y\\}$,' )\n",
    "plt.xlim([-0.5,7.5])\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "correct-guyana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([288.40109253, 520.05981445, 174.38587952,  12.13976383,\n",
       "        21.22443199,   7.37999201,   4.82315874,   6.91622734,\n",
       "         3.92274189,   6.91451311,   8.82761955,   5.23841238,\n",
       "         3.69217658,   4.1145196 ,   3.53625274,   3.78770065,\n",
       "         4.34527826,   3.56554747,   5.35130167,   7.84004211,\n",
       "         4.16054344,   5.08994246,   7.28105164,   4.05234098,\n",
       "         3.47216654])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(list(mev_ds_mae2.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "quality-webcam",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['12_deta1s', '12_deta2s', '12_deta05s', '12_dT1s', '12_dT2s', '12_dT05s', '12_dS1s', '12_dS2s', '12_dS05s', '12_dX1s', '12_dX2s', '12_dX05s', '12_ddx1s', '12_ddx2s', '12_ddx05s', '12_ddy1s', '12_ddy2s', '12_ddy05s', '12_dTAUX1s', '12_dTAUX2s', '12_dTAUX05s', '12_dTAUY1s', '12_dTAUY2s', '12_dTAUY05s', '12'])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mev_dsigma2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "exterior-bhutan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['12_deta1s',\n",
       " '12_deta2s',\n",
       " '12_deta05s',\n",
       " '12_dT1s',\n",
       " '12_dT2s',\n",
       " '12_dT05s',\n",
       " '12_dS1s',\n",
       " '12_dS2s',\n",
       " '12_dS05s',\n",
       " '12_dX1s',\n",
       " '12_dX2s',\n",
       " '12_dX05s',\n",
       " '12_ddx1s',\n",
       " '12_ddx2s',\n",
       " '12_ddx05s',\n",
       " '12_ddy1s',\n",
       " '12_ddy2s',\n",
       " '12_ddy05s',\n",
       " '12_dTAUX1s',\n",
       " '12_dTAUX2s',\n",
       " '12_dTAUX05s',\n",
       " '12_dTAUY1s',\n",
       " '12_dTAUY2s',\n",
       " '12_dTAUY05s']"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "architectural-dutch",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "arctic-certification",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "soviet-family",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "finnish-annex",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "celtic-pizza",
   "metadata": {},
   "outputs": [],
   "source": [
    "dMdvar = {}\n",
    "for r in m2d_dsigma_keys:\n",
    "    dMdvar[r] = (mev_ds_mae[r] - mev_ds_mae['11'])/ dvar[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "published-enforcement",
   "metadata": {},
   "outputs": [],
   "source": [
    "dMdvar2 = {}\n",
    "for r in m2d_dsigma_keys:\n",
    "    dMdvar2[r] = (mev_ds_mse[r] - mev_ds_mse['11'])/ dvar[r]\n",
    "dMdvar3 = {}\n",
    "for r in m2d_dsigma_keys:\n",
    "    dMdvar3[r] = (mev_ds_acc[r] - mev_ds_acc['11'])/ dvar[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "curious-buffer",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "latest-murder",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "naval-demand",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "excess-translator",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "russian-radius",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "insured-mitchell",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "weighted-lucas",
   "metadata": {},
   "outputs": [],
   "source": [
    "xlabs = [r'$\\Delta \\eta$', r'$\\Delta \\theta$', r'$\\Delta X$', r'$\\Delta dx$', r'$\\Delta dy$', r'$\\Delta \\tau_x$', r'$\\Delta \\tau_y$' ]\n",
    "ylabs = [r'$\\Delta = 0.5\\sigma$', r'$\\Delta = 1\\sigma$', r'$\\Delta =2 \\sigma$', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "pursuant-track",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Accuracy $[\\\\%]$')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4), tight_layout=True, dpi = 150)\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][2::3], 'v', label= ylabs[0])\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][::3], 'o', label= ylabs[1])\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][1::3], '^', label= ylabs[2])\n",
    "plt.hlines(np.array(list(mev_ds_mae.values()))[-1], -0.5, 6.5, linestyle = 'dashed', color = colors['11'])\n",
    "plt.xticks([0,1,2,3,4,5,6], xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'Mean Absolute Error $[cm/s]$')\n",
    "\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.plot(np.array(list(mev_ds_mse.values()))[0:-1][2::3], 'v', label= ylabs[0])\n",
    "plt.plot(np.array(list(mev_ds_mse.values()))[0:-1][::3], 'o', label= ylabs[1])\n",
    "plt.plot(np.array(list(mev_ds_mse.values()))[0:-1][1::3], '^', label= ylabs[2])\n",
    "plt.hlines(np.array(list(mev_ds_mse.values()))[-1], -0.5, 6.5, linestyle = 'dashed', color = colors['11'])\n",
    "plt.xticks([0,1,2,3,4,5,6] , xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'Mean Square Error $[cm^2/s^2]$')\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.plot(np.array(list(mev_ds_acc.values()))[0:-1][2::3]*100, 'v', label= ylabs[0])\n",
    "plt.plot(np.array(list(mev_ds_acc.values()))[0:-1][::3]*100, 'o', label= ylabs[1])\n",
    "plt.plot(np.array(list(mev_ds_acc.values()))[0:-1][1::3]*100, '^', label= ylabs[2])\n",
    "plt.hlines(np.array(list(mev_ds_acc.values()))[-1]*100, -0.5, 6.5, linestyle = 'dashed', color = colors['11'])\n",
    "plt.xticks([0,1,2,3,4,5,6] , xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'Model Accuracy $[\\%]$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "vital-queensland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean Absolute Error $[cm/s]$')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ze3g942t5s63GUiRrKRkTkYxx9y/cva277xUuDRn9bCy7aib+bGYn11bYzDqZWc3h3Zewq7biP8xst4ESwhEIH6f68M6ZdAkQm3dqWj32i5Vtz65kLpFiYHxYK1SNmQ1nV1+15VQf8Q8zO87Maq1BNLP+BKNnwu61G3eHj3nAlHA4+Jr7H0j1uY5+X9v5kpgHxPrP/DjJfT8EuK+O48QPOFNbDVrMWPb8d7apaax7HrXn436+KWwCnZCZHWRmNWtk4xOn2poe5rGrb1ldSVvsmOXUqMU2s4PZNWVCwiaKZtbFzEYkei+IK9MRuCF8uo6g362IpEB9xkQkp7n7HDP7BfBbggTjFTN7HHiKYFLdSoLR/o4hGAJ+MMGw7X+JO8aXZvYUQXOdo4A3zOz/AYsJmhqdSNC36SCCZjyxwUYyKf7DeX0GgXgMuC38+Qp2jWpZ03yCpo1dzOw+guGw2xNco6sJPjRXAt9P8G16T2CCmb1PMNz22wQJSynBaIKDw2PE1BxMZArBnHPDCZqFvmtmvyP40JkPnAz8lF0jEz7o7q+k8NqrcfcNZvYIwXU4EphjZncQJOftwjh/HMb9MckTrYXAVwR94H5mZmsJagliid6O+BFC0/E720Ctw7nyUvGFuze0z1NDNMo9j5q7v2RmrxIMbHEM8E8zu5+g3+RXBM1hjyGYm+9Udu+bmVIyRvC7uHcK5WBXDdqH4UTR8c4NH7eTfLLvwQStFj4PB+94nWCut50EI6UOAL5H8NoqgR+kMH+eiISUjIlIznP328MPwPcQ1LbE+kglk2gy42sIErEjCD4sTax5GoLR75aR4WTMzHqx60PbG+6+ItV93X2pmS0g+IA2xMwOcfflCYr+kmDEw3OAkxJsLwG+5+6zaznd0eGSTCnwE3efVSNGN7MLCaYZuIBgkJJEo7M5QSJ3fS3nqMsNBDUMxxFc05rDka8haJp6F0mSMXffbma/BX4NdEpwjFeAQTX2ScfvbH0dTzBXXiruAa5LwzlT0sj3PGoXEHwJ0ZfgdSSrwVsWNluNVzV4B8F7TTKpDt5RCHy9lnLnho//cPcdSQ4Ti+lAgi9Rrk5SbgNwtbtPTxaPiOxOyZiINAnuPsHM/kZQY3QGQRIQawL0JcE3uW8Aswg+HNfcf62ZHU/wIe98dn3wXkNQG/ZHd3/NzC6P8nWkKL5WrD5NFOP36UMwguV3gZsSlCkjqAUbRTCX2dEEtUVrCJpi3eXuHyU5/qMETQ+HECSuBwP7E3yTv4Wg5ulF4E9JEkHCD4YXmtlp4flPJqhFqCDol/MKMD7ZnEipcvdNYZPJHxPUzHQnuC6fEgzzfbe7r69rygB3/42ZfURQy3YcQTPPWpu07unvbFPTWPc8auHvy8kEyfW3CZKZfQm+fPg3QdPe56nRvDcUa3q4MG66h0RSHbzjGHYNJFKtXNhE8pTwaW2jKI4laNI7gKDW+0CCv+cCgt/TfwF/BybV0XdNRBKw2v/WRURERJovMxsL3Bw+PaQ+NdHZzMy+Q9ACoBzYrzETKTN7mWBex0/dvWtjnVckG6lmTERERCQ13eMGF2nsfnXpdm74+ErUiVg4H9kRcavSNW+fSM5TMiYiIiKSmvhpHBq1X10EXicYXfHlRjjXQaTeX1GkWVEy1kDh3DA/Aw4HWgGrCPph3OrupbXtKyIiIpJJ7n5npmMQEfUZazAzG0LQ0fpDYBtBp9s/AtPc/ceZjE1ERERERLJfk6wZM7M+wDcJhpXtRzDyT4m771XHfnsBYwhGQOpMMErQs8BN7r4qvqy7v1hj9xVmdgpwWlpehIiIiIiINGlNsmbMzJ4kmBsnXq3JWJiIzSaYT+ffBENZdyVI6NYDJ7r7J7Xs34NgaNjnVDMmIiIiIiJ1yct0ABF5HbgVOJvdZ7dP5hcEidjrQHd3v8jd+xFMCNoReDjRTmb2lZmVAIsIOsFm80SUIiIiIiKSJZpkzVhNZubUUjMWDrm6DmgP9Hb3d2psfxc4Fji+5mSTZnY40BI4HridYELKmxEREREREalFk+wz1gD9CRKxT2omYqEnCJKxs4FqyZi7Lw1//JeZVQIPm9md7r4twnhFRERERCTHNdVmivXVM3xcmGT7whrlamNAwR5HJCIiIiIiTZpqxgKdw8dVSbavqlEOM/slMB9YRpCA9QXuAJ5y903RhCkiIiIiIk2FkrHA3uHj9iTbt9UoB0E/sfuAg4FyYAVwN3Bvqic1sw+SbOoO7AA+S/VYIiIiIiLS6A4Gtrt7qoMGVqNkLGDhY7LRTKzmCnf/BcEIjFHIKygoaHPggQceFdHxG11lZSUAeXlqGZvtdK9yi+5X7tC9yh26V7lD9yp3NNV79fnnn1NWVtbg/ZWMBbaGj62TbG8VPn6VzpO6+9cTrTezD7p163bUBx8kqzjLPS++GMyRPWTIkAxHInXRvcotul+5Q/cqd+he5Q7dq9zRVO/V17/+dRYtWtTg1mxNKzVtuJXhY6ck2zvVKCciIiIiIrJHlIwF3g0feyfZHlv/XiPEIiIiIiIizYCSscA8YDNwmJkdl2D7BeHjM40XkoiIiIiINGVKxgB3LwXuD5/eb2ZVfcfM7CcEEz7Pdfe3MhGfiIiIiIg0PU1yAA8zGw78ssbqQjN7I+75r9x9Ztzz24DTgJOAJWY2B+gC9AM2AN+NMGTMrD3QPnxaEBtxRkREREREmqYmmYwBHQmSqHhWY13H+I3uvtPMBgNjgEuAc4GNwCTgl+4e9Zxf1wE3x55s2LAh4tOJiIhILnF33JPNwtO86Uvs3JHt98rMMNttVqvINMlkzN0nAhMbsN8O4KZwaWzj2BXzc8XFxd0yEIOIiIhkme3bt/PFF1+wbdu2TIeSdVq3DnqWfPTRRxmOROqSS/eqsLCQNm3aUFxcTH5+fqTnapLJWC5y903AJgAzK2tqE+KJiIhI/VVUVLBq1SoqKioyHUpWin3Al+yXS/eqtLSUDRs2sG3bNjp37hxpQqZkTERERCRLrV+/noqKCoqKijjooIMoKCjIdEhZZcuWLQC0bds2w5FIXXLlXlVWVrJt2zbWrl3Lzp072bBhA/vtt19k51MyJpErqShjbtlSAPpXnEJhvn7tREREUrF161YA9ttvP4qKijIcTfaJtSRSi6Lslyv3Ki8vj3bt2gHw+eefs3Xr1kiTsey+GtIkzPxsPs+XLeb5ssXM/Gx+psMRERHJCe5OeXk5gBIxkUYWa1ZZWloa6cA5SsYkUiUVZTyydHbV8ylLX6C0ojyDEYmIiOSG+A+AUQ8iICLVxdfgKRlrBsysvZl1NbOuNKF5xmZ+Np/1OzdXPV+/c7Nqx0REREREUDKWTa4DlodLt6Ywz1jNWrEY1Y6JiIiIiCgZyybjgEPCZUlxcXFmo0mDmrViMaodExERERFRMpY13H2Tu69w9xVAzs8zlqxWLEa1YyIiIiLS3OX2J37JWslqxWJUOyYiIiLZrKSkhC5dujB69Oi0HfPpp58mLy+Pd955J23HbIqa07VXMiZpV1pRXmutWMwjS2erdkxERCSblJVCyc6GLWWlmY4+JUcffTRmxsUXX1xruQceeIA1a9bw05/+NG3nPvvsszn22GMZM2ZM2o5ZHy+//DKDBw+mTZs2tG/fnjPPPLPeyYmZJV2effbZtMTZFK99Mpp9V9KurlqxmHU7NzHzs/mc1/XkRohKRERE6jT3HzD1gYbte+kPYfBZ6Y0nzRYsWMAHH3xA27ZtefLJJ9m8eXPVBL/xSktLuf3227ngggvo1KlT2s5vZowePZorrriCN998k759+6bt2HWZOXMmI0aMoF27dnznO98B4NFHH+Xkk0/mlVde4Rvf+EbKx+rSpQuXX375busPP/zwPY6zKV772qhmTNKqtKKcKUtfSLm8asdERESySP8zoMO+9d+vw77Q//T0x5NmEydOJC8vj3HjxrFz504ef/zxhOWefPJJ1q9fzyWXXJL2GL71rW9RVFTEQw89lPZjJ1NaWsrVV1/NXnvtxeuvv87999/P/fffz2uvvYaZcfXVV9freF27dmXs2LG7LelIxprata+LkjFJq1RrxWJitWMiIiKSBQoKYdhF9d9v2Mhg3yxWVlbGX//6VwYOHMioUaPo2LEjkyZNSlh28uTJtGrVitNP3z3BrKiooHv37rRq1Yo1a9ZU2zZnzhyKioo48cQT2bFjR8Jjt2vXjiFDhjBt2jTKysr2/IWl4Pnnn2fVqlWMGjWKI444omr9kUceyaWXXsrChQsj7Uu1fPlyfvjDH3L00UdTWFiYsInjCy8EX+Ynu/Z7et0hM9e+LkrGJG3qWysWo9oxERGRLFLf2rEcqRWbMWMGGzZs4OKLL6ZFixZceOGFzJs3j6VLl1YrV1lZyZw5c+jduzcFBQW7HSc/P5+xY8eyY8cO7rrrrqr1H3/8Meeeey4HH3wwM2bMoGXLlklj6devH1u3bmXBggXpe4G1mDNnDgCnnnrqbttOO+20amVSsXHjRsaPH89vfvMbJkyYwGeffZa07Lvvvkvv3r2ZOnUqPXv25MYbb+TMM8+s2t65c2cOO+wwjjnmmFqvfTquOzT+ta+L+oxlCTNrD7QPnxZUVlZmLpgGqm+tWIz6jomIiGSRWO1Yqn3HcqBWDGDSpEkUFBRw/vnnA3DJJZfw4IMPMnnyZG699daqcosWLWLLli306tUr6bFGjhzJb3/7W8aPH8/Pf/5z8vLyGD58OGbGrFmz2Hff2pPZPn36APDaa69xwgknJC03duzYlF9f165dE/bjAqoSzsMOO2y3bbF1NZPS2rz33ntcc801Vc9btGjB9ddfzx133IGZVa0vLS3lwgsvZOvWrUyfPp0hQ4bQtm1bAC677DKmTJnCbbfdxqhRowB4//33a732e3rdIfVr31iUjGWP64CbY082bNiQuUgaaNjBfRna6fiE21555RUABg4cmHB7nqmSVkREJGv0PwP+/hhs/KL2cjlSK7Z+/XpmzZrFGWecwT777APASSedROfOnZk8eTK33HJLVRKxatUqAPbff/+kx8vLy+OWW27h/PPP57bbbmPhwoWsWrWK2bNn061btzrj2W+//aqdK5lbbrklpdcHwWesZMnYli1bAKoSoXixdZs3p/aF+o033sj555/PEUccQWVlJa+//jo33ngjd911Fx06dKg2WuHUqVNZsmQJN954I0OGDKl2nAsuuIApU6awcOHCqmSsrmu/p9cdUr/2jUWfgLPHOOCQcFlSXFyc2WgaoCi/gJYtihIuhdaCQmuRdHtR/u7NAERERCRDCgrhzG/XXW7YRTlRKzZ16lTKysqqDWdvZowcOZJPP/2Ul19+uWr9l19+CUD79u1rPea3vvUt+vTpw3333cdrr73GlClTOOmkk3YrN3z4cB588MFq6zp06ADU/eW7u6e8xL+GRMeJveY9ddddd3HCCSfQoUMHiouLOeuss3jhhRdo164dt99+e7W+WNOmTQPg2muv3e04bdq0AYK+YDGpXPs9ue6Q+rVvLErGsoS7b3L3Fe6+AijLy9OtERERkQwaMLT2vmMd9g1q0HLApEmTaNWqFSNGjKi2PpacxQ/kEetztHPnzlqPWVlZWVXLcuaZZ3LBBRckLDdz5kx+8IMfVFsXG2Sirv5N6RIbvj9R7Ves1izREP+p6tSpE6effjpbtmzhww8/rFr/xhtvcPjhh9O1a9fd9lm9ejVQvelkKtd+T647NP61r4uaKYqIiIjI7mK1Y4/uXrsA5Eyt2Hvvvcc///lPLrroIvbee+9q23r16kWPHj2YPn06DzzwAK1bt6Zjx47ArlqaZK6//npmzZrFfvvtx3PPPceyZcs49NBDU4pp48aNAFXnSiZdfcZiQ85/8skn9O7du9q2Tz75pFqZhoq9lm3btgHB9duyZQs9e/ZMWH7WrFlA9S4sqVz7PbnukPq1byxKxkREREQksQFDYdbju/cdy6FasYkTJwJUa6IY7+KLL+amm25i+vTpXHbZZRx11FGYGUuWLEl6zHHjxnHvvfdyzTXXcNFFFzFo0CDGjh3L5MmTq5V79dVXGTFiBBs3bqzWRPDjjz8G4Jhjjqk19nT1GRswYAB33HEHs2fP5sILL6y2LTak/IABA1I+VyKx0Qm7dOkC7GoSGat5i7dixQqefPJJevbsWW2wjrqu/Z5ed0j92jcWtYUTERERkcSS9R3LkVqx8vJypk6dSvv27Rk6dGjCMjWbKu6zzz4ceeSRvP322wnL/+1vf+OGG25g2LBh3HfffQwcOJBBgwYxderUak30ABYuXEjv3r13SwjefPNNAPr3719r/OnqM3baaafRqVMnpkyZwkcffVS1fvHixUydOpXjjjuO4447rto+ixcvrqo1i/nXv/7FV199tdvx77nnHubPn0///v058MADgaBvVufOnXnvvfdYtGhRVdmvvvqKUaNGsWPHDn73u99VO05t1z4d1x1Sv/aNRTVjIiIiIpJczdqxHKoVmzVrFuvWreOggw5KOIhETKtWrXjppZdYuXIlnTt3ZsSIEdxxxx0sX76cQw45pKrc/PnzufTSSzn22GN57LHHyM/PB+Dmm29m8ODB3HzzzTz++ONV5RcsWFA1lHq8l156iT59+lQlLlErKiriD3/4A+eccw4nnngil1xyCQCPPvoolZWVjB8/frd9evToQZcuXVixYkXVur/85S9MnDiRU089lc6dO1NRUcEbb7zBW2+9RceOHfnTn/5U7Rhjxozhmmuu4ZRTTuHCCy/EzJgxYwaff/4548aNq5rjLF6ia5+u6w6Nf+3ropoxEREREUmuZu1YjtSKwa7artWrV/OXv/wl6bJ9+3bcvaq525VXXgnsGg0QYNmyZZx99tkUFxfzzDPPVOt/NmjQIAYOHMgTTzzBu+++W7V+wYIFHH989Wl/3nzzTT755JOqczSW2KiHxx57LBMnTmTy5Mn069ePefPm0bdv35SOMXToUIYMGcLChQv505/+xJ/+9Cc2b97M6NGjeffdd+nRo0e18ldffTXjx49n//335+GHH2batGn07t2bOXPm8OMf/zjhOWpe+3Rdd8jcta+NasZEREREpHYDhkJJSfBzjtSKATzxxBMN2q9bt24MGzaMyZMn81//9V+YGYceeijr1q1Luk/NZoLbtm3jo48+2q2GZuLEieyzzz5ceumlDYptTwwePJjBgwenVDY2HH68oUOHJm3umcxVV13FVVddVetcZ/FqXvt0XXfI7LVPRjVjIiIiIlK7gkI488JgyZFasT31m9/8ho8++ojp06c3aP933nmHtm3bVhu6fc2aNUyYMIExY8bUmZQ0Z3ty7RNdd8jea6+asSxhZu2B9uHTgsrKyswFIyIiItLM9ezZkz//+c+Ul5c3aP8FCxbsNojEypUrGTNmDD/84Q/TFWaTtCfXPtF1h+y99krGssd1wM2xJ9kyK7iIiIhIc7UnfYtGjx7N6NGjq63r27dvyv2zmruGXvtE1x2y99qrmWL2GAccEi5LiouLMxuNiIiIiIhESjVjWcLdNwGbAMysLC9PebKIiIiISFOmT/wiIiIiIiIZsMc1Y2b2cDoCSWCzu18f0bFFREREREQyKh3NFC8HHLA6ytWHA2sBJWMiIiIiItIkpavP2E7g8TQdC+A7aTyWiIiIiIhI1klXMrbZ3b+bpmNhZkrGRERERESkSdMAHiIiIiIiIhmQjpqxe4HNaThO1McUERERERHJGnucjLn7dWmII/JjioiIiIiIZBNN+pwlzKw90D58WlBZWZm5YEREREREJHKR9Bkzs+5m9m0z6xrF8Zuo64Dl4dJtw4YNmY1GREREREQiFdUAHt8B/grcnGijmQ03s6vNrH9E589F44BDwmVJcXFxZqMRERERacZKSkro0qULo0ePTtsxn376afLy8njnnXfSdsxsomtWf1ElYwPDx8k1N5jZY8DTwAPAK2Y2x8z2jSiOnOHum9x9hbuvAMry8jTQpYiIiEi6HX300ZgZF198ca3lHnjgAdasWcNPf/rTtJ377LPP5thjj2XMmDFpO2aq7r33XkaNGsWRRx5JXl4eZsbOnTvTeo6mds0aQ1R9xrqEjwviV5rZycCF4dP1QFvgZOApM+vv7h5RPCIiIiLSAFu2VzJnUQkff17GzjJnrwKj+0EFDOhRRNtWufXl8YIFC/jggw9o27YtTz75JJs3b6Zdu3a7lSstLeX222/nggsuoFOnTmk7v5kxevRorrjiCt5880369u2btmPXJVZb1aVLF9q1a8emTZvSevymeM0aQ1R/QR2Bre6+pcb6y8LHZ4ADgEOBD4ETgNq/nhARERGRRlNa7kx+eRs/m7yJJ9/cwaJV5SxbW8GiVeU8OX8HP5u8iSkvb6OsPHe+S584cSJ5eXmMGzeOnTt38vjjjycs9+STT7J+/XouueSStMfwrW99i6KiIh566KG0H7s2M2fOZN26daxYsYKePXum/fhN8Zo1hqiSsTKgIMH64YADYzzwb+AXgKFkTERERCQrlJY79zyzlTmLSqhIMsBzRSW8uqiEcc9spTQHErKysjL++te/MnDgQEaNGkXHjh2ZNGlSwrKTJ0+mVatWnH766bttq6iooHv37rRq1Yo1a9ZU2zZnzhyKioo48cQT2bFjR8Jjt2vXjiFDhjBt2jTKysr2/IWlaNiwYXTs2LFe+yxfvpzvfe97dOnShcLCQsxst+WFF14Akl+z2PU64IADWLt2bbVtqVwvyNw1awxRJWMrgb3M7GuxFWZ2LHAgsMTdP4grO4sgQesdUSwiIiIiUg/T5m7n48/LUyr78eflPDZ3e8QR7bkZM2awYcMGLr74Ylq0aMGFF17IvHnzWLp0abVylZWVzJkzh969e1NQsHvdQn5+PmPHjmXHjh3cddddVes//vhjzj33XA4++GBmzJhBy5Ytk8bSr18/tm7dyoIFC5KWybR3332X3r17M2HCBPr06cONN97ImWeeWbW9c+fOHHbYYRxzzDG1XrP463XvvfdWra/P9YLcuGYNEVWfsVeBI4H/AGK/pVeGjy/GF3T3UjPbCGj4QBEREZEM27y9ktcWl9Rrn3mLSzinb8us7kM2adIkCgoKOP/88wG45JJLePDBB5k8eTK33nprVblFixaxZcsWevXqlfRYI0eO5Le//S3jx4/n5z//OXl5eQwfPhwzY9asWey7b+1j0/Xp0weA1157jRNOOCFpubFjx6b8+rp27crll1+ecvnalJaWcuGFF7J161aeffZZvvnNb1Ztu+yyy5gyZQq33XYbo0aNAuD999+v9ZqNHDmSX//61zz88MP88pe/rPf1gtSvWa6JKhl7EPgecIuZFYTn+QFBDdj0BOVbA9n/lYqIiIhIEze3lqaJyVRUwpwPSxjep/bajUxZv349s2bN4owzzmCfffYB4KSTTqJz585MnjyZW265BTMDYNWqVQDsv//+SY+Xl5fHLbfcwvnnn89tt93GwoULWbVqFbNnz6Zbt251xrPffvtVO1cyt9xyS0qvD2DgwIFpS8amTp3KkiVLuPHGG6slYgAXXHABU6ZMYeHChVXJWF3XLC8vjzFjxjBq1KgGXS9I/Zrlmki+vnD3fwH/DewF/IpgvrF8YL67z44va2aHA0XAuihiEREREZHUffx5w/rkfLw6e/vyTJ06lbKysmrD2ZsZI0eO5NNPP+Xll1+uWv/ll18C0L59+1qP+a1vfYs+ffpw33338dprrzFlyhROOumk3coNHz6cBx98sNq6Dh06ALBhw4Zaz+HuKS/xr2FPTZs2DYBrr712t21t2rQBgr5gMalcsxEjRtCrV68GXS9I/ZrlmqhqxnD3O83sPeAqgqHu/wX8PEHRWOPTxVHFIpkTGw73jZWHUlaZz7tPb8nZ4XBFRESag51lDRuMo6H7NYZJkybRqlUrRowYUW39xRdfzJ133smkSZMYPHgwQFXfpbrm4KqsrKyqrTnzzDO54IILEpabOXPmbutig1XU1U8qU9544w0OP/xwunbtutu21atXA3DYYYdVrUvlmlVWVlYNIFLf6wXZf80aKrJkDMDdnwWeraPYdwmaL74SZSzSuErLnWlzt/Pa4lhTh7YAbFhVzqJV5cx4awcnH1nEyP6tKGhhGY1VREREdtmroGH/lxu6X9Tee+89/vnPf3LRRRex9957V9vWq1cvevTowfTp03nggQdo3bp1VcIQq+1J5vrrr2fWrFnst99+PPfccyxbtoxDDz00pZg2btwIUOfohpnoM/bll1+yZcuWpMPfz5o1CwiaRcakcs3GjBnD888/36DrBalfs1wTaTJWFzMrBP4IHEcwqqI0AbHhcGsbhSk2HO6aTRWMPqsNhUrIREREskL3AwtYtCq1kRSr7XdQolmNMm/ixIkA1Zooxrv44ou56aabmD59OpdddhlHHXUUZsaSJUuSHnPcuHHce++9XHPNNVx00UUMGjSIsWPHMnny5GrlXn31VUaMGMHGjRur+qRBMJIgwDHHHFNr7JnoMxaLc8uWmtMFw4oVK3jyySfp2bNntcE66rpm48aNY/z48Vx55ZWMGjWq3tcLUr9muSYt7cTMbLCZ5dd3P3cvdfc/uvvV7q5mik1EUxwOV0REpLnof1QR+fX8hJifBwN6FEUT0B4oLy9n6tSptG/fnqFDhyYsE0vSYnOO7bPPPhx55JG8/fbbCcv/7W9/44YbbmDYsGHcd999DBw4kEGDBjF16lQ+/PDDamUXLlxI7969d0ss3nzzTQD69+9fa/yZ6DPWoUMHOnfuzHvvvceiRYuq1n/11VeMGjWKHTt28Lvf/a7aPrVds9j1Ov3007nrrrsadL0g9WuWa9JVMzYb+NLM/g48DTzr7l+l6diSQ5rqcLgiIiLNRbtWeZx0ZBFzFqX+//zkI7OzL/isWbNYt24dBx10UMLBKGJatWrFSy+9xMqVK+ncuTMjRozgjjvuYPny5RxyyCFV5ebPn8+ll17Ksccey2OPPUZ+flAXcfPNNzN48GBuvvlmHn/88aryCxYsqBqSPd5LL71Enz59OPDAA9P4amt3++23s3hxUPcRe/z+979f9Rp+97vfVQ0xP2bMGK655hpOOeUULr30UsyM6dOn8/nnnzNu3DhOO+203Y6f6JrFX68JEyY0+HpBZq5ZY0jXX817wD4E84o9Bqw3s2fM7D/N7IA0naNJM7P2ZtbVzLoCBZWV9RxTNkvsyXC4IiIikh1G9m9F9wNT+86++4EtGNm/VcQRNUystmv16tX85S9/Sbps374dd69qNnfllcH0uLFRBQGWLVvG2WefTXFxMc8880y1/meDBg1i4MCBPPHEE7z77rtV6xcsWMDxxx9fLaY333yTTz75pOocjeXZZ59l0qRJTJo0ibVr1wLwyCOPVK376qtd9ShXX30148ePZ//992f8+PFMmTKF3r17M2fOHH784x8nPH7Na5au6wWZu2aNwdzTM/KNmXUGzgHOBQYQ1Lp5uLwNPAk85e4fJjlEs2ZmYwmmAACCzonr1uXeaP93P72lQe3Mj+rUgutHtI0gIqmvF18M5mUfMmRIhiORVOh+5Q7dq9yRLfeqsrKSjz76CIAjjjiCvLzGrXkqLXcem7udeYsTf9Gan0fGB+OK9Wtq2zb9nyGGDx/OsmXLWLRoUcJmc3XZtm0bbdu25aOPPuLwww+vWv+DH/yAxx57jOXLl0cSdybVds3qulfJrhdk5pql+vf39a9/nUWLFi1y96835Dxp+6t295Xufp+7nwrsB4wC/o9gMue+wK+B983sYzO708z6W0N+s5uuccAh4bKkuLg4s9E0UFMcDldERKQ5KmxhjBrUmjsva8+5/VpyVKcWHLp/Pkd1asG5/Vpy52XtGTWodZMdFfk3v/kNH330EdOnT2/Q/u+88w5t27atNgT8mjVrmDBhAmPGjGlyiRjs2TVLdL2g6V+zSEZTdPdNwFRgajhi4hDgPOAs4HDgRuAG4Aszm0HQz+w5d699QocmLLxmmwDMrKyxv/1Kl6Y2HK6IiEhz17ZVHsP7tGR4n6Y1v1NdevbsyZ///GfKy+vf4geCJnc1B6NYuXIlY8aM4Yc//GG6wswqe3LNEl0vaPrXLPKh7d29lGCusWcBzKwvQVPGc4AewBUEc43tMLPngacImjNujDo2Sb+mNhyuiIiINF970kdp9OjRjB49utq6vn370rdv3z0NK6s19Jolul7Q9K9Zo1e/uPub7v6LsF1lN+BnwGvAXgQJ2l+AHzV2XJIeTWk4XBERERGRKGW0LZy7f+Luv3P3AcABwPeAGQT9zCQHxYbDrY9sHQ5XRERERCRKkTdTTJW7fwE8HC6Sw0b2b8XaTRUpTfyczcPhioiIiIhEKbJkzMzaAJcBRwIFwEqC+cgWuvvnUZ1XMq+whTH6rDZZPxyuiIiIiEgmRZKMmdkxwHMEQ9wn2r4eWBi/uPuKKGKRzIgNh3tO35bM+bCEN/61lrLKfPbftx3dDypgQA81TRQRERGR5i2qmrG7gP3Dn18FVhPMn3UM0JogSRsKnBHbwcw2uvu+EcUjGRIbDrfl5mVA5ifQFBERERHJFlElYycDDnzX3SfHVoaTPHcDjguXXuFjR6BDRLGIiIiIiIhknaiSsXJgZ3wiBuDuDnwcLo/F1pvZgQRJmYiIiIiISLMQVTL2IXBsqoXDAT00qIeIiIiIiDQbUY2gMAloaWYDIjq+iIiIiIhITosqGfsLsAC4x8xaRnQOERERERGRnBVJMubu5cB5BPOLzTezHlGcR0REREREJFdFOdHT/sBHwNHAv8zsVTO7wcwGhhNCi4iIiIiINFuRJGNmNgiYR1A7FjtPf+BO4EVgo5l9ZGZ/NbMbzWywmbWLIhYRERERkfoqKSmhS5cujB49Om3HfPrpp8nLy+Odd95J2zGzia5Z/UVVM3YLUASUAA8BY4GJwLsEw97nEcw3dhFwB/ACsCGiWEREREREADj66KMxMy6++OJayz3wwAOsWbOGn/70p2k799lnn82xxx7LmDFj0nbMVKxatYrf//73nHrqqRx00EEUFhZy8MEHc8UVV7B8+fK0nacpXbPGElUy1otg0udz3P0/3f1Wd7/C3XsDewPHA/8J/AF4E9gZYSw5wczam1lXM+sKFFRWVmY6JBEREREASirKmLr0RaYufZHSivJMh9NgCxYs4IMPPqBt27Y8+eSTbN68OWG50tJSbr/9di644AI6deqUtvObGaNHj+Yf//gHb775ZtqOW5f777+fG264gX//+9+cffbZXH/99XTv3p0JEybQu3dv3n///T0+R1O7Zo0lqgSoDNjm7s/X3ODuZe6+0N0fcvdr3f1EoA1B37Lm7Dpgebh027BBFYUiIiKSHWZ+Np/xi59h/OJnmPnZ/EyH02ATJ04kLy+PcePGsXPnTh5//PGE5Z588knWr1/PJZdckvYYvvWtb1FUVMRDDz2U9mMn069fP+bNm8eiRYsYP348d9xxB7Nnz+b3v/89mzZt4sYbb9zjczS1a9ZYokrG3gUKzCw/lcLuXunuiyKKJVeMAw4JlyXFxcWZjUZERESEoFbskaWzq55PWfpCTtaOlZWV8de//pWBAwcyatQoOnbsyKRJkxKWnTx5Mq1ateL000/fbVtFRQXdu3enVatWrFmzptq2OXPmUFRUxIknnsiOHTsSHrtdu3YMGTKEadOmUVZWtucvLAXnnXceJ5100m7rR48eTatWrZg7d27C/ZYvX873vvc9unTpQmFhIWa22/LCCy8Aya9Z7HodcMABrF27ttq2VK4XZOaaNZYo5xkrAoZFdPwmx903ufsKd18BlOXlNetWmyIiIpIlZn42n/U7dzXnW79zc07Wjs2YMYMNGzZw8cUX06JFCy688ELmzZvH0qVLq5WrrKxkzpw59O7dm4KCgt2Ok5+fz9ixY9mxYwd33XVX1fqPP/6Yc889l4MPPpgZM2bQsmXyqXb79evH1q1bWbBgQfpeYAO1aNGCFi1a7Lb+3XffpXfv3kyYMIE+ffpw4403cuaZZ1Zt79y5M4cddhjHHHNMrdcs/nrde++9Vevrc70gu65ZOu1+5dPA3R81s4uBe83sTXdfW+dOIiIiIpJVataKxUxZ+gLDD+5HYX4kHyUjMWnSJAoKCjj//PMBuOSSS3jwwQeZPHkyt956a1W5RYsWsWXLFnr16pX0WCNHjuS3v/0t48eP5+c//zl5eXkMHz4cM2PWrFnsu+++tcbSp08fAF577TVOOOGEpOXGjh2b8uvr2rUrl19+ecrlAZ566im2bNnCeeedV219aWkpF154IVu3buXZZ5/lm9/8ZtW2yy67jClTpnDbbbcxatQoAN5///1ar9nIkSP59a9/zcMPP8wvf/nLel8vSP2a5ZpI/oLMbAawBDgZ+KeZfc/dZ0ZxLhERERGJRs1asZhY7dh5XU/OQFT1t379embNmsUZZ5zBPvvsA8BJJ51E586dmTx5MrfccgtmBgQjDwLsv//+SY+Xl5fHLbfcwvnnn89tt93GwoULWbVqFbNnz6Zbt251xrPffvtVO1cyt9xyS0qvD2DgwIH1SsbWrl3Lj370I4qKiqolowBTp05lyZIl3HjjjdUSMYALLriAKVOmsHDhwqpkrK5rlpeXx5gxYxg1alSDrhekfs1yTVRt4YYDo4F2wH7A02a2yszGm9mVZnacmeXOVykiIiIizUyyWrGYXOo7NnXqVMrKyqoNZ29mjBw5kk8//ZSXX365av2XX34JQPv27Ws95re+9S369OnDfffdx2uvvcaUKVMS9ssaPnw4Dz74YLV1HTp0AKCuAdvcPeUl/jXUZdu2bZxzzjmsXr2a+++/n6OPrj6O3rRp0wC49tprd9u3TZs2QNAXLCaVazZixAh69erVoOsFqV+zXBNVQnQ30JNgiPt9wnUHAt8PF4AyM/sAWBhb3D33GiCLiIiINEHJasVicql2bNKkSbRq1YoRI0ZUW3/xxRdz5513MmnSJAYPHgxQ1Xdp586dtR6zsrKyqrbmzDPP5IILLkhYbubM3RuHxQarqKufVBR27NjB2Wefzfz587nrrrv43ve+t1uZN954g8MPP5yuXbvutm316tUAHHbYYVXrUrlmlZWVdOzYEaj/9YrFHX+upiKqPmM3xH42s87AcTWWTkBh+HMv4AqCeclUWyYiIiKSYaUV5bXWisU8snR21vcde++99/jnP//JRRddxN57711tW69evejRowfTp0/ngQceoHXr1lUJQ6y2J5nrr7+eWbNmsd9++/Hcc8+xbNkyDj300JRi2rhxI0DVuZJJd5+xkpISzjvvPF566SVuueWWhEPaf/nll2zZsoWePXsmPMasWbOAoFlkTCrXbMyYMTz//PMNul6Q+jXLNZH/5bj7SmAl8FRsnZntQ/XkrDeQWoNREREREYlUXbViMet2bsr62rGJEycCVGuiGO/iiy/mpptuYvr06Vx22WUcddRRmBlLlixJesxx48Zx7733cs0113DRRRcxaNAgxo4dy+TJk6uVe/XVVxkxYgQbN26s6pMGwUiCAMccc0ytsaezz1h5eTnf/va3+cc//sHPfvYzbrrppoTlYnFu2bJlt20rVqzgySefpGfPntUG66jrmo0bN47x48dz5ZVXMmrUqHpfL0j9muWajIyf7u5fuvtsd/+du1/q7j0IJn4WERERkQwqrShnytIXUi7/yNLZWdt3rLy8nKlTp9K+fXuGDh2asEwsSYvNObbPPvtw5JFH8vbbbycs/7e//Y0bbriBYcOGcd999zFw4EAGDRrE1KlT+fDDD6uVXbhwIb17994tsXjzzTcB6N+/f63xp6vPWEVFBZdeeilPP/00P/rRj7jjjjuSlu3QoQOdO3fmvffeY9GiXdMAf/XVV4waNYodO3bwu9/9rto+tV2z2PU6/fTTueuuuxp0vSD1a5ZrohpNcV/gWOBtd989rU7A3ZPP9CYiIiIijSLVWrGYbK4dmzVrFuvWreOggw5KOBhFTKtWrXjppZdYuXIlnTt3ZsSIEdxxxx0sX76cQw45pKrc/PnzufTSSzn22GN57LHHyM/PB+Dmm29m8ODB3HzzzTz++ONV5RcsWFA1JHu8l156iT59+nDggQem8dUmd+utt/L444+z77770qFDh4TNH+PXjRkzhmuuuYZTTjmFSy+9FDNj+vTpfP7554wbN47TTjttt/0TXbP46zVhwoQGXy9o/GvWWKJqpngZcBcwBxgU0TlEREREJI3qWysWk619x2K1XatXr+Yvf/lLneUnT57M//zP/3DllVdyxx13MG3aNMaMGQPAsmXLOPvssykuLuaZZ56p1v9s0KBBDBw4kCeeeIJ33323qr/VggULuPnmm6ud48033+STTz5JOGJgVD799FMAvvjii92GsY+JT8auvvpqzIx7772X8ePHs/fee9O/f38ee+yxhCMgArtds3RdL8jMNWssUf3FnB0+3llXQTM7AViUag2aZLGyUqisTLgpr7w0+KEkySg7eXlQUBhRYCIiIpKK+taKxWRr7dgTTzzRoP26devGsGHDmDx5Mv/1X/+FmXHooYeybt26pPvUbCa4bds2Pvroo91qeiZOnMg+++zDpZde2qDYGmLixIlVfedSddVVV3HVVVelXL7mNat5vWr2QUv1esXib+xr1liiSsYOByqBF1Mo+33gcjM7z92fjigeaQxz/wFTH0i4aVDsh8fHJd730h/C4LMiCEpERERSNezgvgztdHyD9s2zjAxFEJnf/OY3HHfccUyfPj3pMOy1eeedd2jbtm21IeDXrFnDhAkT+NWvfkXbtm3TGW5W2JNrluh6QdO/ZlH91XQENrl77RM0BB4DDDgvoliksfQ/AzrsW//9OuwL/U9PfzwiIiJSL0X5BbRsUdSgpSi/INPhp1XPnj3585//THl5wwYnWbBgwW6DUaxcuZIxY8bwwx/+MF1hZpU9uWaJrhc0/WsWVc3YZqCdmZm7ex1l5xHMMfaNiGKRxlJQCMMuSlo7ltSwkWqiKCIiIlnnyiuvbPC+o0ePZvTo0dXW9e3bl759++5pWFmtodcs0fWCpn/NoqoZ+wAoAOq8cu6+DdgEfC2iWKQx1bd2TLViIiIiItJMRZWMzSJoevjzugqaWT7BHGOtI4pFGlOsdixVqhUTERERkWYqqmTsTwS1XeeY2a/rKNuToLnk+ohikcaWau2YasVEREREpBmLJBlz983A98Kn/2Vmfzezo2qWM7O9gd8T9Bl7I4pYJAMKCuHMb9ddbthFqhUTERERkWYrsjFI3f3/gEuBUuAM4F9mtsDM7jezW8zsL8BiYEC4S9Obxa05GzC09tqxDvsGNWgiIiIiIs1UpNOku/s0M/sIuBc4GTgO6BVXJDZ25a/c/aUoY5FGFqsdezRJjq1aMRERERFp5iJNxgDc/R1ggJmdAJxD0Edsf4JJoT8AHnb3V6OOQzJgwFCY9Ths/KL6etWKiYiI1Cl+vqWKigry8prWpMoi2ayysrLq55pzn6VT5MlYjLu/gfqFNS/JasdUKyYiIlInM6NFixaUl5dTUlJCQUHTmlRZJJtt27YNgMLCwqaRjEkzVbN2TLViIiIiKWvTpg0bN25k3bp1FBQUKCGrIVZ7EV+LIdkpV+5VZWUl27ZtY+3atUDwNxilPU7GzGwEsNPdn0tDPJEdUzKkZu2YasVERERS1rFjR7Zs2UJJSQnLli3LdDhZp6KiAoD8/PwMRyJ1ycV7tddee1FcXBzpOdJRM/Yk8G/goDQcK8pjSqYMGMrSRR8AcLhqxURERFKWn59Pp06d+OKLL6qaTckusWvStm3bDEcidcmle1VYWEibNm0oLi6OPHlMVzPFKBpSRtc4UxpXQSErj+oHwOGqFRMREamXVq1a0blzZ9wdd890OFnl5ZdfBuAb3/hGZgOROuXKvTKzSPuI1ZSuZKylmV2WpmM1S2bWHmgfPi3I9va0IiIi0rga+0NiLtFIk7lD96q6dCVjbYEJaTpWc3UdcHPsyYYNGzIXiYiIiIiIRC4dydhKIIo683URHDObjQMmhj8/V1xc3C1zoYiIiIiISNT2OBlz965piKPZc/dNwCYAMytTFa6IiIiISNOmT/wiIiIiIiIZoGRMREREREQkA5SMiYiIiIiIZICSMRERERERkQxQMiYiIiIiIpIBSsZEREREREQyQMmYiIiIiIhIBigZExERERERyQAlYyIiIiIiIhnQIoqDmtkp4Y/vufumKM4hIiIiIiKSyyJJxoCXgUqgY0THFxERERERyWlRJWObgQp33xjR8UVERERERHJaVH3GlgJtzKwoouOLiIiIiIjktKiSsWlAAfDtiI4vIiIiIiKS06JKxu4BXgfuN7NhEZ1DREREREQkZ0XVZ+wXwKvAMcAMM/sAmAesAyqS7eTut0YUj4iIiIiISFaJKhkbCzhg4fOjga+nsJ+SMRERERERaRaiSsZeJUjGREREREREJIFIkjF3HxTFcUVERERERJqKqAbwEBERERERkVooGRMREREREcmAqPqMVTGz3sDFwPHAfgR9ydYDbwF/dfd3oo5BREREREQk20SWjJlZa+DPwEWxVTWKnALcYGbTgP90921RxSIiIiIiIpJtIknGzCwPeAoYTJCE/Rt4EVgVFukUbjsQGAnsZ2anu7tGYBQRERERkWYhqpqxy4AhQBlwA/Cgu1fGFwgTtquBu8Oyo4DJEcUjIiIiIiKSVaIawOM/CPqG/dTd76+ZiAG4e6W7PwjcSFB7dllEsYiIiIiIiGSdqJKxnkAFQZ+xujwElAO9IopFREREREQk60SVjLUBtrr7jroKhmW2AntHFIuIiIiIiEjWiSoZ+wJoZ2b71VUwLNMe2BBRLCIiIiIiIlknqmTsdYJ+YGNTKHtLWHZeRLGIiIiIiIhknaiSsQcIEqyrzGyKmR1es4CZHW5mjwBXEQz28UBEsYiIiIiIiGSdSIa2d/eXzWwccB1wCXCJmX0GrCZIvA4mmGss5m53fyWKWERERERERLJRVPOM4e4/MbNlBE0V9wE6h0u8DcBYd1etmIiIiIiINCuRJWMA7n6/mT0EfBM4HogN6LEOeBt43t13RhmDiIiIiIhINookGTOzU8If33P3TcCMcBERERERERGiqxl7GagEOkZ0fBERERERkZwWVTK2Gahw940RHV9ERERERCSnRTW0/VKgjZkVRXT8jDOz75rZS2a23sy2mtkCM7s003GJiIiIiEhuiCoZmwYUAN+O6PjZ4FTgaWAYcBzBa55iZhdlNCoREREREckJUSVj9wCvA/eb2bCIzpGUmfUxs/8ys/8zs9Vm5mZW56iNZraXmd1iZh+b2U4z+9zMHjazTjXLuvt/uPvd7v6Wuy9197uAZ2naCaiIiIiIiKRJVH3GfgG8ChwDzDCzD4B5BEPaVyTbyd1vTdP5fwmcU58dzGwvYDZwEvBv4CmgK/Bd4CwzO9HdP6njMO2Bz+obrIiIiIiIND9RJWNjAQcsfH408PUU9ktXMvY68C7wVrisSWGfXxAkYq8Dp7v7VwBm9hPg/wEPAwOT7Wxm3yGYS+2HexS5iIiIiIg0C1ElY68SJGMZ4e53xD83s2RFY9sLgB+FT6+NJWLhsX4fJlqnmFkfd1+QYP9zgD8CV7n7wj2NX0REREREmr5IkjF3HxTFcSPUn6CJ4Sfu/k6C7U8AxwJnA9WSMTMbCUwErnb3iZFGKSIiIiIiTUYkyZiZnRL++J67b4riHGnWM3xMVqu1sEY5AMzs+8B9wOXuPi2i2EREREREpAky9/S3JjSzSqAS6JgNEz+bmQMl7r5Xku2/B64H7nb3nyTY3hP4J7DQ3fuE634C3AlcSzDYR0yFu69PMa4Pkmw6rEuXLkUPP/xwKofJCdu2bQOgdevWGY5E6qJ7lVt0v3KH7lXu0L3KHbpXuaOp3qsrrriCTz/9dJG7pzI+xm6iGtp+M7ApGxKxFO0dPm5Psn1bjXIAPwbygfEEoy/GlreiCFBERERERJqWqAbwWAoca2ZF7l4S0TnSKTbCR7Jqwt1GAHH3rnt60mQZtJl90Lp166OGDBmyp6fIGi+++CIATek1NVW6V7lF9yt36F7lDt2r3KF7lTua6r3a05q+qGrGpgEF5M4EyFvDx2RXs1X4+FWS7SIiIiIiIvUSVTJ2D8F8Xfeb2bCIzpFOK8PHTkm2d6pRTkREREREZI9E1UzxFwRzjR0DzAgHqpgHrAMqku3k7uma9Lm+3g0feyfZHlv/XiPEIiIiIiIizUBUydhYgv5Xsb5WRwOpjDCSqWRsHsGgI4eZ2XEJ5hq7IHx8pnHDEhERERGRpiqqZOxVkg+GkXXcvdTM7gf+m6Bp5enuvg2qhrA/Fpjr7pGNlGhm7QkmngYoqKysjOpUIiIiIiKSBSJJxtx9UBTHTZWZDQd+WWN1oZm9Eff8V+4+M+75bcBpwEnAEjObA3QB+gEbgO9GGDLAdcDNsScbNmyI+HQiIiIiIpJJUQ3gkWkdCZKo2AJBk8n4dR3jd3D3ncBg4FcE842dC3QFJgHHufvSiGMeBxwSLkuKi4sjPp2IiIiIiGRSVM0UM8rdJwITG7DfDuCmcGlU7r4J2ARgZmV5eU01TxYREREREciSmjEzO8HMTsl0HCIiIiIiIo0lLTVjZlYJ/NvdD0qw7W6grbtfWcsh/kbQbLBJ1tSJiIiIiIjUlM6aMUuyfiRw+R7sLyIiIiIi0uRkRTNFERERERGR5kbJmIiIiIiISAaoj1aW0KTPIiIiIiLNi2rGssd1wPJw6aZJn0VEREREmjYlY9ljHJr0WURERESk2VAzxSyhSZ9FRERERJoXfeIXERERERHJgHTWjO1vZhXJNta2jWCOMU9jLCIiIiIiIlktncmYJm0WERERERFJUbqSsVvSdBwREREREZFmIS3JmLsrGRMREREREakHjaaYJTTps4iIiIhI86LRFLPHdWjSZxERERGRZkPJWPYYhyZ9FhERERFpNtRMMUto0mcRERERkeZFn/hFREREREQyQMmYiIiIiIhIBigZExERERERyQAlYyIiIiIiIhmgZExERERERCQDlIyJiIiIiIhkQKMkYxbY18w6N8b5REREREREsl2kyZiZ9Taz/wM2A2uBZTW2dzCzP5rZeDMrjDKWbGdm7c2sq5l1BQoqKyszHZKIiIiIiEQosmTMzEYBrwPnAnsDFi5V3H0jcAjwfeCbUcWSI64DlodLtw0bNmQ2GhERERERiVQkyZiZ9QD+DBQA9wLHA18kKT6ZIEk7J4pYcsg4gsT0EGBJcXFxZqMREREREZFItYjouD8BCoEH3P06ADOrSFL2xfDxxIhiyQnuvgnYBGBmZXl5GltFRERERKQpi+oT/xDAgTvqKujunwPbAQ3uISIiIiIizUZUydiBwDZ3X5Vi+R1Ay4hiERERERERyTpRJWMlQKGZWV0Fzawl0J5gxEUREREREZFmIapkbAXB4B3dUig7DMgHFkUUi4iIiIiISNaJKhl7lmCExNG1FTKzYuBOgv5lMyOKRUREREREJOtElYzdDXwFXG1mN5tZm/iNZtbSzC4B3iYYyn0DMD6iWERERERERLJOJMmYu68FLgHKgJuA9UAxgJl9AHwJTAG6EPQvu9jdt0QRi4iIiIiISDaKbDIrd38GOAVYQDDnWAuCpos9gKLw53eAU9x9dlRxiIiIiIiIZKOoJn0GwN3fBPqa2bFAf4Ih7/OBNcA8d387yvOLiIiIiIhkq0iSMTPrDFS4+2oAd38PeK+W8gcCLdx9ZRTx5AIza08wxD9AQWVlZeaCERERERGRyEU5tP2b9Sg/D1gWTSg54zpgebh027BhQ2ajERERERGRSEXWZ4ygT1iU5ZuacQQjSx4CLCkuLs5sNCIiIiIiEqlI+4zVw15AeaaDyCR33wRsAjCzsry8KPNkERERERHJtIx/4g/7i3UkmGtMRERERESkWUhLzZiZnQIMqrF6bzO7qbbdCAasGBb+PD8dsYiIiIiIiOSCdDVTHAzcDHjcutbhuroYsBP4bZpiERERERERyXrpSsZWAK/EPR8IlAGv17JPJbAFeB+Y5O5L0xSLiIiIiIhI1ktLMubuk4BJsedmVgl86e6D03F8ERERERGRpiaq0RS/C+yI6NgiIiIiIiI5L5JkLKwpExERERERkSQyPrS9iIiIiIhIcxRJzZiZPdyA3dzdr0x7MCIiIiIiIlkoqj5jlxMMc29JtnuN5xauUzImIiIiIiLNQlTJ2GR2T7jitQOOBzoBG4BnIopDREREREQkK0U1gMfldZUxMyOoQfsDsMXdR0cRS64ws/ZA+/BpQWVlZeaCERERERGRyEVVM1Ynd3dgQpiE/M7MXnX36ZmKJwtcB9wce7Jhw4bMRSIiIiIiIpHLhtEUHyJo0vjDTAeSYeOAQ8JlSXFxcWajERERERGRSGWsZizG3bea2RagV6ZjySR33wRsAjCzsry8bMiTRUREREQkKhn/xG9m+xD0lSrIcCgiIiIiIiKNJuPJGHB7+PhRRqMQERERERFpRFFN+nxZHUX2Ag4GzgN6EPQZmxBFLCIiIiIiItkoqj5jE6l9nrGY2KTQk4EHIopFREREREQk60SVjK2k9mSsHNgIvAv81d1fjCgOERERERGRrBTVpM9doziuiIiIiIhIU5ENA3iIiIiIiIg0O0rGREREREREMkDJmIiIiIiISAbscZ8xM3s4HYEA7u5XpulYIiIiIiIiWS0dA3hcTjByotVRLpnYvg4oGRMRERERkWYhHcnYZFKbU0xERERERERCe5yMufvlaYhDRERERESkWdEAHiIiIiIiIhmgZExERERERCQD0tFnrFZm1hu4GDge2I+gf9l64C3gr+7+TtQxiIiIiIiIZJvIkjEzaw38GbgotqpGkVOAG8xsGvCf7r4tqlhERERERESyTSTJmJnlAU8BgwmSsH8DLwKrwiKdwm0HAiOB/czsdHdvtqMymll7oH34tKCysjJzwYiIiIiISOSiqhm7DBgClAE3AA+6e7XsIkzYrgbuDsuOIhgmv7m6Drg59mTDhg2Zi0RERERERCIX1QAe/0HQN+yn7n5/zUQMwN0r3f1B4EaC2rPLIoolV4wDDgmXJcXFxZmNRkREREREIhVVMtYTqCDoM1aXh4ByoFdEseQEd9/k7ivcfQVQlpengS5FRERERJqyqD7xtwG2uvuOugqGZbYCe0cUi4ikqKSijLllS5lbtpTSivJMhyMiIiLSpEWVjH0BtDOz/eoqGJZpD6iTlEiGzfxsPs+XLeb5ssXM/Gx+psMRERERadKiSsZeJ+gHNjaFsreEZedFFIuIpKCkooxHls6uej5l6QuqHRMRERGJUFTJ2AMECdZVZjbFzA6vWcDMDjezR4CrCAb7eCCiWEQkBTM/m8/6nZurnq/fuVm1YyIiIiIRiiQZc/eXCUYHNOAS4CMzW2Fm88xsrpl9CnwEXBzucre7vxJFLCJSt5q1YjGqHRMRERGJTmRD9rn7T4AfAxsJkrLOwInAScDB4bovgR+5+41RxSEidatZKxaj2jERERGR6EQ16TMA7n6/mT0EfBM4HogN6LEOeBt43t13RhmDiNQuWa1YzJSlLzD84H4U5kf6diEiIiLS7ET+6SpMtmaEi4hkmWS1YjGx2rHzup7ciFGJiIiINH2aWVikGSutKK+1VizmkaWz1XdMREREJM0iScbMrNDMOpvZAQm27W1mvzOzd81soZn9ysxaRhGHiNSurlqxmHU7N6nvmIiIiEiaRVUz9j1gOfCbBNtmAtcDxwC9gF8As8zMIopFRBIorShnytIXUi6v2jERERGR9IoqGTsjfHw0fqWZjQAGEMwrNhV4CCgL142KKBYRSSDVWrEY1Y6JiIiIpFdUyViP8HFBjfWXECRid7j7KHf/T+A6ds1HJiKNoL61YjGqHRMRERFJn6iSsY7AdnffWGP94PDxobh1U8LHnhHFIiI11LdWLEa1YyIiIiLpE9XQ9q2BHfErzKwrQZK20t2Xx9a7+zYz2wTsE1EsIlLDsIP7MrTT8Qm3vfLKKwAMHDgw4fY80yCsIiIiIukQVTL2JdDRzNq7+6Zw3ZDw8bUkcXwVUSwiUkNRfkHSbYUWvC20bFHUWOGIiIiINEtRfcW9MHy8EsDM8sKfHXgpvqCZdQT2BtZEFIuIiIiIiEjWiSoZm0gwKMftZjYLeBM4kaD2639rlB0QPn4YUSwiIiIiIiJZJ5JkzN0fByYA+QTD3PcGdgJXxzVbjLmIBDVmIiIiIiIiTVlUfcZw9yvN7GHgJGAT8EL8wB0AZlYIbAYmA3+PKhYREREREZFsE1kyBuDu84B5tWwvBf4zyhhERERERESykcaoFhERERERyYBIa8ZizOzrwPHAfuGqdcDb7v5BY5xfREREREQk20SajJnZWcBvgaOSbF8E/Le7Px1lHCIiIiIiItkmsmaKZnYT8BTwdYJh7isIasTWhT9buO1vZjY2qjhyhZm1N7OuZtYVKKisrMx0SCIiIiIiEqFIkjEzGwqMJUi4XgVOB/Z296+5+9cIJnk+HXg5LPNLMzsjilhyyHXA8nDptmHDhsxGIyIiIiIikYqqZuwn4eP/AoPd/YVw5EQgGEXR3V8ATg3LWNw+zdU44JBwWVJcXJzZaEREREREJFJRJWPHE0zk/BN392SFwm03hE+/EVEsOcHdN7n7CndfAZTl5WmgSxERERGRpiyqT/yFwCZ3X11XQXdfBWwECiKKRUREREREJOtElYwtA/Y2s8K6CppZEUEfsmURxSIiIiIiIpJ1okrGHiWo6boshbKjwrKPRhSLiIiIiIhI1okqGft/wFzgXjP7TrJCZnYZcC8wJ9xHRERERESkWdjjSZ/D+cQSmQMcAzxsZrcQDGO/mmBgj07AQKAzsDks+wvg1j2NR0REREREJBfscTJGMJ9Y0hETCYat70zQHLHmeoB2wJjwZyVjIiIiIiLSLKQjGXuV2pMxERERERERqWGPkzF3H5SGOERERERERJoVzSwsIiIiIiKSARlPxswsz8zONrMnMx2LiIiIiIhIY0lHn7EGMbPuwBUEc5Htn6k4RCSwZXslcxaV8MbKQymrzOfdp7fQ/aACBvQoom2rjH9vIyIiItLkNGoyZmatgG8TJGEnx1aHjx82ZiwiEigtd6bN3c5ri0uoqARoC8CGVeUsWlXOjLd2cPKRRYzs34qCFlbrsUREREQkdY2SjJnZCQQJ2EXA3rHVwGLgf4H/dff3GyMWEdmltNy555mtfPx5edIyFZXw6qIS1myqYPRZbShUQiYiIiKSFpElY2bWkWBusSuBI2Orw0cHvuHuC6I6v4jUbdrc7bUmYvE+/rycx+ZuZ9Sg1hFHJSIiItI8pLUjiAWGmdl0YBVwF9AD2AlMA4bGFVezRJEM2ry9ktcWl9Rrn3mLS9iyvTKiiERERESal7TUjJnZYQTNEL8DfI2gBsyBucBk4HF33xqWTccpRWQPzV0U6yOWuopKmPNhCcP7tIwmKBEREZFmJF3NFJcQJF8GLAOmAJPdfXmaji8iafbx52UN2291mZIxERERkTRId5+xe4GfuXtpmo8rImm2s8wbdT8RERERqS5dfcZKCWrFfgR8bmYPhCMoikiW2qugYU2GG7qfiIiIiFSXrmTsAODHwHvAPsA1wDwz+8jMfmFmndN0HhFJk+4HFjRsv4Matp+IiIiIVJeWZMzdN7n7/e5+HNAH+AOwGegG/ApYZmYvmtl303E+Edlz/Y8qIr+e7wD5eTCgR1E0AYmIiIg0M2kd2h7A3d9x92sJRlUcBbxC0IRxEPBQXNHTzaxRJp0Wkd21a5XHSUfWL7E6+cgi2rZK+9uGiIiISLMU2acqdy9x96nuPgQ4HPgNsDrcbMB0YJ2ZTQjnJlNiJtLIRvZvRfcDU/vT635gC0b2bxVxRCIiIiLNR6N8xe3uy939f4AuwDDg/4ByoD1wGTADWNsYsYjILoUtjNFnteGUWpos5ufBKUcVcd1ZbShoocE7RERERNKlUWuj3N2BZ4FnzWxfgkTsu8DXCRIzEWlkhS2MUYNac07flsz5sIQ3/rWWssp89t+3Hd0PKmBADzVNFBEREYlCxpoGuvsXwO+B34fD4F+RqVhEBNq2ymN4n5a03LwMgCFDhmQ4IhEREZGmLSv6abn7G8AbmY5DRERERESksajtkYiIiIiISAYoGRMREREREckAJWMiIiIiIiIZoGRMREREREQkA5SMiYiIiIiIZICSMRERERERkQxQMiYiIiIiIpIBSsZEREREREQyQMmYiIiIiIhIBrSI8uBmdhjwbeBYYB+goJbi7u6nRhmPiIiIiIhItogsGTOzm4H/Iah9sxR28ahiiYKZnQLcAPQCOgO3uPvYTMYkIiIiIiK5I5JkzMwuBW4On34O/CN8LI/ifBmyN7AIeBQYl9lQRKQ5KqkoY27ZUgD6V5xCYX6kjR1EREQkzaL6z31t+Pg08G13L43oPAmZWR/gm0BfoB9wIFDi7nvVsd9ewBjgYoLari+BZ4Gb3H1VfFl3/zvw93C/O9L9GkRE6jLzs/k8X7YYgGM+m895XU/OcEQiIiJSH1EN4HE0QbPDHzR2Ihb6JfBb4DyCRKxOYSI2G7iJoNbrKeAz4LvAwrD/m4hIViipKOORpbOrnk9Z+gKlFU2p8YGIiEjTF1Uy5sAWd/88ouPX5XXgVuBs4IAU9/kFcFK4b3d3v8jd+xH0C+sIPBxFoCIiDTHzs/ms37m56vn6nZuZ+dn8DEYkIiIi9RVVMrYYaGVmRREdv1bufoe73+zuz7j72rrKm1kB8KPw6bXu/lXcsX4PvAecEjZ/FBHJqJq1YjGqHRMREcktUSVjDxEMY39hRMdPt/5Ae+ATd38nwfYnwsezGy0iEZEkataKxah2TEREJLdEkoy5+58JBu+4NxwCPtv1DB8XJtm+sEY5EZGMSFYrFqPaMRERkdwR1dD2NwHvAgOAl8xsHjAf2Frbfu5+axTxpKBz+LgqyfZVNcphZnsDh4dPC4EDzKwXUOrui1I5qZl9kGTTYdu2bePFF19M5TA5Ydu2bQBN6jXlsryKcvDKhNt2btsOwMvPPZt4Z8ujUkOoZ8z8shWsL9u9Vixm/c7N/P6FyfQt6Np4QUlK9D6YO3SvcofuVe5oqvcq9roaKqpPVGPZNYmzETQDTGXM5UwlY3uHj9uTbN9WoxzA8cBLcc+vCpdPga7pDE4k3b72yb844u3nG7TvR9/4Jqu7HZfmiCQV5V7B3PKldZabU76U3i0OpoXlN0JUIiIi0lBRJWOvsisZywUWPiaL2WqucPeXE62vD3f/esKTmX3QunXro4YMGbInh88qsW9BmtJrymkD+sMn78DGL+q3X4d9OeKKH3FEQWE0cUmt/rZiHlve31lnuS2+k22HtdK8Y1lG74O5Q/cqd+he5Y6meq9at269R/tHkoy5+6AojhuhWPPJZFezVfj4VZLtIrmloBCGXQRTH6jffsNGBvtKoyutKGfK0hdSLv/I0tkMP7gfhWpSKiIikrWiGk0x16wMHzsl2d6pRjmR3Nf/DOiwb+rlO+wL/U+PLh6pVbIRFJNZt3OTRlYUERHJckrGAu+Gj72TbI+tf68RYhFpHLHasVSpVixj6lsrFvPI0tkaWVFERCSLKRkLzAM2A4eZWaKRCS4IH59pvJBEGkGqtWOqFcuo+taKxah2TEREJLupMwHg7qVmdj/w38D9Zna6u28DMLOfAMcCc939rUzGKZJ2BYVw5rfh0QdrLzfsItWKZdCwg/sytNPxCbe98sorAAwcODDh9jzTd24iIiLZKtJkzMx6AtcSDG3fieQDZAC4u6clHjMbDvyyxupCM3sj7vmv3H1m3PPbgNOAk4AlZjYH6AL0AzYA301HbMmYWXugffi0oLIy8RxQImk3YCjMejz5yIod9g1q0CRjivILkm4rtOBts2WLosYKR0RERNIksq9MzeyHwFvAlcCRBHN0WR1LunQkSKJiC+Hx49d1jN/B3XcCg4FfEcw3di7BfGGTgOPcve7JffbMdcDycOm2YcOGiE8nEorVjiWjWjERERGRSESSjJlZP+AeIB94EBgWbvqSoPbpP4CJQCnwBXAJkLZJB9x9ortbHcvEBPvtcPeb3P1wdy9y9wPc/XJ3/yxdsdViHHBIuCwpLi5uhFOKhAYMTdx3TLViIiKSpUoqyphbtpS5ZUs1WJHkrKhqxn5MUBN1j7v/yN2fDdeXuvuL7v6ou18BnEAw0fKvgIURxZIT3H2Tu69w9xVAWV6e+nlII0pWO6ZaMRERyVIzP5vP82WLeb5ssQYrkpwV1Sf+kwmSrHtqrK/WFNHd/wn8CDgM+GlEsYhIKmrWjqlWTEREslRJRRmPLJ1d9XzK0hdUOyY5KapkbH+gxN0/jVtXCeyVoOzfgDLgWxHFIiKpqFk7plqxrLZleyUz397BSysP5bkV3bj76S3MXLCDLds1+I+INH01p/xYv3OzasckJ0U1muL2BMfeCrQ1syJ3L4mtdPcyM9tOMHKhiGTSgKEsXfQBAIerViwrlZY70+Zu57XFJVRUArQFYMOqchatKmfGWzs4+cgiRvZvRUGLdI6LJCKSHWrWisVMWfoCww/uR2G+Zm6S3BFVzdhqYG8zaxu37pPw8RvxBc3sQKAd6R1NUUQaoqCQlUf1Y+VR/VQrloVKy517ntnKnEWxRGx3FZXw6qISxj2zldJyb9wARUQaQc1asRjVjkkuiioZey98PCJu3csECddNZrYXgJkVAveG2/8VUSw5wczam1lXM+uK5hkTkQSmzd3Ox5+n1ifi48/LeWzu9ogjEhFpXMlqxWLUd0xyTVTJ2DMEiddFceseAEqAU4FVZjaPoAbtPILBPu6PKJZccR2aZ0xEkti8vZLXFpfUXTDOvMUl6kMmIk1KslqxGNWOSa6JKhn7O3ALsCS2wt2XE8wnthXYBzgRKCZIxO5096kRxZIrxqF5xkQkibm1NE1MpqIS5nxYvwRORCRblVaU11orFvPI0tmqHZOcEUkPR3ffQpCM1Vz/NzN7hWAS6IOBzcBz7r40ijhyibtvAjYBmJnmGRORaj7+vKxh+60uY3iflmmORkSk8dVVKxazbucmZn42n/O6ntwIUYnsmUYfbsbdvwQeaezziojksp1lDRuMo6H7iYhkk9KKcqYsfSHl8o8sna2RFSUnNEr1iwX2NbPOjXE+EZGmZq+Chg0429D9RESySaq1YjGx2jHJDiUVZcwtW8rcsqVqQlpDpMmYmfU2s/8jaI64FlhWY3sHM/ujmY0PR1YUEZEEuh9Y0LD9DmrYfiIi2aK+tWIx6juWPWZ+Np/nyxbzfNliJck1RJaMmdko4HXgXGBvgtEVq31F6+4bCQas+D7wzahiERHJdf2PKiK/nu/Y+XkwoEdRNAGJiDSS+taKxah2LDvUnI5A0w9UF0lDWjPrAfwZKCCYR2wy8CzB6Ik1TQZOA84BZkYRj4hIrmvXKo+TjixizqLUR0c8+cgi2rbSYEAiktuGHdyXoZ2OT7jtlVdeAWDgwIEJt+eZ3gMzrWYyHZt+QAOsBKLq1fgToBB4wN2vAzCziiRlXwwfT4wolpxgZu2B9uFTTfosIrsZ2b8VazdVpDTxc/cDWzCyf6tGiEpEJFpF+cmbWxda8FG2ZQu1AshGySbpnrL0BQ2wEorq64IhBPOH3VFXQXf/HNgONPfBPa5Dkz6LSC0KWxijz2rDKbU0WczPg1OOKuK6s9pQ0EKDd4iISOYka2Kqybl3iSodPRDY5u6rUiy/A2gXUSy5YhwwMfz5ueLi4m6ZC0VEslVhC2PUoNac07clcz4s4Y1/raWsMp/9921H94MKGNBDTRNFRCTzktWKxah2LBDVqy8B9jIzc/daJ7kxs5YEzfM2RRRLTtCkzyJSH21b5TG8T0tabg4GqR0yZEiGIxIREdmlroFX1HcsENUn/hUEg3ekUrszDMgHFkUUi4iIiIiINJLSivJaa8ViNP1AdMnYswTD2I+urZCZFQN3EvQv00iKIiIiIiI5LtXpCDT9QHTJ2N3AV8DVZnazmbWJ32hmLc3sEuBtgnnGNgDjI4pFREREREQaQX0n6W7utWORJGPuvha4BCgDbgLWE84xZmYfAF8CU4AuBP3LLnb3LVHEIiIiIiIijaO+k3Q399qxyEaJcPdngFOABQRzjrUgaLrYAygKf34HOMXd625UKiLSXJWVQsnOhEteeSl55cm3U1aa6ehFRKSZqG+tWExzrh2LdCxJd38T6GtmxwL9CYa8zwfWAPPc/e0ozy8i0iTM/QdMfSDhpkGxHx4fl3jfS38Ig8+KICgREZHq6lsrFhOrHWuOIys2ysD+7v4e8F5jnEtEpMnpfwb8/THY+EX99uuwL/Q/PZqYREREahh2cF+Gdjo+4bZXXnkFgIEDBybcnmfNc1qn5j3LWhYxs/YE860BFFRWVmYuGBHJLgWFMOyipLVjSQ0bGewrIiLSCIryC5JuK7Qg7WjZoqixwskJaUnGzKxzOo7j7ivTcZwcdR1wc+zJhg0bMheJiGSf+taOqVZMRJqwLdsrmbOohDdWHkpZZT7vPr2F7gcVMKBHEW1bNc8aFslN6aoZW0EwV9iecJp3Td04YGL483PFxcWpTJgtIs1FfWvHVCsmIk1Qabkzbe52XltcQkUlQFsANqwqZ9Gqcma8tYOTjyxiZP9WFLSwjMYqkop0fnVgaViaLXff5O4r3H0FUJaXp291RKSG/mcENV51Ua2YiDRBpeXOPc9sZc6iWCK2u4pKeHVRCeOe2Upp+Z7WE4hEL501UU5QQzYReDWNxxUREQhqus78Njz6YO3lhl2kWjERaXKmzd3Ox5+nNvz5x5+X89jc7Ywa1DriqET2TLqSsVnA6cAhwFhgGTABmOTuq9N0DhERGTAUZj2evO9Yh32DGjQRkSZk8/ZKXltcUq995i0u4Zy+LdWHTLJaWn473X040Bn4BbAEOAz4FbDCzGaZ2YVmpq9pRUT2VKx2LBnViolIEzS3lqaJyVRUwpwP65fAiTS2tH1V4O7/dvfb3f1IYABBc8UdwBnANODfZnavmfVO1zlFRJqlAUMT9x1TrZiINFEff17WsP1WN2w/kcYSSb2tu89z9yuBA4ArgXlAB+CHwFtm9q6Z/djMiqM4v4hIk5asdky1YiLSRO0sa9hgHA3dT9Jny/ZKZr69g5dWHspzK7px99NbmLlgB1u2a05diHgoeXffTtB3bIKZHQZcAYwCjgHuBjoBP4syBhGRJqlm3zHViolIE7ZXQcMG3W7ofrLnNA1BahqtR6O7f0KQmD0KlDbWeUVEmqSatWOqFRORJqz7gQUN2++ghu0ne0bTEKQu8mTMzFqZ2eVm9irwEfBToBB4H5gd9flFRJqsAUNZ2msgS3sNVK2YiDRp/Y8qIr+en1rz82BAj6JoApJaNWQaguYqsmTMzPqb2cPAGuAvQH9gE/AH4Bvu3tPd/xHV+UVEmryCQlYe1Y+VR/VTrZiINGntWuVx0pH1S6xOPrJIw9pnQEOnIWiufcjS2mfMzA4CvgNcTjC8vQGVwPMETRT/5u5qopiAmbUH2odPCyorm+cvpIiIiEgiI/u3Yu2mipRqXLof2IKR/Vs1QlRS055MQzC8T8togspiafm6wMy+bWazgBUE84sdTjDx8y+Bru4+1N0fUyJWq+uA5eHSbcOGDZmNRkRERCSLFLYwRp/VhlNqabKYnwenHFXEdWe1adaDQmSSpiGon3TVjE0DHNgO/C8wwd3npOnYzcU4grnZAJ4rLi7ulrlQREQknUoqyphbthSA/hWnUJgf6WDGsgd0r7JbYQtj1KDWnNO3JXM+LOGNf62lrDKf/fdtR/eDChjQQ00TM03TENRPut9htgODgEFm9f42wt39sDTHkzPcfRNBnzrMrCwvT28kIiJNxczP5vN82WIAjvlsPud1PTnDEUkyule5oW2rPIb3aUnLzcsAGDJkSIYjkhhNQ1A/6UzGDOgYLg3RPNNhERFp0koqynhk6a7Bg6csfYHhB/dTjUsW0r0S2XPdDyxg0arURlKstl8znYYgXe8ut6TpOCIiIk3KzM/ms37n5qrn63duZqZqXLKS7lWWKiuFJAOb5ZWHwxGU7Ey8b16eRpttZP2PKmLG2zvqNYhHc56GIC3JmLsrGRMREamhZk1LjGpcso/uVRab+w+Y+kDCTYNiPzw+LvG+l/4QBp8VQVCSTGwagjmLUh/evjlPQ9A8X7WIiEgjqFnTEhOrcZHsoXuVxfqfAR32rf9+HfaF/qenPx6p08j+reh+YGpfYDT3aQiUjImIiEQgWU1LzJSlL1BaUf9+FZJ+uldZrqAQhl1U//2GjVQTxcZWVgolOymsKGH0Nws45YgWtU9DcEQLrvtmAQUVJcG+zZDq3EVERCKQrKYlRv2RsofuVQ7ofwb8/THY+EVq5VUrlhlxTUoLgVHAOS3aMqd4MB+36cHOvJbsVbmD7ls/ZMCGl2j79hb4a7hvM21SqmRMREQkzUorymutaYl5ZOls9UfKMN2rHBGrHUvSd2w3qhXLjARJc9vyLQxf+xTD1z6VfL9mnDyrmaKIiEia1VXTErNu5yb1R8ow3asckmrfsWb8wT7j1KS03pSMiYiIpFFpRTlTlr6QcvlHls5Wf6QM0b3KMQWFcOa36y437KJm+8E+K9R3wJVmnjwrGRMREUmjVGtaYlTjkjm6VzlowNDaP+h32DdIBiRz6ls71oxrxUDJmIiISNrUt6YlRjUuja+h90ojK2ZYXbVjqhXLDmpSmjIlYyIiImlS35qWGNW4NL6G3ivNO5YFktWOqVYse6hJaco0JFCWMLP2QPvwaUFlZWXmghERkQYZdkAvhu53DI++VsJrSypS3u/kbi0YdkDLCCOTmoYd0IuT9j6aX07fQUU9/uXm58GAk1tHF5jULfZB/9EHq6/XB/vsMmAozHo8+XQESp4B1Yxlk+uA5eHSbcOGDZmNRkRE6q3o9Rcp/cn3eWuxk+eFKS9vLq6kZM7cTIffrBS9/iJvj3sUr0j9PuV5IV5RyPwXlmY6fKlZO6YP9tlHTUpTomQse4wDDgmXJcXFxZmNRkRE6q//GcztNJyKvPo1PKnIa8GcdppQuFH1P4OPO/Rs0K4fFx6a5mCk3mp+0NcH++ykJqV1UjKWJdx9k7uvcPcVQFlenm6NiEjOKSjk44MbllR9vEbN0xtVQSE79zmwQbvu1Pgd2WHAUJb2GsjSXgP1wT5bJasdU/JcRZ/4RURE0mhn64a1bNhZ5mmOROqy1z4dGrZfgaU5EmmQgkJWHtWPlUf10wf7bKYmpbVSMiYiIpJGexU27F+rPuA3vu4HNewDfPeDCtIciUgTpialtdJoitmqsoLyrzZGcuj8Vu2wFJpBVmzfjKdpVEcr+Qqg6jXlt9wby6/7n1nFjq14RPO55BW1Iq+gqO4Ydm7Dy0ujiaFwL/IK6x5BrbJ0B5WlOyOJwVoUkr9X3SODVZaVUFmyPZoY8luQ37JNneW8ooyKHV9FE0NeHvmt2tUdQ2UlFdvrPxx2qlrsndo39bG/pZp/W+mQifeI3WLI4feIw/d1Fq2q//kO71i+233Ue0QYQ0TvESd0cWa87VRUpp4I5+fBgB7Jfy+y7T0iCnqPCOhzRCCl94gBQ1n6/ntYZQVdjusHaf79zOjniMrUR85NRMlYlirfuoG1T98ZybH3H/GzlN7M1z//Ryq2peePpfXq1QCs3fg6APue+n2K9q+7A/SX8/5K6brlaYmhpvb9zqf1YcfXWW7LO39n+/KFkcTQ5uhTaXvsaXWW+2rxPLa+PzuSGFod0psOJ15YZ7kdK//FpvnTI4mhcL9D6Hjaf9ZZrvSLz/hi9p8jiSG/dQcOOOdndZar2L45sr9NgIMu+W1K5WIx1PzbSodMvEfUlMvvEd0qW5PHVVSSn/K58qig+yfjWbu8ejKj94hAlO8RPVuczsLSnuAOVktSFm4/+cgi2rZKnohk23tEFPQeEdDniEBK7xEFhXx+QEf2+nQee/397rTHkMnPEeVb92wEdDVTFBERSaM2edvoVfh+8MTr6gcWbD+u8H32zoumVklqd2bLF+mSv7L2RAzAjO4HtmBk/1aNE5iINAtKxkRERNIs5Q/4GF3yVzK0ZTTfWEvdCqyc/9h7Ov3K3iC/MnFztnwv5/hWH3LdWW0oaKG+fSKSPmqmKCIikmaxD/jPbh/MP0uPpsJ2/3ebRwXHFb7P0JazKbA963Mge6bAyjlzn1c4/62pvNamPx+36cHOvJbsVbmDbjs+ottha2jXriUFLTQXnIikl5IxERGRCBRYOWe3fp7hm57mvU1HVPuA37loNUd3XKamidkkPw/r1JLhHz/F8LVPVa3e1L0T21p0BOoeJEFEpL6UjImIiEQo74Aihq74e9UH/IqiAtaceBSkMBqdNK5tXyumzadryS8pA4J7te3Ahs0bJyKSCvM6OxdLYzOzD4468oij3n0rfaOjxcvEkLRz5swBYMCAAUEMGpI2iCELh6R98cUXARgyZEj1GHJw2Op6xZCjQ9vX/NtKBw1bHUjne4TNeY786ROC8hd8F+9/emoxZOF7RNIYmsh7RLJ7lavvEVHIpveIZP+zqmLIkfeIBseQQ+8RLz7/D6y8JK3/r6piyODniJ7fOJFFiz9a5O5fb8j+qhnLVnn5Kb/hRiWVfzqp8qK9gdT/iVTFkMIfVtSCN5i632SilFfYMqU320hjKChK6Z9OlCy/ION/F5aXl/EYYNffUkP/ttIhne8RDY4hV94jTjuPpZ8sA+DwU89L+4Sneo8IpOU9Yg/vVba9R2SS3iPCGPQ5IpBfgGf4/3gknyPyUp/GJBElYyIiIlErKGTlUf0AODzNiZikme6ViDQiNVgXERERERHJACVjIiIiIiIiGaBkTEREREREJAOUjImIiIiIiGSABvDIEmbWHmgfPi2ojGgoWBERERERyQ6qGcse1wHLw6Xbhg0bMhuNiIiIiIhESslY9hgHHBIuS4qLizMbjYiIiIiIRErNFLOEu28CNgGYWVleCjPbi4iIiIhI7tInfhERERERkQxQMiYiIiIiIpIBSsZEREREREQyQMmYiIiIiIhIBpi7ZzoGqcHMthQVFbU57LDDMh1K2mzbtg2A1q1bZzgSqYvuVW7R/codule5Q/cqd+he5Y6meq8++eQTSkpKtrp724bsr2QsC5nZGqAV8FmmY0mjWGb5SUajkFToXuUW3a/coXuVO3SvcofuVe5oqvfqYGC7ux/QkJ2VjEmjMLMPANz965mORWqne5VbdL9yh+5V7tC9yh26V7lD9yox9RkTERERERHJACVjIiIiIiIiGaBkTEREREREJAOUjImIiIiIiGSAkjEREREREZEM0GiKIiIiIiIiGaCaMRERERERkQxQMiYiIiIiIpIBSsZEREREREQyQMmYiIiIiIhIBigZExERERERyQAlYyIiIiIiIhmgZExERERERCQDlIyJSDVmNtLM5pjZFjPbaGZ/M7PDMh2XSC4zs8fNzM2sb6ZjERGR7KFJn0UEADNrAUwGLgY+Ad4GugG9gX8DX3f3jZmLUCR3mdlSoCvQxt13ZDgcERHJEqoZk0iZ2ZPht8G9zex0M3vezDaZ2bbw5yMzHaNUGUeQiN0MdHf3ke7eB/gL8DXgRxmMTTLEzM42s3+Z2U4zW57peHKRmbUHDgMWKxHLfqrFzC5mVmBmO8J7Utfy60zH25zpXjVMi0wHIE1eT6ASuAj4MUFty4vAycBpwNNmdqS7V2YuRDGzgcC1wER3v7XG5nuAK4HTgZrbpAkzsw7AYwT3fQqgRKJheoeP72Q0CklVb6AC+FemAxEAOhK8D8W7DCgFptVY/2RjBCRJ6V41gJopSmTMrB2wKXy6ABjp7kvDbfsDS4A2QA93X5yRIAUAM3sFOBHo6u6f19i2P7AGWOXuB2ciPskMM7sE+CPQwd3LMx1PtjOzocCNQF+gDJgBXA98D7gT+Im73x2WfZSgJvp/3P3XNY7TH3gB2AKc6O6fNNqLaObCWsyNwAfufnSGw5EEzKwT8Bnwtrt/I9PxSHK6V6lRM0WJUs/wcRNwXiwRA3D3tcDC8OnejRyXxDGz7sApwFM1E7FQq0YOSbKAmS0EphL8fZaZ2foMh5TVzOwWYBbQH5gXLucBzwP9wmLxNWM3AeXADWbWNu44RwBPEdTMDFci1uhUi5n9jg0fVXOZ/XSvUqBkTKIUS8b+6u6fJdjeJnxc3UjxSGLnh4+dzWxizYWgmSIE3xY3O2Eb+FVmVmlmRyUp09vMtptZmZkNqcexbzazimTHzbAzgNeAhwj6DKbcv9PMvhb2G3ggquCyiZn9B0FytQA43N3PdPcRQC/gEOBbYdF/xvYJv5yaAHQARofH2Z8goWsHfNvd32qkl9DsmNlQM3shHDV2Q/h+1wHoExZZGJb7Xti/5cFajvVMWOa0xohdqj7gv5fRKCQVulcpUDImUeoVPs6oucHMjGCkvg3u/u/GDEp2Myh87At8J8Fydrh96W57NgPuXgbcBxhBk7NqzGw/grbvLQmaob2YynHDD943Av/r7ovSFnCauPt6oAfwmruvcfcN9dj338CfgP8Ma3qarLA59r0ETQrPdvdVsW3uvhz4M8HvznJ331Rj91uBncBPzOxA4BmC5O1qd5/ZCOE3S/WsxXwtfOxHAmY2DBgOPOnuL0QVs1Sj2pbcoXuVAiVjEqVYzdjCBNu6E9SMJdomjas3sN3dLdECPBqWW5DBGDNtPLAVGBUmXwCYWSHwf8DBwEPufl89jvkLgiaAv01noOliZocS1No0tLnWnQT/Y5r6oC9XEVyn+5J8sRT7EmO397owcfsD0B54FzgeuMXdH4omVGlALeaHBK0CjjWzvWocq5BgFNoS4IaoY5cqx4SPqm3JfrpXKVAyJpEws3zg68CasH9YTbF2+UrGMijsrL4vwTxiibYbu2rOXm6UoLKQu28mGOK/iGDUyZgHCEYGnVtjfa3MrBVBreN77v5uGkNNp94EHzI/aMjO7r4aeAk4L6wFbKrOCh9rjhQWE+sTmyyp/T3gBH+HE919bPpCk3gNqcX0YJSzNwhGn+5d45DXE7Tw+H/uvizyFyCx+TCPANaGtfeJynwzbDZ6cty6EWHT6bMS7SPpl+K9+tDM7qqxrtDMPjazmxsjzmygZEyiciSwF3F9JGpQMpYdOoSPW5Ns7wscCHwOvN4oEWWvcQQDLvzAzFqa2Q8JRslbCZzv7qX1ONaFBP2CpqY9yvTpDbwfNtNsqEeBAuDytESUnXpRe9J6fPi4WzIWftnxe4IEAILfL4lOQ2sxY+99VU0Vw2al/0PQ5/k3aY5TkjuY4D0labN5d38e+Adhq4Nw6pa/At9z92caI0gBUrhXBE2ET6ix7kaCLz/uiCiurKNkTKISa6L4zyTbjwsflYxlVmxui8Ik278fPv6luc8F5+6fAk8Q1GCMB+4GtgPnuPu6eh4u9u3sy2kLMP36sOd/ny+Hj8P38DhZycwKCJpb7/QE88SETVpjzd4S1Yz9niAxnwGsAy43s24RhSsNr8WM9RuLnwT6zrD8z9x9W3rCkxQUhI/J/mfF/BQ42cx+ATxNcJ+y+cuvpiiVezUP6BPWomFmBwP/DVzv7jsjji9rKBmTqKSSjG0BNGxzZq0m+Db+EDMrit8QjvB3GfAlQeIh8Lvw8TIgH/iOu/+zAcfpTzAPVUpNFM3s5bDZTX2WQQ2IK95x7GEyFjbd+gLoW/P3qykIaw03Au2SNMW8kWBgl7U1a2LM7CfAdcB84CKCb4Fb0PT72GVSLxpWizmfYKqBfgBh87dLgXnu/ijSmFYQTJfzDTObF46CuVfNQu7+L4Kk+9fA79y9WYzsmmVWUPe9mkfwHhkb6ONu4FV3f6rRoswCLTIdgDRZSZMxM+sK7AO8kujbZGk87l5mZi8B3yQYXvtOADPrTDAwRQFwlbs3y2HtE/iY4MN3B2C8uz9R3wOEtSUHAEvcvSTF3Z4l+MdWH2vqWb4ad9+v7lIp+YigX90RNM1O3AuBU4Fbzezq2HuamV3JrkEdqiW1ZvZtgsR+KUHfpR1m9geCb/MvMrPfuntTvFYZE1eLubm+tZju/pWZ/QvoFSbd9wGVwI+jjVpqcvdSMxsJ/D+CmspDE9WgmFlPYBjBl16aIzEDUrlX7v6xmX0BnGBm+xKM3tzsJltXMiaRcPehtWxWf7HscgswBLjDzIYS1Fh+k6DP348aknA0RWaWR9AHKtbP7rhaitcmluSknOC6++0NPFc2+DJ87JjRKKJzK8Hfz38SNIv6gODDxFEENSr9iPtwH/ZfmUxQYzg01rE9TMhuJ+ib+Gt2TSkhaRB+8bQR6GBm+ycYWCppLWbodYKatT8R/O3/2d31PywD3P0fBH3CEjKzw8PtDxK0/BhrZo+4+1eNFKKE6rpXodeAAQRfbvze3ZdEHliWUTNFyQQlY1nE3ecRfIM4n6AjbX/gOeBEd78/k7Flmd8S9Dn5J0Ftzwnxo3XVQ7vwMdmgKU3NlvCxXa2lcpS7v0rQ7+tfBFN2fJNgdNIRwJyw2DtQ1fT3SYImb2e5e81m2n8kaDp8lpmdGHnwzU/sf86t4eApQO21mHFi/cZGEDS9+u8oApQ9Y2YHEcwX96S7/zdBDXQ+Qa2zZKd5BE21WwO3ZTiWjFDNmGSCkrEs4+7PESRgkkA4N9HPCAZZOAc4g+Ab8hsJ/pHUx+bwsW09zv9fBCOU1sft7r64nvtEIZaEba61VA5z9+nA9ASbZhD3ITCc3LtDgnKx7TuBTmkPUGLqVYtZw2txP49NNlS3ZI6ZFRP8H3sb+AGAu281s18Dt5nZ+CS1npJZCwlGlL2huQ6GY+qyI43NzP5N8EG0rbtXZDoekdqYWV/gFYKWBEPcfV44GMWnBE3vjnD32oburXm8/YC1wCfufniK+7wMDKxn6IPd/eUkx4vkjT+cJLzmueYS9BnrqX5Qkmlmdj5wM8GXG18RfBC8BziF4MuVCxM1zTazYwj6PH4IHOvumoZAJA3MbApwkLsPyXQsmaJkTEQkibDJy1vA14Ar3H1C3Lb/AX4F/MHdf1DP4/6bYIj8tu6+I40hZ52wc3ZroH09BiwRySpmNgsYCpwezmMlIg1kZvlAMXA+cBdwXHPsKxajPmMiIgmYWUuC/j1fA+6OT8RCDwLbCOaG2reeh59D0Ey8oYOA5AQzO4zgH+6bSsQkV4V9yoYC05SIiaTFQIIRf28gqI1utokYKBkTEUnmYYJ5h/5Bgs7f7v4lMIFgBLZ61YwBM8PHwXsS4J4ws++a2Utmtt7MtprZAjO7NEG5w8L5YT43s1IzW21mE8zs0BROMyh8/HtagxeJmJn1MLOHzOw54M8EzZKvzXBYIk2Cu7/o7nnufri7z8p0PJmmZExEpAYz+29gJMG8YiNr6dv4e4KR8a5NNPFoLR4nGNDikj0KdM+cCjxNMJLmcQQTpE4xs4tiBczsGwR9ag4Fvgv0CB8PAd4Jt9fmEoJ5fiamO3iRiA0BriSYH2kGcGr4BYyISFqpz5iISAaY2d3AdUCfbJmvyMz+Duxw9/PDedUWEcyHdrK7V8aVM4JRJIuBoxIlq2bWiaA24Ql3v6jmdhEREVHNmIhIpvyWYDS3/8p0IHHaE0xGDPAN4AjgzvhEDMCDb/HuIphXK1nt2E+BSuCmSCIVERFpApSMiYhkgLuvI0hozg8nA84oM/sOQR+5P4arYn3CPkqyS2z9IQmO9TWCuZz+7O7J9hcREWn2NOmziEiGuPutBBPRZpSZnUOQhP3/9u49VrOqvOP499dhBrkOl6oTLIL0imLBxloNNUA7MYqCoa1ojYFpKymFqJXalCpmhmprsV6qMYpE0qHGC7GNKdLGQqmDZWixJCK2VWJTR26hrdxGKQNenv6x1uucnjnnnXPO+56zp5zvJ9nZZ++91rvXWnMy8z6zbr85Y8jkzn4+Yp5so/t7bObcN1Y9YKqFlCTpCcieMUlaxZK8CrgaOH/W8v3bgV3AGfNkPRN4HLh5eUsoSdITl8GYJK1SSc6jrXS4qaq2znxWVQ8BlwEXJDlhVr4TgN8CLuvpJEnSEhiMSdIqlOQi4EPAG4BtSTb048n9+cHAFcDngOuSbOz3NwLXATcCl/d0kiRpCVzaXpJWoSQ7gGPmePSNqjo2yRZg84z7N1bVqUm2AafMuH9pVW1ZrnJKkvREZjAmSZIkSQNwmKIkSZIkDcBgTJIkSZIGYDAmSZIkSQMwGJMkSZKkARiMSZIkSdIADMYkSZIkaQAGY5IkSZI0AIMxSZIkSRqAwZgkSZIkDcBgTJIkSZIGYDAmSZIkSQMwGJMkSZKkARiMSdITVJItSSpJDV2WaUqyqdft1Ak/5wfts7djOiWf3LTqLknaN+w3dAEkSVqkTcAp/edtU/rM/5zS5yy3TUy/7pKkgRiMSZJWvaraMHQZJEmrj8MUJUmSJGkABmOStAol2dbnQ21Jc16SW5LsTPKtJP+Y5DULzL8uycVJbk/ySJIHk1yf5CVj8u/o+TeNSbO1p9narzf1+VujYXqb55jfdeySGmSJkmxI8sdJvpTk4SS7kvxHko8keeY8edYneVWSjyX5cpIHer5vJPl4kufPkWdRdV9K+856NvPPd22S30lya5KH+v1TJ22HSSU5eUbdXzFPmp9L8u2e5p3LUQ5JmoTBmCStbmuATwNXAD8DFHAw8Hzgo0ku3Uv+dcDfAe8AjgceBw4DNgJ/k2TLFMv6KG1u13f69SP9eubxvSm+b6wkLwO+Bvwe8NPAAcB3gWcAvwF8Mck5c2R9I/AJ4NXACcDafv/pwK8CNyd5/aw8Q9X9SbS5ae8CTgS+PzvBBO0wkaraDlzTL/8gyZpZ5fpJ4FrgIOCqXj5J2qcYjEnS6nYhcCptYYhDq2o9cDTwmf78kiQ/Pib/BcDzgPOBQ6rqcFpQ8Rf9+eYkZ06joFV1dZ/bdXO/9a6q2jDruGsa79qbJM8D/pIWuH6YFogeUFUHA8cAH6QFqlcmee6s7PcB76UFvIdX1SG0AOY44H09zXuSPGeUYcC6X0gLsH6N9vtxBPDDwO0wcTtMw8W0IPSngB/05CY5CvjbXtZrgddW1T6zKqYkjRiMSdLqdjhwVlVdVVWPAlTV3cArgHtp/06cPSb/euCCqvpwVe3q+e8CXgl8vqd5x3IVflqS3DfmeNYcWT5ACzLeVlXnV9VXq+p7AFV1Z1VdCLyftlDWJTMzVtXlVXVRVd1SVQ/1e1VVX6+q36YFMGtogdDQDgZeXVVbZ/x+3F9VD/TnS26HaaiqrwBb++XmPqTyMOCztGDwJuDsqvrutN8tSdNgMCZJq9v2qvrc7JtV9RitZwFaz8h87gL+bI783wfe3i+fmeTZkxZ0mT11zLF2ZsIkJwI/Sxsy+O4xn/nn/bxx9hC6vfjrfv75ReRZLv9aVZ+Z68EKtMNCbaYN43wGLYD9K+DZwJeBM0ZB5GxJ7knyqWUojyQtmEvbS9LqdsuYZ/f28xFj0mwbM/zr87S5Q/sBz6V9Od4nVVUWkXwUJP0QcEcyb9ZR4HEQcCTwX6MHSY6jDfE8DfhR4BD2/A/SH1lEmZbL9jHPJm6Haaiqe5K8nzYn7L399g7gxaOex9mSPAU4CvjSNMsiSYtlMCZJq9u3xjwbDe1aOybNPfM9qKrHktxP6116yhLKtq86qp/X0Oq2EAeOfkhyFm0Bj/1nPN8J7KItoLKONnz0oIlLOrlxgdNE7TBl7wN+lxYYPgC8qKruHZP+v2nz9L4zJo0kLTuHKUqSJrEaF0UY9fR8taqywGMHQJIjaXOc9gf+nrZ4yoFVtb6qntoX6ZhzmfaBjFuhccntME1J9qOtBjr6TnMgbdjivPocvV2j+W2SNBSDMUnSJOYdSpdkf9qwNNizh2XU6/akMZ+9foJyLaf7+vm4JIvtvTodOBR4kDaf6cY55jRtmLSArEz7TtIOU5E2NvIjwMtovV1fp9V57JYMSa7se+L5PUjSoPxLSJI0iVMy/2ShF7J7OPyts5492M9Hz5Wxf0mebyn00V5Xi5nnNU2jeVTrgLMWmXdU3zuq6n/mSbNxTP6F1n2S9l2oSdphWt4JnAt8G3gp8JZ+/9y9bDZ9EnB7X2hGkgZjMCZJmsTTaV+G/4/+Zf/N/fIrVTV78Y7RwglnzRPMncv8vW47+/mwxRV1am4Fvth//sMkTx6XOMnMBVAe7uefSLJHr1WSk2ibQc9noXWfpH0XapJ2mFiSNwFvos37+uWq+mfgk7Q90NYwz5YKfVjjs4DbplkeSVoKgzFJ0iQeBj6U5LxRcJHkaNoCFaf1NG+ZI98n+vl44Io+l4okhyZ5I3A5bSGGufxLP5+e5GlTqMOi9NUjzwceowWjtyT5lSQzF+l4WpLXJLkeuGxG9utovVtHAB8blT/JuiRn9+fjFlVZaN0nad8FmbAdRs+PTVL92LLQdyc5h9YrVsCmqrpuRpne2pOdmeTkObIfT5uzd9tC3ydJy8VgTJI0iQ/SekiuAHYmeQC4k90bRb+9qj49O1NV3cDu/adeC3wzyYO04XXv6Z835/5WwFW0lQd/DLizb8y8ox8rshx8VX0BOAO4n7a/1ado9f9mkkeAu4GPMmvIYVV9DfiTfvlLwN1JHqINs7u6n18/5tULqvuE7btgS22HSSQ5HbiSNlTzoqr6+KwyXcPuLRv2CABpQxTBYEzSPsBgTJI0iceBX6QNSbyD1uPwMHAD8NKqeuuYvL8OvIH2pfhR2r9J24FXVtXr5svUA5rTgGtoizYcCRzTjxXbsqWqrqcFRb8P3ESr92G0nq9/owUMZwKvm5XvYuAc4Au0eq8F/h34I+A57N7fba53LqbuS2rfxVpqO3Qze/f+aW/vSvICWsC3H3BZVf3pPElHvbEnJ3n5rGcn9bLts/veSVo9Mv9enZIkzS3JNuAU4NKq2jJsafT/VZJLgLcBN1XVC1fonTcAR1XV8SvxPkkax54xSZI0lF/o5zePTTVdJ+IQRUn7CIMxSZK04vo+dC8APltV/7BC7zyaNrTztpV4nyTtzYqNrZckSRqpqseAA1b4tSf2820r/F5JmpPBmCRJWhWq6lqG2yxckvbgAh6SJEmSNADnjEmSJEnSAAzGJEmSJGkABmOSJEmSNACDMUmSJEkagMGYJEmSJA3AYEySJEmSBmAwJkmSJEkDMBiTJEmSpAEYjEmSJEnSAAzGJEmSJGkABmOSJEmSNACDMUmSJEkagMGYJEmSJA3AYEySJEmSBmAwJkmSJEkDMBiTJEmSpAH8L2qXk750QXvIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4), tight_layout=True, dpi = 150)\n",
    "xlabs = [r'$ \\eta$', r'$ \\theta$', r'$ X$ $(=\\frac{f}{2\\Omega})$', r'$ dx$', r'$ dy$', r'$ \\tau_x$', r'$ \\tau_y$' ]\n",
    "ylabs = [r'$\\Delta(x_i) = 0.5\\sigma(x_i)$', r'$\\Delta(x_i) = 1\\sigma(x_i)$', r'$\\Delta(x_i) =2 \\sigma(x_i)$', ]\n",
    "\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1)\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2)\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3)\n",
    "plt.hlines(np.array(list(mev_ds_mae.values()))[-1], -0.5, 6.5, linestyle = 'dashed', linewidth=3, alpha=0.6, color = colors['11'])\n",
    "plt.xticks([0,1,2,3,4,5,6], xlabs)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.ylabel(r'Mean Absolute Error $[cm/s]$ ')\n",
    "plt.yscale('log')\n",
    "plt.xlim([-0.5,6.5])\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'Mean Absolute Error $[cm/s]$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "likely-ontario",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0699dfdbb0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BPhLd5mb7NZvBe7BZDGluK/r1cax898Km8X6TaflsnPdM9pnRscjGm5Zbxi/EcvkBhT6T9Iuw42kSW5n8FXEn6YkU13k86sV5VJNlu6nhR8IGSYe08fNp1dgknRm1/QvSWG9b+QFRnKQJYZ/3qLg+CmJ6Ns31ngrWq5K0UL7Z9+3yo88eJN//6qmoY/VqluN+Jmrbuycp2y+q7N2teCx/FLWfhVF/vy9p5zY4l5Oj9rlUftq5I+S7Cfxevjnlt2r44b8x1pdSsK1Q3+PZeC7yLXoi27g4yf4iF38qJfXM0nPYSdL+MW6PRMV1WJwy27T26yUfYpbUPWq/14RwPM6L2n/ktlJ+doTox96XVBSsU6KGz9XI7VRtfcnz74L/1ynGhUNJbwfL7w3+Hx+1jZjJc/Dej5T5SD4BP0F+3IPj5H9obwmWr1KMz135WRlq5Ud2v1L+u+pg+c+Ws+TH0aiJin3PFF4bzwf3T0s6W9J3JR0pfwGwOli2WVn4bJF0S9R+e0Y9flDU41en8bp+Mbh/Qb7P+QD5hPqOqONQozgXEtT4u+6h4BgeEcTzPfnWVo9Iqoyxrqnxd80H8mOGHCLpcPkxRL6IWn5HnBiGRJU5L8by6NdWjyTHZm5QbmWTxyvkP+d+F7WtH6n55+AeTdZr8Ws2g/fg3Ba+xqJfH69Gthm8Pr4r37X11y0o3+Lznu4+Mz4WLd0Atxa9EK8N3hR/lVQWdjxNYjs46gV4bYrr7KuGD9UFUY93lvSWGn6Exr163IrPp1Vjk+9fGTlef0ljvWOj1svKj/MsHCuTTzqcpNlprrs26vm8L2mfGGXK5EdydPJXECuyFHf7qC+b9xX7h3z0LTqpHdeKx7NEzX8gf6UWXE1OY9+XRe1zsqR2McqMaBLbCwm2F9p7PFvPRb42ObL8vgT7i65p/+9sPY8E+3s52Neq1t5Xvscsn8xHzs2dITzv85q8zlzwHi+O8V4/KVjnjBTLO6WRPMdZP5Pbea1wnHpEbT+SPHdVQ8uRHzQpf3hU+SODx8ZHPdbsc0X+ok1ke3dLKo0Ty2HyF3OdpLtiLG+n5AnUwVHbiHnBNcZr44Y45X4SVeamFh7nUkmfB9t6Psby16Jec3FbEcaI/UEFF3+alDsx6pi/p+YXKKN/D/05SexdYjx2btT6r0jqEKNM1yav/cExygxJ9PpWFpLnOMcu4fs3W6/ZDN6DLyv576L9JfVK8fVxe5J9p1u+xec93X1m/J5rjY1yy/+b/FWayIvv0jTW+2vUeiPla1YjVzBr5AfCCus5tVpsaphqyEl6Ko31fhGss06+GXFLb2k1GY8T06Co55Lwi6/Jet2j1qtUguRQ0n1RZXfK0vk9psmHZjq301r5tffbJsfmyDZ4vfdQw8WE5xWnNjkoG11LkOwLrs3f49l+Lmr48l0WZ3mJGi4GrJTUvpXPVbEaWqA83tqvjXyPWf4C3/rIOVSMH/itvP/zYnyGrAyW/abJ408Hj89v8vivm7wWo29D0ogl1vqZ3M5rhePUI2r790c9/kT0sYl6/O7g8XejHhsftY1YyXOkpuo9xUlCosrerIbP4Ize0/I1gk5xurM1eW28Hu+1Gbx/1gTlXm7hcY5u8dCsGbh88+C4xzBO7GsldUxQ9p6osic3WRZ9EWRkBs/n1WDdWiX+HXFc1H7+EWP5kESvb4WXPLfZa7bJezDVWyrP8QPFuIDdwvItPu/p7jPTW0EOGIasiB5o7as01rtKDSOh/kG+z8wh8i/kHzvnHo63opmdG3Ty/226wbZBbMOD2GKOiOz8VEiVwb/pDFLXIer+3SzcokeQTpl525sf5Ou+4OE6JRgoLob+UX//1Tm3LEHZyNRrTv5HcDZ8twXrvpKlGJoxs93lmxdHbFTzkfVbwwT52hQn6SLnXKI5wF+P+ntJ3FJexu+jFsj2c1kQ3PeyJoM6BX4pX8suSb9yrT9d2z7yg8pJjWd4yGWhxez8r6TIZ9M+kqaaH0QuFwbXuUuNZykYambnyjdZjdgknyhmw7HyU2i29PZIluJJxeTg/ujIQE1mViHf/Sl6eUJmtq38D2lJesg5V51klbnBfZmSfF8E34k7m9neZrZ/5KaGgSY7yScmiUxzztXFWhB8hkVmAtgryXaSiQwGtlENI5pHmy7fTDy6bDIPOufWJVj+16i/hzVZ9knU3+eZn7c7JWa2k3yXKsk3M477O8I596R8v2pJ+p6lMABk2FrzNdvGHnDOpTPYYcLyrXTe040xZSTPiKc86u+v45Zqwjm3Rg1Ta+0vXxso+R+gU5KsHvlgyCgBTKYNYotcZKhIUKapeNMotRkzO1k+Uf5afuTnPeWviJ7lnFuYxqYOivp7epKy3YP7j51zlQlLpq5X1N+9nXOW6Cb/HCXpA5f9EfQl1c/lPEe+qXBEZ0kXtcb+ovZbId/HTPJjFixNskr0j6SEyXML30dpa6XnEkmeTdLAJvvbVf4CgST92zn3f2mEm6l+UX+/1gb7y4Z+UX+/FsL+L5UfjGmj/CBxr0uqzMpIqi3g/Awd9zd5uGmiPM1FzeTRwv0td869mYXbN9mIJ0WPyH/fmBqmdzxd0nby30X3xVyruf7yrUQk6TcxRjhudJP/fotodpHbzIrMbIyZ/Vv+ou4a+QGb/hN1uzpqlS5J4kv2WRUZCbtDwlIJmFlXNU7Gvm1axjn3pRqe+ylm1imFTb+YZPkSNSTkfZvs70NJ/47sT9JKM7vVzE4LPl8TOTDq71R+f0TKVEjqmUL5sGX1NZum55L9LgpuPVLYVroVAMnKt8Z5b7VKCpJnxBN9NWybuKVimyTf7CLieufcxBTWu1F+3tvn09xfOjKN7a/ysf0jQZn2wX2yK4nRIglclfwPh9IW3nZOY9/xOPmLBP9JVrCJSM3zZiWvye0X3L+W5j4S2SPq74/ilpJkZr3U8IH770RlM2UNcznvFzz0FzVcYLnczNJ9X6XjGDXMf5tK8hf5Ut4i31w5mUzfR5lojecS/eV8aJNlN8u/F6vVuMVAa4r+8flaG+2zpUKN2Tm3Rf674oO23ncKbm3yf9Oat6bLtyrBuftb8O8PzczUMLfzv5xzn8Res5mdWhBGdAWBzKyD/ICTU+U/c7ZNdxsxbEqyPFIr3ZLf4udFrT8lQblIbX6Z/MWmZD5LtDCoMY0k/7EuIpwj6cng713lP0sfkvSJmb1nZrebWd8Y60VPBbYmhTg/jbNursraazZk6bRITaV8a5z3dGNMWc43cUBooq+Kp/yBFDSbu18N80NLzX84xJTGF2ZGWhjbGiV4Q5tZsaTtg3+/SSOsF+S/QNvJj1r5WJLyreFp+at+neX7CF0paZR8c8P+aZyXSM3za4ma1ZpZdzW8phbHK5eByI+dTSk0sz0r6u+mtUQtFvwYnKyGuZwfkq8pWyPfBHkX+eZzd2R734HoVgCLUigfmXP1zWTNyFryPspQazyXN+RHBN9Ovsm5JD9np/youJL0J+fcO2lFmrl+wf0GNVxQy3X9gvtQYjazn6ih6egbkv5HvqYv9NY8zrm3zOxp+VGFm3oqhdYTKQsuBLbLwqZWtXHt873yI+n2kB8868iox1MV/Rv2vyXNTGPdVU3+v0XS4ODvFyTdKX8ReLX8d0qNJJnZ+fL9faWQ5xgPvmd+FPXQs/6hpC6QnwUjEZdKCPHKBrXdw8zsYPlR5Y+Ub8FXJt+67RJJl5ifB/6yoCtG9DZTjSHfZPM1G6ZEXacyKd8a5z3dGFNG8ox4opuxdk5lhSCBfEANPxi+kLSjpJ+b2R3OuZUJ1u0n38RiunPuB5kE3Iqx7SXfn/hx59yJcYptr4Y3f9xtNeWc+8LM5koaKj83bZsnz865DWqoZX7OzD6UT/x2kv9ivjbZNoL+KpHmWMkS4ugJ7rOZPEc+z7YkKhS8Fs4L/n1XfiCfbLte/sq75JsIj3HO1ZnZrZIul2+m9xsz+2sKfZ4ysUvU358nKhj049s7+Ddhk+2WvI9aIOvPJTgXL0r6voLkOeg3FflBuVr+h01bidTAvBH1IzIuM3tMflqTHzvn7mmy7KeS/le+2eXQVuyvnW7Ml0q6TX7k8qtiLC+Tn6aos6S9nXNfJNnklcH9V/LTpiXqnxmGPyt28vznLO/nX2roBtMSP1Limsuscs4tMbPX5V9Hf5b//vxK0qNpbCb6NVLunHszk1jMbDtJkd8dC+VfT/F+eOdS7ebR8olouvoFF8YTfd7vkmCZgn7Mkebfa+OVc869rKC7W9Aa6xBJJ0v6qfwF77GS3pFvmSVJX0atnkoz5egyceOII7o/erLa/3S64yWSlddsAWrL895iLW62nay9PrfWu2XjBZDAG1F/75OscHAF9G75/i2ST7gitXtl8gMLJRLp7/B6wlIZyEJs/VKIrXfU3+k2d/6f4H6ImaU6mEdrelANXyqxmlXFEj1YWDrJc7LBqdIRacazbXDO4zlbDT84JqTywz8dZnaxGn7YL5efqqZSkoKanUhtczc1/GDLtvZRfyf7sXd51N9xz10W3keZyvpzCUT6Pe9gZj3lR76PNLG/IlbfwdZgZp3V0N3ijURlo/w/+avz/2VRg6WY2bHyr6/3JJ3YWolzhjG/ENwPjLP8cvmuF9cmS5zND7oT6XbxRA4mzpI0W81r5N+T9M8QYslVkabEkS4s04Mm3al6VQ3fVcNbEEcvNXQNeTBRyynFf/2G4fyovy+S/25LdPtZnHVjadqdpan+amjxkNLvNudclXNunnPucvmB7iLOifo7+vPkMCUXKbNR6beA2RD1d9xKIjMrUuMxVWJJ9XdEtl6zhaYtz3uLtTh5TrHzObdWuGXjBZDgvK5WQ7/RQxKVDfyPGmrz/uKc+3/OuWflr4pL0tlmNiDmml4kSct68pzF2F5LUCb6zZ7OIFtyzv1TfuoOSbrVzFI53q0m+MEdqeFLZWARKb3kOVL2U+fcpwlLpifSxLZUcZJ+M9tB0k3Bvy/L16JmjZmdKF+7JvljeLxzrulV0VvU0Bfut8EXc7ZF19AeFK+QmQ2W9MOohxJdzGjp+yhTrfFcpMbv01Pkpy2R/KAqWX1dJNEj6u+U+u86516TNEv+ItAYqb7W/e/yA6Yd73yzydbSI+rvVPscvy4/HsLBTReY2XckjZMfQTWV/sDRn0utMthfSzk/yvKt8i1hIrc/uzijL7dgPz2y9JtiSjbjStF0+QQmcnxSGmU7wjn3lRpGI+5jZqdnGEd0K8y4NYzmuxyNzHAfWWV+MMpTg39fdc79r3NuRpLbXyRFajpHW+JxN8403w88np9E/f1k3FJxOOdeUEPyumPU45+r4bfW0ea7JcRkZt9XQ0ujp13QtD4N0UlXs8+lKCcp+aBu0QOfxh31P4uv2YLSxue9xfJywDAzOzOqBvaEFMr/j5lVBl/Q2YxjbzOrMbPfZXO7LWFmU6KOzXkt3Nyc4L5Pog9RM7tK0mXBvzPk+3ZGjJO/ImdqSFpiqW8CGGcfGT2vLMXWL7h/LUGZyFXajZLmpRpflB9J+lh+QIjnzeznSWpPW1vk6n+qXTsiSc1mJR9hNJJgxU1uMjzf0YO5Xdl0YVBb9rh8c7QNks6O90M2k/2b2UD511ixfHJ8gnPu/ablgqTmf4N/e6lhepZ4283kWEQnhr+zGFP4mNl35QfgirzOahSn1USW3kc58VyivKiGPlHXyfd/rlWKg4Rl8bM2ur9qOonVVUH535kfcXd2sK2Rzrl3Y60QZszBD5xXJHUOavqj3SjfhPMKl9rUItH97VOeiiTL349JOedudc5tE3W7LflaWw/n3JfOuQ5RxyeT0XHHq6HW7y4zOyJBWZlZ1xgtvN5Vw+v4B2bWbGAm8yNUz1R2+pdnwzlqaJUzI431ImW3V0PyHcsOkibFurhrZiPU0Nf6AzXpbmZmBwXfh3GZ2SD5z1ypec3hn4L7IknTgpYmTdffVQ3fo5K/KJ2uBWr4/PhFnPO+hxouiCcSPTZMslrq8Wr5a7YQtdV5b7G8TJ7VeKTAhKMGmlk3+YEJ7s5yLZeCHyh/l++7mEv9YLIlMl9gkRqmo2nEfD+2CcG/T0g6Nzohcb5PTWQ7RwcfurEcKOlz5wfmyoosxtZXCQbFMd/3Z0jw76MuvWZnkVjWyPd7fk/+y/lWSa+Z2aVm1ifJFeJcEKlNft0lHixsdzVcZc5mf2fJj7wbmV5jlJk9YGZDzewQM/ul/IWZQ+UT21Occ1lr6mNme8r/gCiXT8BGOd/XK56b1HCB4r9a4ULJbDW0HOkjaZ6ZnW5m/c3seDP7i3xS2lENV8yXuhjThmXxfRT6c4nmGvf1jwx4drtzLt1uFy0VXXN6oZmdaH7O4h6JVnLOvS0/YnFP+aaAu8v3rU+r5UuGMopZMZpum29p8wP5GoREsxkAzTjn5kn6r+Df7eXH7fibmZ1lZgeb2QAzO87MrjA/BdVKNR5kK1IbGHnt7SvpRTP7YfDdMcjMfi3/WTFQmV0cbw3RyVQ6g049GPV3oqbbi+Sbes8zs1HB5+1QM7tNfqqxIvkLDj+JUfPXV9IiM/uPmV1vfoqqQ4NtDDOzG+U/1yOaDl42LWr5QEmvm9nFZjbQzA4zsyvkP/MiM2zc6Zx7LrWn3yBoFRYZMLR38FzPDJL/IWY2Qf4if6l8F6xElkiKdPX5TbCdfc1sr+C2W9R+W/yazVCFRc1ZnuSWsM97K2mT854Vzrm8usn3S9giP6LxxuC2bYLyf5V/g/dopXgOkr+CdH3YxyaIZ2YQj5Ov+WrJtkz+qqKT9I8Yy38QHFsnfwWvPM529paf9sXJTx1T3GT5d4Jl/8rW88pibJ2DZfMT7OuUqNi+38Jj3lH+qlpN1DbTuX2ZpdfRymB7c1OMOXKsb09S9uSoWE/K9utYvn/+qgTH511Jh2bzfSR/hX55VPmLU4z1zqh1RrbCsRgkf9En3rGolG+GXRn8P7m13ke58FzibPf2qG18JqljGjFl87P20RjP6b4U1usfVf43uR6zGj4rbwn+N/kLXjWSDkhj3z2i9jm+rc9Z8Fpr+txXZritlTG2NaQl5yZXbk3O0/0ZbmN81DaOSVDuR/JzM6fyPTk7xvo7S1qWYJ06Sdc0OffNzlOy5U3KTomUTfOY9IvaxwsZHNNXop7THnFi/758kpzoM/cHabw/Yt22SLokzjbay1+UTbR+pGtEvO+bIVFlz4tTZnv5xDfePj6Vb9I9V0ne5/IJcbztzM32azaD92A6t4kteW1nUj4b5z2TfWZyy8ea5zPka+b+T/7Lu1wNA9g0Yn4ExXPkE5+VrRGM882M3pb0I4sauCVEkeYfb6mFA5M4/0qMNFc53szq+6WY79s5Wf7Hz38kjXDOxZzT0Pka+siIsPuq+dXOvsF9ooFnUn5eWY6tX3CfqC/2uZHYnHMtmjPYObfOOXeh/FXQm+RfW64l28xQpGYxlRrRflHlUu3vnKxsRq9j56cWOli+Kc8K+S/4L+RrpS+RdKBz7sX4W0hv/2bWXr7GOdL/5kbn+5Wl4kY1zAmeqOtHpsdivvzUIPfLT5FVIz8F3RL549Mr2GakGXSjZvRZfh+F+lwSiJ7L9EqX3sBTWfuslb9I8Sf5RCrymkg4V3rQ4uXGqIdSGbU91JjVvOb5B/Jjatzl0qvxj/5cSqepezafP3KIc26y/CCMV0j6t3zSE+lL/al8jfFN8i28ms2a4Zz7TP4z5io19M/fLF+BMFXSIBdjlPiQRNc6p9Nku+k6Tae6ilYt/9v6h5Kelf8erZJvBXSPpL7OuXjTPP5N0lHyFz6elr+4vE6+VdbXkl6SdIOk3s65mNM1Ouc2O+fOkE/ip8t/zlTKV5i9Kz945cHOuV+4xAO8JeT8IJ6D5LsfvRpsf5P8b6/r5X8zJGpFFr2t6ySdLv/Z8qmSdClp6Wu2ELXVec9GoHl1k/8RHLkqNjL4e06csj8Olv8szvLHguUXxFj202DZC5LaJ4npqqDsySEfm95quOJyWpa2ua38gD1O0rhWivs3wfbHtNXzSiO2y4L9/iTO8q7yH3RO0jmtFEOF/NXD3inc9srSPt8KntOSLD+X2cF2P0tQJrTznQv7z9VYCu25NPlse0GS5dNzkZ8P18n/wKiUvxBQkcsxB3F8IP9DqKN8P8FvJO2Y5jb6Rj2Xy/PlnHHjxo0bt/y/5VXNc9B/eZD8D55n5PvffS3p++bnmW0qMgz883E2ma3pPiJt7o9P5Xm0oqHB/WvyNfMt5vx0LZEpaK4ws47Z2G4TkZrneLW7WX9eaegXte9YrpJvCfGGMrsCnJRzbqNzbqVzblkKtxVZ2u3HwX3vWIM2tECk5jlRzWCY5zsX9h8tl2JpqVx7Lr+T739fK990MJ0WHqE+F/MDuP1IfgC8c+VrgnZS4sHOcuX4vyjfYux++bnhr3HJ53RuKnpU90/ilmosV54/ACCP5VXyLN8E2yTNdM7VOj8q5//Jj7x5VozyR8hf4X471sZc9qb7WCzfdOyodJ5MKzg6uL8qzR+Cydwpfww7q/E8qtnSV76JUMzzpNZ7XqnoK//jutlE9uZHjI00eRrrsjwFScgig6K0l/SwmZ0UDKLRJ9MNBgNmRAahSNRkO8zznQv7j5ZLsbRUzjwXMztMvqmcJN3q/KBn6QjtuZjZD+QHcFsiPzBdrXwTyCpJv7b4MyPkyvGPNN0+Qb4ZXtKRbM1su2AQm0PM7Kfyo6NL/uJ3qgOk5crzBwDkMcun7xAz+4+k/SUd4YIRRc3se5KekrTIOXdoVNmd5fvGveGc6xtre0G5PvKJ0QfygwsskL+C/z2XxqilZvaxfBPebVwGoy3nOvPzt54oab1zLmtDw5ufduZb+dFx456nMAR9Cr+VtMI5t1+M5UdLGizpE+fcXW0dX2sK+re/Lj+YW7R1zrntM9zmifLjFEi+2SS1P2gT5qcg2VW+C8QQ+b5428tPq/bdFFoX5QQzGyI/p+pnkg5xUTNIBKOOXyTpaufcNaEEmAIzO1i+z6PkB8p7LFH5YJ2TJT0cY9EU51w2RqEFACAluTDAVUrM7AD5xPlDNVy5lvxABmskHWJmPV3D9DORwa2+TrRd59zbZvY3+YFLXpUfOffMdBLnqP10ldRFqTcjyxvOucXK/tRCkh9cqESJBwsLy77yTbJfi7XQOfes/Ouv4DjnvjCz/pJ+Kd/ccU9Jcef6TlF0U8vWeC0B8fxIzadD+Vx+nIp8SZz7yCeQlfKDtzWdevF6+YHafmVmtznnEn73hSgyl+pTqSTOTUQGAHxL0kPyA9oBANBm8iZ5lk9uJWlGdJMr51ydmc2U9Av5Zt3/HSyK9M39Vsn9Kdh+F/kRVx+KV9DMrpev+W7aRHt91H4LLnluLc6PVp7tOW6zJdKSIdkIsgXJ+bmnf5vFTUaS57XOuQ8TlgSyKzK6c5X8VGZPyI+M/lH8VXKL8/M6d0qw/CM1jDaek8zM5Jtc18oPxpgS59wjyt3vCQDAViQv+jwHX7hnB/8+EKNI5LHRUY9FphxJONhRBtN93CjfV6upSK3cN0nWRx4IXnOR19PTYcZSKJxzJzrnzDnXJexYsHVxzv0weO2VOed6OucuyafEuYCMlXS4fD/zZuNIAACQ6/IieZYfiGt3SW8755qNyOz8vK0fSNon6Jsr+aZdkh/kKpH/lXSM/Lx0WyT91swq4hV2zn3jnFsfY1GkRiCVAcaQo8ysvZnNkO8LeaR808JcbFIOADnPzPqY2d1m9rR8K6+3JP0+5LAAAMhIviTPkRrAWLXOEZFpgs6R6ie8/0xSTzMrjrVCutN9mFknM3NmNrDJ49vJD0bzdjACOPLX/vKT3FdIulXSaeGGAwB5baikC+SnqXtI0nHOuU3hhgQAQGZyfrRtM2snPyBYJ0m9nHPvxil3gPygU6sl7R70hZ4l6VRJBzrn/tOk/A8kTZOf7uMo59xGM9td0gr5ftJ7NK1hNrOhkv4labvoQWaCEVCflfRX59yFLX/WAAAAAIBckg8Dhg2XT5xrJE32XVHjqpOvAT5avp/qHPnk+ShJ9clzkOzeI+ljSSc45zZKknPuYzO7V366j19Kajrdx0GSlsUYnXVwcD8nrWcGAAAAAMgL+VDz/Hf5ZrTpmOycO9/MtpX0qaRXI6NjB9N9LJRvsj4oRo10N0nvStosX/v8ddSy+yU559yYJussle9b3dU5V5NmrAAAAACAHJfzNc/OuTNasO63wRzOPzGzHs65lS2c7uMg+RrresFcuH0k3UDiDAAAAACFKV8GDGuJP8jP7XlFSzZiZu0l7SPp1SaLfi0/x/PNLdk+AAAAACB3FXzyHNQk3yHpAjP7Tgs2daCkYkmvRR4ws70lnSHpJufc2pbECQAAAADIXTnf5zlXmNlFkn7rnOsRdiwAAAAAgLZF8gwAAAAAQBI5P2AYGpjZGknl8lNsAQAAAMhvu0va5JzbJexAkBw1z3nEzNaXlZVt17Nnz7BDybqNGzdKkioqKkKOBKngfOUfzll+4XzlF85X/uGc5ZdCPl/vvfeetmzZssE51yHsWJAcNc/55eOePXvu+9Zbb4UdR9Y988wzkqShQ4eGHAlSwfnKP5yz/ML5yi+cr/zDOcsvhXy+9ttvPy1dupRWpXmi4EfbBgAAAACgpUieAQAAAABIguQZAAAAAIAkSJ4BAAAAAEiC5BkAAAAAgCQYbRsAAGAr4JwTU5Q2VldXF3YISEM+nC8zk5mFHQZaCckzAABAgaqtrdXatWu1YcMGVVVVhR1OzojMF/zOO++EHAlSkW/nq6KiQl26dFF5eXnYoSDLSJ4BAAAKUG1trT766CNVVlaGHUrOiSRjyA/5dr42btyoyspK9ezZU8XFxWGHgywieQaAQlFdJcVp0lZUE9Q4bYnzI7qoSCpt10qBAQjD2rVrVVlZqeLiYu28886qqKhQURHD3UjS+vXrJUkdOnQIORKkIp/OV3V1tT755BNt2bJFX3zxhXbZZZewQ0IWkTwDQKGY/6Q0/Y6Yi4ZE/pg5Mfa6oy+Vjj6hFYICEJYNGzZIknbeeWd17Ngx5GhyS+QiAhcT8kM+na+ysjLttNNO+vjjj7VhwwaS5wKT+69AAEBqBh0ndeqS/nqdukiDjs1+PABC45yr7+Ocb01egXxXVlYmSaqpqWGQvgJD8gwAhaK0nTT8rPTXGz6KJttAgYn+wZ4PtXVAIYnu50zyXFj4NAWAQpJu7TO1zgAAACkheQaAQpJu7TO1zgAAACkheQaAQpNq7TO1zgAAACkjeQaAQlPaTjr+zOTlhp9FrTMAAECKSJ4BoBAdOSxx7XOnLr6GGgAAACkheQaAQpSs9plaZwCtobpK2lKZ2a26Kuzos2rLli3q3r27xo4dm7VtPvrooyoqKtKrr76atW0WIo49WktJ2AEAAFrJkcOkOTOlr79s/Di1zgBay/wnpel3ZLbu6Eulo0/IbjytYP/999dbb72lUaNG6YEHHohb7o477tCaNWv061//Omv7PvHEE3XggQdq3LhxeuKJJ7K23VTNnTtXEyZM0CuvvKLi4mIddthhuu6663TQQQelvA0zi7tszpw5GjZsWIvjLMRjj9xA8gwAhSpS+/y3Oxs/Tq0zgNYy6Djpnw82v2iXTJ4MYLh48WK99dZb6tChgx555BGtW7dOHTt2bFauqqpKN9xwg04//XR17do1a/s3M40dO1bnn3++XnrpJQ0cODBr205m9uzZGjlypDp27Kgf/vCHkqS//e1vOuKII/Tcc8/p4IMPTnlb3bt313nnndfs8b322qvFcRbisUfuoNk2ABSypn2fqXUG0JrSnS4vIk+mzZsyZYqKioo0ceJEVVZWaubMmTHLPfLII/riiy90zjnnZD2GU089VWVlZbr77ruzvu14qqqqdNFFF2mbbbbRCy+8oNtvv1233367Fi5cKDPTRRddlNb2evToofHjxze7ZSN5LrRjj9xC8gwAhaxp32dqnQG0tlSny4vIk1rn6upqPfDAAxo8eLDGjBmjHXfcUffdd1/MslOnTlV5ebmOPbb586qtrVWvXr1UXl6uNWvWNFo2b948lZWV6bDDDtPmzZtjbrtjx44aOnSoZsyYoerq6pY/sRT8+9//1qpVqzRmzBjts88+9Y/37t1bo0eP1pIlS1q1L/DKlSt16aWXqnv37mrXrp3MrNntqaeekhT/2Lf0uEvhHHvkFpJnACh0Rw7Tin6DtaLfYGqdAbS+dGuf86TW+bHHHtPatWt19tlnq6SkRGeccYYWLFigFStWNCpXV1enefPmqX///iotLW22neLiYo0fP16bN2/WTTfdVP/48uXLdfLJJ2v33XfXY489pvbt28eN5ZBDDtGGDRu0ePHi7D3BBObNmydJ+t73vtds2THHHNOoTCq+/vprTZo0Sdddd50mT56sjz/+OG7Z119/XUcddZSmT5+uAQMG6IorrtDxxx9fv7xbt27q2bOnDjjggITHPhvHXWr7Y4/cQp9nACh0pe300b6HSJL2yoMfqAAKQKp9n/Ok1lmS7rvvPpWWluq0006TJJ1zzjm68847NXXqVF1zzTX15ZYuXar169erX79+cbc1atQoXX/99Zo0aZKuvPJKFRUVacSIETIzzZkzR126JK65HzBggCRp4cKFOvTQQ+OWGz9+fMrPr0ePHjH7IUuqv0DQs2fPZssijzW9iJDIG2+8oYsvvrj+/5KSEl122WW68cYbGw0oVlVVpTPOOEPffvutZs2apZNPPrl+2bnnnqtp06bp2muv1ZgxYyRJb775ZsJj39LjLqV+7FGYSJ4BoICt31SneUu36MWP9lR1XbFef3S9eu1WqiP7lKlDOY2PALSSeAMWNpUnXUm++OILzZkzR8cdd5w6d+4sSTr88MPVrVs3TZ06VRMmTKhP+latWiVJ2nnnneNur6ioSBMmTNBpp52ma6+9VkuWLNGqVav09NNPa++9904az0477dRoX/FMmDAhpecnSYMHD46bPK9fv16S1KFDh2bLIo+tW7cupf1cccUVOu2007TPPvuorq5OL7zwgq644grddNNN6tSpk8aNG1dfdvr06Xr33Xf185//XEOHDm20ndNPP13Tpk3TkiVL6pPnZMe+pcddSv3YozDxywkAClBVjdPUuRv1m6nf6JGXNmvNpg5aW1mhpatq9MiizfrN1G80be5GVde4sEMFUKiaDljYVB4NYDh9+nRVV1fr7LPPrn/MzDRq1Ch9+OGHmjt3bv3jX331lSRp++23T7jNU089VQMGDNBtt92mhQsXatq0aTr88MOblRsxYoTuvLPxRYhOnTpJktauXZtwH865lG/RzyHWdiLPuaVuuukmHXrooerUqZN22GEHnXDCCXrqqafUsWNH3XDDDY36Es+YMUOS9JOf/KTZdrbbbjtJvi9zRCrHviXHXUr92KMwkTwDQIGpqnH68+MbNG/pFtXWxS5TWyc9v3SLJj6+QVUk0ABaQ9MBC5vKk1pnyTfZLi8v18iRIxs9HkmmowcOi/SZraysTLjNurq6+lrM448/XqeffnrMcrNnz9bPfvazRo9FBrVK1j83WyLTccWqXY7USseasitVXbt21bHHHqv169fr7bffrn/8xRdf1F577aXu3bs3W+eTTz6R1LgpeSrHviXHXWr7Y4/cQrNtACgwM+Zv0vLVNSmVXb66Rg/O36QxQypaOSoAW6Ujh0lzZjbv+5xHtc5vvPGGXnvtNZ111lnadtttGy3r16+f+vTpo1mzZumOO+5QRUWFdtxxR0kNtaDxXHbZZZozZ4522mkn/etf/9L777+vPffcM6WYvv76a0mq31c82erzHJlC6r333lP//v0bLXvvvfcalclU5Lls3LhRkj9+69evV9++fWOWnzNnjiTf3LzpNhId+5Ycdyn1Y4/CRPIMAAVk3aY6LVy2Ja11FizbopMGtqcPNIDsi9f3OY9qnadMmSJJjZpsRzv77LN11VVXadasWTr33HO17777ysz07rvvxt3mxIkTdeutt+riiy/WWWedpSFDhmj8+PGaOnVqo3LPP/+8Ro4cqa+//rpRk+nly5dLkg444ICEsWerz/ORRx6pG2+8UU8//bTOOOOMRssiU0QdeeSRKe8rlsjo1ZFa5sjzjdRsR1u5cqUeeeQR9e3bt9HgYMmOfUuPu5T6sUdh4pcSABSQ+QmaasdTWyfNezu9hBsAUta073Me1TrX1NRo+vTp2n777TVs2LCYZZo23e7cubN69+6tV155JWb5hx9+WJdffrmGDx+u2267TYMHD9aQIUM0ffr0Rk2WJWnJkiXq379/swTupZdekiQNGjQoYfzZ6vN8zDHHqGvXrpo2bZreeeed+seXLVum6dOn66CDDtJBBx3UaJ1ly5bV10pH/Oc//9G3337bbPt//vOftWjRIg0aNEi77rqrJN+3uFu3bnrjjTe0bNmy+rLffvutxowZo82bN+vmm29utJ1Exz4bx11K/dijMFHzDAAFZPnq6uSFYq33SbVGDKD/FoBW0LT2OY9qnefMmaPPP/9cu+22my655JK45crLy/Xss8/qo48+Urdu3TRy5EjdeOON+uCDD7THHnvUl1u0aJFGjx6tAw88UA8++KCKi4slSVdffbWOPvpoXX311Zo5c2Z9+cWLF9dPjRTt2Wef1YABA+oTzdZWVlamv/zlLzrppJN02GGH6ZxzzpEk/e1vf1NdXZ0mTZrUbJ0+ffqoe/fuWrlyZf1j99xzj6ZMmaLvfe976tatm2pra/Xiiy/q5Zdf1o477qi//vWvjbYxbtw4XXzxxTr++ON15plnqqysTLNmzdLq1as1ceLE+jmmo8U69tk67lLbH3vkFpJnACggldWZDf6V6XoAkJIjh0lbghYueVLrLDXUJn/yySe65557kpafOnWqfv/73+uCCy7QjTfeqBkzZtRPvfT+++/rxBNP1A477KDHH3+8Uf/pIUOGaPDgwXrooYf0+uuv1/fzXbx4sa6++upG+3jppZf03nvvxRwJujVFRsWeMGGCpkyZoqKiIh1xxBH6wx/+0KwfdDzDhg3TRx99pCVLluiJJ55QbW2tunfvrrFjx+rKK6/Ud77znUblL7roIpmZJk6cqHvvvVfbbrutBg0apAcffDDmCNmSmh37bB13Kbxjj9xB8twKzGwXSVdKGiFpd0mbJX0g6Wnn3G/CjA1AYdumNLNpRDJdDwBSUtpOOv6M5OVyzEMPPZTRenvvvbeGDx+uqVOn6re//a3MTHvuuac+//zzuOs0bTa9ceNGvfPOO81qQKdMmaLOnTtr9OjRGcXWEkcffbSOPvrolMpGpreKNmzYsLjN3+O58MIL65vGx5pnuqmmxz5bx10K99gjN9DnOcvM7DBJb0v6paRqSY9KelHSDpJ+FV5kALYGvXYtzWy93TJbDwAQ23XXXad33nlHs2bNymj9V199VR06dGg0FdOaNWs0efJkjRs3LqVEcmvVkmMf67hLHHt41DxnkZntKumfksokneqce7jJ8oGhBAZgqzFo3zI99srmtAYNKy6SjuxT1npBAcBWqG/fvrrrrrtUU5Pa1IFNLV68uNmgVR999JHGjRunSy+9NFthFqSWHPtYx13i2MMjec6uGyRtL+nnTRNnSXLOvdTmEQHYqnQsL9Lhvcs0b2nqo2cf0buMaaoAoBVccMEFGa87duxYjR07ttFjAwcO1MCB1MWkItNjH+u4Sxx7ePxayhIz6yTpTEnrJN0dcjgAtmKjBpWr166pXRvttWuJRg0qb+WIAAAA8h81z9lzhHxz7ackVZvZ6ZIGSSqVtEzSTOfcZyHGB2Ar0a7ENPaE7fTg/E1asCz2vM/FRb7GedSgcpWWMFgYAABAMhZrJDykz8yulG+2fa+kPpIOa1Jko6QfOef+nsK23oqzqGf37t3L7r333hbFmos2btwoSaqoqAg5EqSC85U/NteU6P1vOuvTDe1V44pVViLtVP6t9tz+K7UvyawfHlof77H8kqvnq6KiQhUVFeratauKimhsGK2uzl9V5Ljkh3w7X3V1dVq1apU2btxY//kQz/nnn68PP/xwqXNuvzYKDy1AzXP2dAruz5W0RdIF8iNtbyvp5/Ijbd9vZu84594IJ0QAW5v2JTXar8vn6tE+N3/cAwAA5AuS5+wpDu5LJF3inItUD38p6XIz6ybpdEm/kfSDRBuKd+XJzN6qqKjYd+jQoVkKOXc888wzkqRCfG6FiPOVfzhn+YXzlV9y8XzV1dXpnXfekeTnxs2XGru2sn79ekmpzRuM8OXb+aqrq1NxcbE6dOiggw8+OOH7j4va+YVP0uzZENzXSbovxvJIMj2kTaIBAAAAAGQNyXP2rAzu1zjnYs0RE1m+U5tEAwAAAADIGpLn7Hk1uO9kTWdV93YI7r9to3gAAAAAAFlC8pwlzrn/SPpAUntJh8QoMiS4X9JWMQEAAAAAsoPkObtuDO5vNbMukQfNbICky4N/J7V5VAAAAACAFmG07ey6S9L3JJ0h6R0zWyg/VdXhktpJuss591CI8QEAAAAAMkDynEXOuTozGyVprqQfSxoqyUl6RdIk59y0EMMDAAAAAGSI5DnLnHN1ku4MbgAAAACAAkCfZwAAACDLtmzZou7du2vs2LFZ2+ajjz6qoqIivfrqq8kL5yGOGXIdyTMAAABazfpNdZr9ymb96dH1un7WOv3p0fWavXiz1m+qCzu0jOy///4yM5199tkJy91xxx1as2aNfv3rX2dt3yeeeKIOPPBAjRs3LmvbTNWtt96qMWPGqHfv3ioqKpKZqbKyMqv7KLRjhsJDs20AAABkXVWN04z5m7Rw2RbVNsmTl66q0WMvb9YRvcs0alC5SkssnCDTtHjxYr311lvq0KGDHnnkEa1bt04dO3ZsVq6qqko33HCDTj/9dHXt2jVr+zczjR07Vueff75eeuklDRw4MGvbTiZSG9y9e3d17NhR33zzTVa3X4jHDIWHmmcAAABkVVWN058f36B5S5snzhG1ddLzS7do4uMbVFXj2jbADE2ZMkVFRUWaOHGiKisrNXPmzJjlHnnkEX3xxRc655xzsh7DqaeeqrKyMt19991Z33Yis2fP1ueff66VK1eqb9++Wd9+IR4zFB6SZwAAAGTVjPmbtHx1TUpll6+u0YPzN7VyRC1XXV2tBx54QIMHD9aYMWO044476r777otZdurUqSovL9exxx7bbFltba169eql8vJyrVmzptGyefPmqaysTIcddpg2b94cc9sdO3bU0KFDNWPGDFVXV7f8iaVo+PDh2nHHHdNa54MPPtCPf/xjde/eXe3atZOZNbs99dRTkuIfs9raWvXv31+77LJLRsdLCu+YofCQPAMAACBr1m2q08JlW9JaZ8GyLTnfB/qxxx7T2rVrdfbZZ6ukpERnnHGGFixYoBUrVjQqV1dXp3nz5ql///4qLS1ttp3i4mKNHz9emzdv1k033VT/+PLly3XyySdr991312OPPab27dvHjeWQQw7Rhg0btHjx4uw9wSx7/fXX1b9/f02ePFkDBgzQFVdcoeOPP75+ebdu3dSzZ08dcMABCY9ZcXGxxo0b16LjJeXHMUPuo88zAAAAsmZ+gqba8dTWSfPe3qIRAxInQGG67777VFpaqtNOO02SdM455+jOO+/U1KlTdc0119SXW7p0qdavX69+/frF3daoUaN0/fXXa9KkSbryyitVVFSkESNGyMw0Z84cdenSJWEsAwYMkCQtXLhQhx56aNxy48ePT/n59ejRQ+edd17K5ROpqqrSGWecoQ0bNuiJJ57Q97///fpl5557rqZNm6Zrr71WY8aMkSS9+eabCY/ZaaedpltuuSXj4yWlfsyAREieAQAAkDXLV2fWLHb5J9U5mzx/8cUXmjNnjo477jh17txZknT44YerW7dumjp1qiZMmCAzP+jZqlWrJEk777xz3O0VFRVpwoQJOu2003TttddqyZIlWrVqlZ5++mntvffeSePZaaedGu0rngkTJqT0/CRp8ODBWUuep0+frnfffVdXXHFFo8RZkk4//XRNmzZNS5YsqU+ekx2zoqIijRs3TmPGjMnoeEmpHzMgEZpto+1UV0lbKmPeimqqVFQTf7mqq8KOHgAApKCyOrPBvzJdry1Mnz5d1dXVjaanMjONGjVKH374oebOnVv/+FdffSVJ2n777RNu89RTT9WAAQN02223aeHChZo2bZoOP/zwZuVGjBihO++8s9FjnTp1kiStXbs24T6ccynfop9DS82YMUOSdMkllzRbtt1220nyfZkjUjlmI0eOzPh4SakfMyARap7RduY/KU2/I+aiIZE/Zk6Mve7oS6WjT2iFoAAAQDZtU5rZtFOZrtcW7rvvPpWXl2vkyJGNHj/77LP1xz/+Uffdd5+OPvpoSarve5tsDuS6urr62tDjjz9ep59+esxys2fPbvZYZHCsZP18w/Liiy9qr732Uo8ePZot++STTyRJPXv2rH8slWPWkuMl5f4xQ34geUbbGXSc9M8Hpa+/TG+9Tl2kQc1HqwQAALmn166lWroqtZG2G623W/PBtXLBG2+8oddee01nnXWWtt1220bL+vXrpz59+mjWrFm64447VFFRUT8idaQ2NZ7LLrtMc+bM0U477aR//etfev/997XnnnumFNPXX38tSUlHvw6jz/NXX32l9evXx53Oas6cOZJ8M/GIVI7ZuHHjMj5eUurHDEiE5Bltp7SdNPysuLXPcQ0f5dcFAAA5b9C+ZXrslc1pDRpWXCQd2aes9YJqgSlTpkhSoybb0c4++2xdddVVmjVrls4991ztu+++MjO9++67cbc5ceJE3Xrrrbr44ot11llnaciQIRo/frymTp3aqNzzzz+vkSNH6uuvv67vUy35kaYl6YADDkgYexh9niNxrl+/vtmylStX6pFHHlHfvn0bDQ6W7JjdeeedmjRpUsbHS0r9mAGJ0OcZbWvQcb4mOVXUOgMAkFc6lhfp8N7pJcJH9C5Th/Lc+1laU1Oj6dOna/vtt9ewYcNilokk1ZE5nzt37qzevXvrlVdeiVn+4Ycf1uWXX67hw4frtttu0+DBgzVkyBBNnz5db7/9dqOyS5YsUf/+/Zslgi+99JIkadCgQQnjD6PPc6dOndStWze98cYbWrp0af3j3377rcaMGaPNmzfr5ptvbrROomP28MMP63e/+52OPfbYjI+XlPoxAxKh5hltK93aZ2qdAQDIO6MGleuzb2q1fHXy5tu9di3RqEHlbRBV+ubMmaPPP/9cu+22W8zBryLKy8v17LPP6qOPPlK3bt00cuRI3Xjjjfrggw+0xx571JdbtGiRRo8erQMPPFAPPvigiouLJUlXX321jj76aF199dWaOXNmffnFixfXT7EU7dlnn9WAAQO06667ZvHZJnbDDTdo2bJlklR//5Of/KT+Odx88831U0aNGzdOF198sY466iiNHj1aZqZZs2Zp9erVmjhxoo455phm2491zCLHa7/99tPkyZMzPl5SOMcMhSf3LvGh8KVa+0ytMwAAealdiWnsCdvpqH3LVBzn12ZxkXTUvmX65QnbqbQkNwcLi9Qmf/LJJ7rnnnvi3jZt2iTnXH0z4gsuuEBSw6jTkvT+++/rxBNP1A477KDHH3+8Uf/pIUOGaPDgwXrooYf0+uuv1z++ePFiffe7320U00svvaT33nuvfh9t5YknntB9992n++67T5999pkk6f77769/7Ntvv60ve9FFF2nSpEnaeeedNWnSJE2bNk39+/fXvHnz9Itf/CLm9pses+jjNXPmzIyPlxTeMUPhoeYZba+0nXT8mdLfmk8j0Mjws6h1BgAgT7UrMY0ZUqGTBrbXvLe3aPkn1aqsdtqm1NRrt1Id2Sc3m2pHe+ihhzJab++999bw4cM1depU/fa3v5WZac8999Tnn38ed52mzaY3btyod955p1lN6pQpU9S5c2eNHj06o9gylW6z7gsvvFAXXnhhyuWbHrPo4xWr/3Sqx0sK75ih8OT2JxYK15HDEtc+d+ria6gBAEBe61BepBED2uuykR007rSOumxkB40Y0D7nE+eWuu666/TOO+9o1qxZGa3/6quvqkOHDo2mdFqzZo0mT56scePGqUOHDtkKNWe05JjFOl5S4R8ztK3C/tRC7orUPsdDrTMAAMhjffv21V133aWamvSn7ZJ8E+Smg1999NFHGjdunC699NJshZlTWnLMYh0vqfCPGdoWzbYRniOHSXNmNp/3mVpnAABQAFrSx3bs2LEaO3Zso8cGDhyogQMHtjSsnJbpMYt1vKSt45ih7VDzjPDEq32m1hkAAABAjiF5Rria9n2m1hkAAABADiJ5Rria1j5T6wwAAAAgB9HnGeE7cphWLH1LkrQXtc4AAAAAchDJM8JX2k4f7XuIJGkvap0BAAAA5CCabQMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAJBlW7ZsUffu3TV27NisbfPRRx9VUVGRXn311axtM5dwzJDrSJ4BAADQqrbUVmv6imc0fcUzqqqtCTucFtl///1lZjr77LMTlrvjjju0Zs0a/frXv87avk888UQdeOCBGjduXNa2mYpVq1bplltu0fe+9z3ttttuateunXbffXedf/75+uCDD7K2n0I6ZihMJM8AAABoVbM/XqRJyx7XpGWPa/bHi8IOJ2OLFy/WW2+9pQ4dOuiRRx7RunXrYparqqrSDTfcoNNPP11du3bN2v7NTGPHjtWTTz6pl156KWvbTeb222/X5Zdfrk8//VQnnniiLrvsMvXq1UuTJ09W//799eabb7Z4H4V2zFCYSJ4BAADQarbUVuv+FU/X/z9txVN5W/s8ZcoUFRUVaeLEiaqsrNTMmTNjlnvkkUf0xRdf6Jxzzsl6DKeeeqrKysp09913Z33b8RxyyCFasGCBli5dqkmTJunGG2/U008/rVtuuUXffPONrrjiihbvo9COGQoTyTMAAABazeyPF+mLyoYa2i8q1+Vl7XN1dbUeeOABDR48WGPGjNGOO+6o++67L2bZqVOnqry8XMcee2yzZbW1terVq5fKy8u1Zs2aRsvmzZunsrIyHXbYYdq8eXPMbXfs2FFDhw7VjBkzVF1d3fInloJTTjlFhx9+eLPHx44dq/Lycs2fPz/meh988IF+/OMfq3v37mrXrp3MrNntqaeekhT/mNXW1qp///7aZZddMjpeUjjHDIWJ5BkAAACtommtc0Q+1j4/9thjWrt2rc4++2yVlJTojDPO0IIFC7RixYpG5erq6jRv3jz1799fpaWlzbZTXFys8ePHa/PmzbrpppvqH1++fLlOPvlk7b777nrsscfUvn37uLEccsgh2rBhgxYvXpy9J5ihkpISlZSUNHv89ddfV//+/TV58mQNGDBAV1xxhY4//vj65d26dVPPnj11wAEHJDxmxcXFGjduXIuOl5Rbxwz5q/krHQAAAMiCprXOEZHa51N6HBFCVJm57777VFpaqtNOO02SdM455+jOO+/U1KlTdc0119SXW7p0qdavX69+/frF3daoUaN0/fXXa9KkSbryyitVVFSkESNGyMw0Z84cdenSJWEsAwYMkCQtXLhQhx56aNxy48ePT/n59ejRQ+edd17K5SXpH//4h9avX69TTjml0eNVVVU644wztGHDBj3xxBP6/ve/X7/s3HPP1bRp03TttddqzJgxkqQ333wz4TE77bTTdMstt2R8vKTUjxmQCMkzAAAAsi5erXPEtBVPacTuh6hdce7/HP3iiy80Z84cHXfccercubMk6fDDD1e3bt00depUTZgwQWYmyY9MLUk777xz3O0VFRVpwoQJOu2003TttddqyZIlWrVqlZ5++mntvffeSePZaaedGu0rngkTJqT0/CRp8ODBaSXPn332mX7+85+rrKys0cUDSZo+fbreffddXXHFFY0SZ0k6/fTTNW3aNC1ZsqQ+eU52zIqKijRu3DiNGTMmo+MlpX7MgERotg0AAICsi1frHJFPfZ+nT5+u6urqRtNTmZlGjRqlDz/8UHPnzq1//KuvvpIkbb/99gm3eeqpp2rAgAG67bbbtHDhQk2bNi1mv+IRI0bozjvvbPRYp06dJElr165NuA/nXMq36OeQzMaNG3XSSSfpk08+0e23367999+/0fIZM2ZIki655JJm62633XaSfF/miFSO2ciRIzM+XlLqxwxIhOQZAAAAWVVVW5Ow1jni/hVP50Xf5/vuu0/l5eUaOXJko8cjyXT0wGGRvreVlZUJt1lXV1dfG3r88cfr9NNPj1lu9uzZ+tnPftboscjgWMn6+baGzZs368QTT9SiRYt000036cc//nGzMi+++KL22msv9ejRo9myTz75RJLUs2fP+sdSOWYtOV6RuKP3BWQi99vJAAAAIK8kq3WO+Lzym5zv+/zGG2/otdde01lnnaVtt9220bJ+/fqpT58+mjVrlu644w5VVFRoxx13lNRQmxrPZZddpjlz5minnXbSv/71L73//vvac889U4rp66+/lqT6fcWT7T7PW7Zs0SmnnKJnn31WEyZMiDlF1VdffaX169erb9++MbcxZ84cSb6ZeEQqx2zcuHEZHy8p9WMGJELyDAAAgKypqq3RtBVPpVz+/hVP53Tf5ylTpkhSoybb0c4++2xdddVVmjVrls4991ztu+++MjO9++67cbc5ceJE3Xrrrbr44ot11llnaciQIRo/frymTp3aqNzzzz+vkSNH6uuvv67vUy35kaYl6YADDkgYezb7PNfU1OjMM8/Uk08+qd/85je66qqrYpaLxLl+/fpmy1auXKlHHnlEffv2bTQ4WLJjduedd2rSpEkZHy8p9WMGJEKz7Swys7lm5hLchoUdIwAAQGtKtdY5IlL7nItqamo0ffp0bb/99ho2LPbPuKZNtzt37qzevXvrlVdeiVn+4Ycf1uWXX67hw4frtttu0+DBgzVkyBBNnz5db7/9dqOyS5YsUf/+/Zslgi+99JIkadCgQQnjz1af59raWo0ePVqPPvqofv7zn+vGG2+MW7ZTp07q1q2b3njjDS1durT+8W+//VZjxozR5s2bdfPNNzdaJ9Exe/jhh/W73/1Oxx57bMbHS0r9mAGJ5OYlvvw3S9K3MR7/pK0DAQAAaCvp1jpH5Grt85w5c/T5559rt912izn4VUR5ebmeffZZffTRR+rWrZtGjhypG2+8UR988IH22GOP+nKLFi3S6NGjdeCBB+rBBx9UcXGxJOnqq6/W0UcfrauvvlozZ86sL7948eL6KZaiPfvssxowYIB23XXXLD7b+K655hrNnDlTXbp0UadOnWI2B49+bNy4cbr44ot11FFHafTo0TIzzZo1S6tXr9bEiRN1zDHHNFs/1jGLHK/99ttPkydPzvh4SW1/zFCYcusTqnBc4ZxbGXYQAAAAbSndWueIXO37HKlN/uSTT3TPPfckLT916lT9/ve/1wUXXKAbb7xRM2bM0Lhx4yRJ77//vk488UTtsMMOevzxxxv1nx4yZIgGDx6shx56SK+//np9f+HFixfr6quvbrSPl156Se+9917MEaVby4cffihJ+vLLL5tNSxURnTxfdNFFMjPdeuutmjRpkrbddlsNGjRIDz74YMwRsiU1O2bRx2vmzJkZHy8pnGOGwkTyDAAAgKwYvvtADev63YzWLbLc60340EMPZbTe3nvvreHDh2vq1Kn67W9/KzPTnnvuqc8//zzuOk2bTW/cuFHvvPNOs5rUKVOmqHPnzho9enRGsWViypQp9X2/U3XhhRfqwgsvTLl802MWfbxi9Z9O9XhF4m/rY4bClHufUgAAAMhLZcWlal9SltGtrLg07PCz6rrrrtM777yjWbNmZbT+q6++qg4dOjSa0mnNmjWaPHmyxo0bpw4dOmQr1JzRkmMW63hJhX/M0LaoeW4dF5jZDpLqJC2X9Ihz7qOQYwKwldpSW6351SskSYNqj8q5PoUAUIj69u2ru+66SzU1mc1jvXjx4maDX3300UcaN26cLr300myFmVNacsxiHS+p8I8Z2pY558KOoWCY2VxJg2Msqpb03865/05xO2/FWdSze/fuZffee2+GEeaujRs3SpIqKipCjgSp4Hzll0XVK/XP6jclSSNK99fA0h7hBoSkeI/ll1w9XxUVFaqoqFDXrl1VVERjw2h1dXWSxHHJE/l2vurq6rRq1Spt3Lix/vMhnvPPP18ffvjhUufcfm0UHlqA6ofsel7S3ZIWSvpU0u6STpf0e0nXmNl659yfQ4wv52yuKdH73+ygTzdsoxpXrLISaafyb7Xn9l+pfUlmV2oBNKh2tZpfs6L+/3k1K9S/ZHeVWHGIUQEAAOQfkucscs41nS1+uaTrzOwVSU9KmmBmf3XObU6ynZhXnszsrYqKin2HDh2anYBDVFXjNGP+Ji18f4tq66IXSGs2ddBbX+2qI3qXadSgcpWWNJ+rD+F65plnJEmF8FosdP+3cr7Wv1lZ//96V6mNPctzbkRbNMZ7LL/k4vmqq6vTO++8I0nq0KFD3tTYtZXIAFT0gc0P+Xa+6urqVFxcrA4dOujggw9O+P7LtRYrSIxP0jbgnPuXpFckdZR0aMjhhK6qxunPj2/QvKVNEucotXXS80u3aOLjG1RVQ9cCIBNbaqt1/4qnmz0+bcVTqqqlZQcAAEA6SJ7bzrvB/XdCjSIHzJi/SctXp/bDffnqGj04f1MrRwQUpnjzrX5RuU6zP14UQkQAAAD5i+S57XQK7r8NNYqQrdtUp4XLtqS1zoJlW7R+U5wqagAxxat1jqD2GQAAID0kz23AzHaUdGTw75IwYwnb/ARNteOprZPmvZ1ewg1s7eLVOkdQ+wwUtujpeiIjFQNoG7W1tfV/N506C/mN5DlLzOxQMzvamrxDzKyHpIclVUh61Dm3Koz4csXy1dWZrfdJZusBW6Oq2pqEtc4R9694mtpnoECZmdq1aydJSafKAZBdW7b4Sp+SkhKS5wLDaNvZ01vSZEmfmtlySWskdZU0QNI2kt6S9JPwwssNldWZDf6V6XrA1ihZrXPE55XfaPbHixh5GyhQ2223ndauXavPPvtMkh/Vl1G3vUhtPLXy+SGfzld1dbU+//xzSf49iMJC8pw9iyT9RdIhkvaVdISkjZJek/R3SX9JNkXV1mCb0syuvmW6HrC1qaqt0bQVT6Vc/v4VT2vE7oeoXTFfB0Ch2WGHHbRx40ZVVlZq9erVYYeTUyLNaouLmfM+H+Tj+SouLtaOO+4YdhjIMn4tZYlz7m1JPws7jlzXa9dSLV2VfjPRXruVtkI0QOFJtdY5gtpnoHAVFxerW7duWrt2rTZs2KCqqqqwQ8oZkabs+TJv8NYu385XRUWFunTpklfJPlJD8ow2NWjfMj32yuZGg4bVqUZfb/eSJKnThoEqavKyLC6SjuxT1pZhAnkp3VrnCGqfgcJVXFysnXbaSTvttJOcc3KOblCSNHfuXEnSwQcfHG4gSEk+nS8zo59zAeOXEtpUx/IiHd67TPOWNoyevW7b1/RFp2ckSUWunTp9+91G6xzRu0wdyumjBSSTbq1zBLXPwNaBH/XN0Qc8v3C+EDaSZ7S5UYPK9dk3tVq+ukZ1qtHaDgvql63tsEAdv+1XX/vca9cSjRpUHlaoQF4ZvvtADev63ZjLnnvuOUnS4MGDYy4vMn6QAAAAJELyjDbXrsQ09oTt9OD8TXr041dUU7KhfllNyQat2/Y1ddn0XR3Ru0yjBpWrtISr5EAqyorjjw3QzvzHffsSukAAAABkgqoGhKJdienMI9upepcXmi3b1GWh/vCDbTVmSAWJMwAAAICcQPKM0Mz+eJHWbmneP3ND3Xo9/+XLIUQEAAAAALGRPCMUW2qrdf+Kp+Mun7biKVXVpj+lFQAAAAC0BpJnhCLZqMBfVK7T7I8XtWFEAAAAABAfyTPaXFVtTcJa54j7VzxN7TMAAACAnEDyjDaX6ly0kblnAQAAACBsJM9oU1W1NZq24qmUy1P7DAAAACAXkDyjTaVa6xxB7TMAAACAXEDyjDaTbq1zBLXPAAAAAMJG8ow2k26tcwS1zwAAAADCVhJ2ANh6DN99oIZ1/W7MZc8995wkafDgwTGXFxnXeQAAAACEh+QZbaasuDTusnbmX4rtS8raKhwAAAAASBnVeQAAAAAAJEHyDAAAAABAEiTPAAAAAAAkQfIMAAAAAEASJM8AAAAAACRB8gwAAAAAQBIkzwAAAAAAJEHyDAAAAABAEiTPAAAAAAAkQfIMAAAAAEASJM8AAAAAACRB8gwAAAAAQBIkzwAAAAAAJEHyDAAAAABAEiTPAAAAAAAkQfIMAAAAAEASJM8AAAAAACRB8gwAAAAAQBIkzwAAAAAAJEHyDAAAAABAEiTPAAAAAAAkQfIMAAAAAEASJM8AAAAAACRB8gwAAAAAQBIkzwAAAAAAJEHyDAAAAABAEiTPAAAAAAAkQfLcSsyss5l9bmbOzJaFHQ8AAAAAIHMkz63nFkldwg4CAAAAANByJM+twMy+J+mHku4KOxYAAAAAQMuRPGeZmbWXNEnSUkk3hxwOAAAAACALSsIOoABdLamnpCGSqsMNBQAAAACQDdQ8Z5GZHSjpckmTnXPPhx0PAAAAACA7zDkXdgwFwcyKJL0gaU9JvZ1za82sh6QPJL3jnOudxrbeirOoZ/fu3cvuvffeFsebazZu3ChJqqioCDkSpILzlX84Z/mF85VfOF/5h3OWXwr5fJ1//vn68MMPlzrn9gs7FiRHs+3s+bmkgZJ+5JxbG3YwAAAAAIDsIXnOAjPbXdK1kp5zzk1p6fbiXXkys7cqKir2HTp0aEt3kXOeeeYZSVIhPrdCxPnKP5yz/ML5yi+cr/zDOcsvhXy+CrE2vZDR5zk77pTUTtLFYQcCAAAAAMg+ap6z4wRJ30j6i5lFP75NcN/NzOZGyjrnvm270AAAAAAALUXynD3bSxocZ1n7qGUccwAAAADIMzTbzgLnnMW6SdojKPJO1OPfhBgqAAAAACADJM8AAAAAACRB8gwAAAAAQBIkzwAAAAAAJMHgVa3IObdSkiUrBwAAAADIbdQ8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkETeJ89mVht2DAAAAACAwpb3ybMkCzsAAAAAAEBhKwk7gCxwiRaaWYmkPSXtGJT9UtL7zrmaNogNAAAAAFAACiF5bsbMOkgaLek0SYdKat+kyGYzWyTpIUnTnXPr2zhEAAAAAEAeKajk2cy2l/Rfkn4mnzC/LelBSe9JWivfTL2TpJ6SDpF0u6SbzewOSdc5575p+6gBAAAAALmuoJJnSe9L+lrSBEkznHMfJypsZrtLOlvSTyVdIGmHVo8QAAAAAJB3Ci15/pWkac65lEbgDpLrP5rZ/0ga06qRAQAAAADyVkElz865KRmuVyspo3UBAAAAAIWvEKaqSpmZ9Qs7BgAAAABA/gmt5tnMnklzFeec+14Ld/uApD7Bvl+V9FpwW5pqU28AAAAAwNYnzGbbQ5RkjuYolkbZuJxzfYI/75HUV76f882SOpjZUkmvOecuaOl+AAAAAACFJRf6PFtb79A5N13S9PoAzL4jn0z3betYAAAAAAC5LxeSZydpvaRpkhZnc8Nm1t0592HSAJz7VNKnkp7I5v4BAAAAAIUhzOR5mqQzJG0jqYOkSyS9IulO+Tmat2RhHy+Y2fHOudezsC0AAAAAwFYqtNG2nXM/lNRV0pWS3pdvvn2wpHslfWJmN5nZXi3czaeSnjezmAONmVlHM7uuhfsAAAAAABS4UKeqcs595Zy7yTm3t6QRkmbLN+PuLOlXkpaZ2cUt2MVRkl6QNNvMRkceNLNtzCyStF/Zgu0DAAAAALYCudDnWZLknJtjZm9K+oOkHwQPm6QdW7DNjWY2QtLdkqaa2W6SNkj6vaTvSHpR0lUtChwAAAAAUPByInk2s2GSLpKvfY6uDf+3fG10xpxztWZ2vqTdJV0fPPyKpB875+a0ZNsAAAAAgK1DaMmzmXWRdL6kCyX1UMOUVWslTZY0yTn3fhb2c6KkCZL6SVojaRdJz5M4AwAAAABSFWbN8ypJpWpImhdImiRppnOuOhs7MLNFkr4rP3DYRZLukXSppP8xs10lnZetfQEAAAAACleYyXM7+cHBnHw/5ApJl0u63MxilXfOuQFp7qOn/IBgtzvnKoPH/mxmqyVNlbSLmZ3inFufyRMAAAAAAGwdcqLPs6TtJPVNsNzkk+x07RkrMXbO/d3MPpf0iKR5SfYNAAAAANjKhTpVlXxSnMotI4lqlJ1zz8lPZdU50+0DAAAAALYOYdY8/yjEfUuSnHP/MbPDwo4DAAAAAJDbQkuenXP3pVrWzI6SdJ6khOuYWW/n3LI041gVrNvHOfd2OusCAAAAALYOYTfbjsvMepjZVWa2QtKzkn6Ywmpvmtnf0qlNNrMjzWympDcyjRUAAAAAUNhyZcAwSZKZVUg6Xb6W+Uj55P4jSYslpTLS9lGS/ihpgZmtlPSEpJclvS/pa/n+050k7SVpoKRjJe0u6UVJg7P3TAAAAAAAhSQnkmczGyKfMJ8mP2XV1/JzMk93zj1vZlcqheTZObdQ0qBgexdKOld+fuemI3WbpE2SHpOf63luy58FAAAAAKBQhZY8m9me8k2xz5XUTVKVpH9Kul/SbOdcVabbDpLhuWZWIp9095G0Y7D4C0lvS1rsnKvJ+AkAAAAAALYaYdY8r5BUJz/P8h8k/d05ty6bOwiS40XBrZnIQGTOufOzuV8AAAAAQGEJu9l2jXwT7a8lbW6LHZpZD/na7nMl7RE8TPIMAAAAAIgrzNG2+0maJOkISTMlfWZmdwX9lbPKzCrM7Idm9qx8jfd4+QsHi7O8n1+Z2f+Z2btmts7MtpjZh2Z2n5ntl819AQAAAADaTmjJs3PuDefcLyXtJulUSXPla4OfNrOPzexGMzuwJfswsyFmNkXSGkmTJR0oPxDZEOdcD0mzWrL9GP5L0vGSvpL0tKTZkirln9cSMzs+y/sDAAAAALSBsJttR/ol/0PSP8xsB0mj5Ufe/rWkK8xsqXyz7pS05kBkKThJfiCyyiYxXSzpTkl3m1k351xtK8YAAAAAAMiyMJttN+OcW+ucu9U511++lniipC6SBqWxmRWSfidppfx0Vbs4505zzj3cyomznHMLmibOweN/CeLaVdI+rRkDAAAAACD7cip5juace9M5d7mkrvI1uv+XxuptPhBZCiK1za2awAMAAAAAsi9nk+cI51ytc+4x59wZKa7ST200EFmqzOxc+Rrn5ZLeDysOAAAAAEBmzDkXdgwtYma1zrniGI+XSBoh3396uHz/7tWS/iZpunPuDTO7UtJ1sdZvYUy/lrSfpApJfYK/V0sa6ZxLOsK3mb0VZ1HP7t27l917771ZizVXbNy4UZJUUVERciRIBecr/3DO8gvnK79wvvIP5yy/FPL5Ov/88/Xhhx8udc4xM08eCH3AsNaS7YHI0nScpO9F/f+xpDGpJM4AAAAAgNxTsDXPCcrvL+lHks6RtLMkl+2a56h9bS/pAElXSTpG0u+dc39owfbe2nffffd96614FdP565lnnpEkDR06NORIkArOV/7hnOUXzld+4XzlH85Zfink87Xffvtp6dKl1DzniZzv85wCS6dwCwciS4tz7hvn3Dz5ZuOLJf23mR3cWvsDAAAAALSOvE+enXMZPYcMBiLLmHOuWtKD8on+ia29PwAAAABAduV98pxHvgzudww1CgAAAABA2kie287g4P69UKMAAAAAAKSN5DlLzOxIMzsrmCIr+vFSM/u5pDGSNss33wYAAAAA5JGCnaoqBD0lTZb0pZktlrRWUhf50ba/I6lS0nnOuY/DCxEAAAAAkAmS5+x5TtJ18s2zD5RPnKskrZT0kKRbnXMrQosOAAAAAJAxkucscc59IOl3YccBAAAAAMg++jwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyTMAAAAAAEmQPAMAAAAAkATJMwAAAAAASZA8AwAAAACQBMkzAAAAAABJkDwDAAAAAJAEyXOWmFm5mZ1sZveY2Rtmtt7MNprZ62Z2lZltG3aMAAAAAIDMkDxnzzmSHpZ0vvxxfULSPEl7SJog6WUz2ym88AAAAAAAmSJ5zp4qSX+R1Ms5t79z7kzn3DBJ+0h6VVJvSRNDjA8AAAAAkCGS5yxxzk11zv3MOfduk8c/lXRJ8O+pZtau7aMDAAAAALQEyXPbeD24L5O0Q5iBAAAAAADSR/LcNvYM7qslfRVmIAAAAACA9JlzLuwYCp6Z3SXpx5Iec86NTKH8W3EW9ezevXvZvffem9X4csHGjRslSRUVFSFHglRwvvIP5yy/cL7yC+cr/3DO8kshn6/zzz9fH3744VLn3H5hx4LkqHluZWY2XNIF8rXO/y/kcAAAAAAAGSgJO4BCZmZ9JN0vyST92jn3epJVJEnxrjyZ2VsVFRX7Dh06NItR5oZnnnlGklSIz60Qcb7yD+csv3C+8gvnK/9wzvJLIZ+vQqxNL2TUPLcSM+sqP9dzJ0m3OOf+HHJIAAAAAIAMkTy3AjPrIunfkrpJmizpinAjAgAAAAC0BMlzlpnZdpLmSOot6f8k/cQxKhsAAAAA5DWS5ywyszJJ/5D0XUlPSjrbOVcbblQAAAAAgJYiec4SMyuW9ICkoyXNk3Sqc64q3KgAAAAAANnAaNvZc6mkU4K/v5R0p5nFKneFc+7LNosKAAAAANBiJM/Z0ynq71PilpLGyyfXAAAAAIA8QbPtLHHOjXfOWQq3lWHHCgAAsLXaUlut+dUrNL96hapqa8IOB0AeIXkGAADAVmP2x4v07+pl+nf1Ms3+eFHY4QDIIyTPAAAA2Cpsqa3W/Suerv9/2oqnqH0GkDKSZwAAAGwVZn+8SF9Urqv//4vKddQ+A0gZyTMAAAAKXtNa5whqnwGkiuQZAAAABa9prXMEtc8AUkXyDAAAgIIWr9Y5gtpnAKkgeQYAAEBBi1frHEHtM4BUkDwDAACgYFXV1iSsdY64f8XT1D4DSIjkGQAAAAUrWa1zxOeV31D7nIO21FZrfvUKza9ewcUNhI7kGQAAAAWpqrZG01Y8lXJ5ap9zz+yPF+nf1cv07+plXNxA6EieAQAAUJBSrXWOoPY5tzQd6I2B3RA2kmcAAAAUnHRrnSOofc4dTS9+MLAbwkbyDAAAgIKTbq1zBLXPuSHe9GLUPiNMJWEHAAAAAGTb8N0HaljX7zZ6bMPmOi14u0ovL/1c1XXF2qlLB+31nVId0budtmvfUKdUZNQvhS3exY9I7fMpPY4IISps7UieAQAAUHDKikvr/66qcZoxf5MWLtui2jpJ2kGS9M0qafmqWj25eLOO6F2mUYPKVVpi4QSMevFqnSOmrXhKI3Y/RO2KSWXQtrisBgAAgIJVVeP058c3aN7SSOLcXG2d9PzSLZr4+AZV1bi2DRDNJGtyT99nhIXkGQAAAAVrxvxNWr46tT6yy1fX6MH5m1o5IiRSVVuTsNY5goHdEAaSZwAAABSkdZvqtHDZlrTWWbBsi9ZvilNFjVaX6kBvDOyGMJA8AwAAoCDNT9BUO57aOmne2+kl3MiOdKcXo/YZbY3kGQAAAAVp+erqzNb7JLP10DLpTi9G7TPaGskzAAAAClJldWaDf2W6HjKXbq1zBLXPaEskzwAAAChI25RmNu1Upushc+nWOkdQ+4y2xORoAAAAKEi9di3V0lXp10r22q00eSFk1fDdB2pY1+82emzD5joteLtKLy/9XNV1xdqpSwft9Z1SHdG7nbZr31AHWGTUB6JtkDwDAACgIA3at0yPvbI5rUHDioukI/uUtV5QiKmsuOGCRVWN04z5m7RwWWTAtx0kSd+skpavqtWTizfriN5lGjWoXKUltBJA2+EyDQAAAApSx/IiHd47vUT4iN5l6lDOT+SwVNU4/fnxDZqXYKT02jrp+aVbNPHxDaqqoX862g6fDAAAAChYowaVq9euqTW27LVriUYNKm/liJDIjPmbtHx1ak3tl6+u0YPzN7VyREADkmcAAAAUrHYlprEnbKej9i1TcZxfvsVF0lH7lumXJ2xHM+AQrdtUp4XL0ptje8GyLVq/Kc3JvIEM0ecZAAAABa1diWnMkAqdNLC95r29RS/+5zNV1xVr5y4d1Wu3Uh3Zh6bauWB+gqba8dTWSfPe3qIRA9q3TlBAFJJnAAAAbBU6lBdpxID2ar/ufUnS0KFDQ44I0Zavrs5svU+qSZ7RJrjEBgAAACB0ldWZDf6V6XpAukieAQAAAIRum9LM+ptnuh6QLpJnAAAAAKHrtWtp8kKx1tsts/WAdJE8AwAAAAjdoAQjosdTXCQd2Se9ubyBTDFgGAAAAApPdZVUF3vo5qKaKv/HlsrY6xYVSaXtWikwxNOxvEiH9y7TvKWpT1d1RG9GSkfbIXkGAABA4Zn/pDT9jpiLhkT+mDkx9rqjL5WOPqEVgkIyowaV67NvarV8dU3Ssr12LdGoQeVtEBXgcZkGAAAAhWfQcVKnLumv16mLNOjY7MeDlLQrMY09YTsdlaAJd3GRdNS+ZfrlCduptITBwtB2qHkGAABA4SltJw0/K27tc1zDR9FkO2TtSkxjhlTopIHtNe/tLXrxP5+puq5YO3fpqF67lerIPjTVRjhIngEAAFCYBh0n/fNB6esvUytPrXNO6VBepBED2qv9uvclSUOHDg05ImztuGQDIGXrN9Vp9iub9exHe+pfK/fWnx5dr9mLN2v9ptgDsgAAEKpI7XOqqHUGkAA1zwCSqqpxmjF/kxYu26LaOknqIElau6pGS1fV6LGXN+uI3mUaNaicvkcAgNySau0ztc4AkqDmGUBs1VXSlkpVbdysPz+6TvOW+sS5TjVau91Crd1uoerkR8KsrZOeX7pFEx9dp6qNm/26AADkgtJ20vFnJi83/CxqnQEkRPIMILb5T0qXnKwZ/zNby9fUSc5JktZt+5q+6PSMvuj0jNZt+1pDeee0fE2dHrz5cWn+v8KJGQCAWI4clnjk7U5dfA01ACRA8pxFZjbAzH5rZv9nZp+YmTOzyrDjAjIy6Dit67KHFnY+yv9v5mudOyyoL7K2w4L62meZb669oMtgre9/TFtHCwBAfMlqn6l1BpACkufs+n+Srpd0iqRdQ44FaJnSdpo/4KeqLWoYGmHdtq+ppmRD/f81JRsa1z5LqrUSzVvh2ipKAABSE6/2mVpnACkiec6uFyRdI+lESbuEHAvQYsvb7Vn/d9Na54hGtc+R9T6pbvXYAABIS7zaZ2qdAaSI5DmLnHM3Oueuds497pz7LOx4gJaqjMqJm9Y6R8Sqfa6spuYZAJCDmtY+U+sMIA0kzwDi2qbU92OOV+sc0bT2ObIeAAA5pWntM7XOANJA8gwgrl67lkqKX+sc0bT2uddupa0dGgAAmTlymFb0G6wV/QZT6wwgLeYczStbi5k5SVucc9ukud5bcRb17N69e9m9997b8uByzMaNGyVJFRUVIUeCaJtrSvTIil5asetfEibPklRSs532XH2JSlSkkXstVfuSmoTl0bZ4j+UXzld+4XzlH85Zfink83X++efrww8/XOqc2y/sWJAcNc8A4mpfUqPiHRcmTZylSO3z69pj+69InAEAAFBwSpIXQVuLd+XJzN6qqKjYd+jQoW0dUqt75plnJEmF+NzyWVVtjW575g/SltTKr+u0QD8/bogq2vVq3cCQNt5j+YXzlV84X/mHc5ZfCvl8FWJteiGj5hlAXLM/XqQvt6xLuXylrde/Vr/UihEBAAAA4SB5BhBTVW2Npq14Ku317l/xtKpqabYNAACAwkLyDCCm2R8v0heVqdc6R3xe+Y1mf7yoFSICgNyzpbZa86tXaH71Ci4cAkCBo88zgJiG7z5Qw7p+t/mC6mq9f++tkqQ9z/+FVNp8Wqoi47ockKlIMiZJg2qPUrtivqpz2eyPF+nf1cskSQd8vEin9Dgi5IgAAK2Fb2QAMZUVx5mruaRMn/U5VJK0X/tt2zAiYOtAMpY/ttRW6/4VT9f/P23FUxqx+yFc8ACAAkX1UBaZ2QgzezFyCx5uF/2YmY0INUgAQM6KlYzRFDh3Ne3e8kXlOrqtAEABI3nOrh0lHRJ1kyRr8tiO4YQGAMh1JGP5o+mFjggueABA4SJ5ziLn3BTnnCW5TQk7TgBA7iEZyy/xBlXkggcAFC6SZwAAcgDJWP6Id6EjggseAFCYSJ4BAAgZyVh+STaVHxc8AKAwkTwDABAykrH8UVVbk/BCR8T9K57mggcAFBiSZwAAQkQyll+SXeiI+LzyGy54AECBIXkGACBEJGP5o6q2RtNWPJVyeS54AEBhIXkGACAkJGP5JdULHRFc8ACAwkLyDABASEjG8ke6FzoiuOABAIWD5BkAgBCQjOWXdC90RHDBAwAKR0nYAQAAsDVqaTJ2So8jWiEqxDN894Ea1vW7kqSqGqeHXtisRcu3qLauedniIunQXmU6/bD2Ki0xFRl1FQBQCEieAQAIQXQy1tRzzz0nSRo8eHDM5SRjba+suFSST5z/+sQGLV/tJLWL2YTP1UovvO20dl2Vxp6wndoVW5vGCgBoHSTPAACEIJKMxdLO/Ndz+5KytgoHKZoxf5OWr06t2fzy1TV6cP4mjRlS0cpRAQDaAskzAAA5Yv2mOs1bukUvfrSnquuK9fqj69Vrt1Id2adMHcqpbQ7buk11WrhsS1rrLFi2RScNbM/5A4ACQPIMAEDIqmqcZszfpIXLIn1oO0iS1q6q0dJVNXrs5c06oneZRg0qV2kJTYDDMn9p7D7OidTWSfPe3qIRA9q3TlAAgDbDZVAAAEJUVeP058c3aF6CxKy2Tnp+6RZNfHyDqmpc2waIestXV2e23ieZrQcAyC0kzwAAhCiTPrQIR2V1ZhcuMl0PAJBbSJ4BAAhJpn1o129Ks+0wsmKb0syazGe6HgAgt5A8AwAQkpb0oUXb67Vr/BHSE663W2brAQByC8kzAAAhoQ9tfhm0b5mK0/zlVFwkHdmHKccAoBCQPAMAEBL60OaXjuVFOrx3eonwEb2ZZgwACgWf5gAAhIQ+tHmkukraUqlRBxep1y6p/XzqtUuRRh1c5NcFAOQ95nkGACAkvXYt1dJVqY203Wg9+tC2vflPStPvUDtJY61UD3Y9Vwt2GKzaouY/pYrranTE2uc06tWpKv1ntTT6UunoE9o+ZgBAVpE8AwAQkkH7lumxVzanNWgYfWhDMug46Z8PSl9/qXauWmM+vkcnffp3zdvhaC3fro8qi9prm7rN6rXhbR259ll1qFnv1+vURRp0bLixAwCyguQZAICQRPrQzlua+ujZ9KENSWk7afhZ0vQ76h/qULNeIz77h0Z89o/46w0f5dcFAOQ9vn0BAAjRqEHl6rVrateye+1aolGDyls5IsQ16Dhfk5wqap0BoKCQPAMAEIZgAKp2tVs09vulOmqfkrjTIBUXSUftU6Jffr9UpbVbGIAqLJHa51RR6wwABYVm2wAAhCEYgEqS2kkaI+mkkg7x+9C+sl56IFiXAajCE9X3OSFqnQGg4JA8AwAQhhhJWEp9aEnKwlXaTjr+TOlvdyYuN/wsap0BoMDQbBsAgDCk2wQ4gqbA4TtyWOK+z526+IsjAICCQvIMAEBYGIAqP0Vqn+Oh1hkAChLJMwAAYWEAqvwVr/aZWmcAKFgkzwAAhCnV2mdqnXNLvNpnap0BoGCRPAMAEKZkTYAjSMpyT9PaZ2qdAaCgkTwDABA2BqDKT00vfHCBAwAKGskzAABhYwCq/HXkMK3oN1gr+g3mAgcAFDiSZwAAcgEDUOWn0nb6aN9D9NG+h3CBAwAKHMkzAAC5gAGoAADIaSTPAADkCgagAgAgZ5E8AwCQKxiACgCAnFUSdgAAACDKkcO0YulbkqS9qHUGACBnkDwDAJBLggGoJGkvap0BAMgZNNsGAAAAACAJkmcAAAAAAJIgeQYAAAAAIAmS5ywzs23MbIKZLTezSjNbbWb3mlnXsGMDAAAAAGSG5DmLzGwbSU9LukrStpL+IeljST+StMTMeoYYHgAAAAAgQyTP2fVfkg6X9IKkXs65s5xzh0i6XNKOku4NMzgAAAAAQGZInrPEzEol/Tz49xLn3LeRZc65WyS9IekoMxsQRnwAAAAAgMyRPGfPIEnbS3rPOfdqjOUPBfcntllEAAAAAICsIHnOnr7B/ZI4y5c0KQcAAAAAyBMlYQdQQLoF96viLF/VpFxcZvZWnEW93333XfXo0SPN0HJfXV2dJKmoiOs5+YDzlX84Z/mF85VfOF/5h3OWXwr5fK1evVqSdg87DqSG5Dl7tg3uN8VZvrFJuUzUVVdXb/zwww8/bsE2clVkJPL3Qo0CqeJ85R/OWX7hfOUXzlf+4Zzll0I+X7srfv6AHEPynD0W3Lsky5Nyzu3X8nDyS6S2fWt87vmI85V/OGf5hfOVXzhf+Ydzll84X8gVhdf2ITwbgvuKOMvLg/tv4ywHAAAAAOQokufs+Si47xpnedcm5QAAAAAAeYLkOXteD+77x1keefyNNogFAAAAAJBFJM/Zs0DSOkk9zeygGMtPD+4fb7uQAAAAAADZQPKcJc65Kkm3B//ebmb1fZ/N7FeSDpQ03zn3chjxAQAAAAAyZ87FGxwa6TKzbSTNlXSIpE8lzZPUPfh/raRDnXMrQgsQAAAAAJARkucsM7P2ksZJOkd+3ravJT0h6f855wpxfmYAAAAAKHgkzwAAAAAAJEGfZwAAAAAAkiB5BgAAAAAgCZJnAAAAAACSIHkGAAAAACAJkmcAAAAAAJIgeQYAAAAAIAmSZwBpM7NRZjbPzNab2ddm9rCZ9Qw7LqAQmNlMM3NmNjDsWAAAQAPmeQaQMjMrkTRV0tmS3pP0iqS9JfWX9Kmk/ZxzX4cXIZD/zGyFpB6StnPObQ45HAAAEKDmGaExs0eC2pX+Znasmf3bzL4xs43B373DjhHNTJRPnK+W1Ms5N8o5N0DSPZK+I+nnIcaGEJnZiWb2HzOrNLMPwo4nX5nZ9pJ6SlpG4pw/aC2QW8ys1Mw2B+ck2e0PYccLzhnyR0nYAWCr1ldSnaSzJP1CvhbzGUlHSDpG0qNm1ts5VxdeiIgws8GSLpE0xTl3TZPFf5Z0gaRjJTVdhgJnZp0kPSh/7qdJIunLXP/g/tVQo0C6+kuqlfSfsAOBJGlH+c+kaOdKqpI0o8njj7RFQEiKc4a8QLNthMLMOkr6Jvh3saRRzrkVwbKdJb0raTtJfZxzy0IJEo2Y2XOSDpPUwzm3usmynSWtkbTKObd7GPEhPGZ2jqT/ldTJOVcTdjz5wMyGSbpC0kBJ1ZIek3SZpB9L+qOkXznn/hSU/Zt8i4/fO+f+0GQ7gyQ9JWm9pMOcc++12ZOApPrWAl9Less5t3/I4SAGM+sq6WNJrzjnDg47HiTHOUOuotk2wtI3uP9G0imRxFmSnHOfSVoS/LttG8eFGMysl6SjJP2jaeIcKG/jkJAjzGyJpOny79VqM/si5JBynplNkDRH0iBJC4LbKZL+LemQoFh0zfNVkmokXW5mHaK2s4+kf8jXeI4gcQ4NrQVy34HBPS0D8gfnDDmJ5BlhiSTPDzjnPo6xfLvg/pM2igeJnRbcdzOzKU1v8s22JV/7slUK+mutMrM6M9s3Tpn+ZrbJzKrNbGga277azGrjbTdkx0laKOlu+X7vKY9VYGbfCfq43dFaweUaM/uBfDK8WNJezrnjnXMjJfWTtIekU4Oir0XWCS4uTpbUSdLYYDs7yyfgHSWd6Zx7uY2ewlbLzIaZ2VPBLANrg8+/TpIGBEWWBOV+HPTLvDPBth4PyhzTFrGjPhF7I9QokA7OGXISyTPC0i+4f6zpAjMz+RGc1zrnPm3LoBDXkOB+oKQfxridGCxf0WzNrYRzrlrSbZJMvvltI2a2k3w/rfbyTXKfSWW7QZJ0haS/O+eWZi3gLHHOfSGpj6SFzrk1zrm1aaz7qaS/SvppUIta0ILuKrfKN7E+0Tm3KrLMOfeBpLvkXz8fOOe+abL6NZIqJf3KzHaV9Lh8sn2Rc252G4S/VUuztcDC4P4QxWBmwyWNkPSIc+6p1ooZjVCLmX84Z8hJJM8IS6TmeUmMZb3ka55jLUM4+kva5JyzWDdJfwvKLQ4xxlwwSdIGSWOCZFmSZGbtJP2fpN0l3e2cuy2Nbf6XfJPo67MZaLaY2Z7yNaKZNln9o/x30dYw0NyF8sfqtjgXBiMXn5p99gWJ9l8kbS/pdUnflTTBOXd364SKiAxaC7wt3wrnQDPbpsm22snPWrBF0uWtHTvqHRDcU4uZPzhnyEkkz2hzZlYsaT9Ja4L+zU1F+o+RPOeAYDCcLvLzOMdabmqomZ7bJkHlKOfcOvlpu8rkRyaPuEN+FPn5TR5PyMzK5Wv233DOvZ7FULOpv3wi8FYmKzvnPpH0rKRTglr2QnZCcN905NiIyBgP8S5E3CLJyb8fpzjnxmcvNMSSSWsB50difVF+RpP+TTZ5mXzLqv9xzr3f6k8AMrMSSftI+ixoKROrzPeDZvRHRD02MuhWckKsddB6Ujxnb5vZTU0ea2dmy83s6raIE1snkmeEobekbRTVp68Jkufc0im43xBn+UBJu0paLemFNokot02UH9zpZ2bW3swulR9B+SNJpznnqtLY1hnyfVqnZz3K7Okv6c2g2Xqm/iapVNJ5WYkod/VT4gsN3w3umyXPwUWqW+QTNcm/xtD6Mm0tEPksrG+6HTS3/738WB7XZTlOxLe7/OdL3G5Fzrl/S3pSQQufYGrGByT92Dn3eFsEiUaSnjP5rhOHNnnsCvmLVje2UlwAyTNCEWmy/Vqc5QcF9yTPuSEyn127OMt/Etzfw5zcknPuQ0kPydcOTpL0J0mbJJ3knPs8zc1FajzmZi3A7Buglr9X5wb3I1q4nZxlZqXy3VEqXYw5IoNm/pHmv7Fqnm+Rv5jymKTPJZ1nZnu3UrhokGlrgUi/54FRj/0xKP8b59zG7ISHFJQG9/G+wyJ+LekIM/svSY/Kn6dcvnBZyFI5ZwskDQhqqWVmu0v6naTLnHOVrRwftmIkzwhDKsnzeklMu5IbPpGv5drDzMqiFwSjP58r6Sv5JBHezcH9uZKKJf3QOfdaBtsZJD8HcEpNts1sbtD0MJ3bkAziinaQWpg8B81Xv5Q0sOlrrFAENfNfS+oYp3n6FfKDyX3WtIbTzH4l6ZeSFkk6S75WpURbRz/xsPVTZq0FFslPIXaIJAXNgUdLWuCc+5vQllbKT4t5sJktCEZJ36ZpIefcf+QvkvxB0s3Oua1mFoActFLJz9kC+c/MyMBif5L0vHPuH20WJbZKJWEHgK1S3OTZzHpI6izpuVi1M2h7zrlqM3tW0vflp8n5oySZWTf5QbBKJV3onNtqp6mKYbl8otRJ0iTn3EPpbiCoidxF0rvOuS0prvaE/I+OdKxJs3wjzrmdkpdKyTvy/cL3UeEOELNE0vckXWNmF0U+48zsAjUMHtXoQoSZnSl/MWaFfJ/bzWb2F/lasrPM7HrnXKEer1BFtRZYl25rAefct2b2H0n9goslt0mqk/SL1o0aTTnnqsxslKT/kW8JsGesmkkz6ytpuPwFS+arD1Eq58w5t9zMvpR0qJl1kZ/1Y/+2jxZbG5JntDnn3LAEi+nvnJsmSBoq6UYzGybfMuD78n3Xf55JcliozKxIvg9vpK/4QQmKJxJJSlO+KOGcuyHDfeWCr4L7HUONonVdI/8++ql889C35H/s7StfU3mIopKwoN/lVPla+WGRgXOCBPoG+f71f1DDVHHIouDC4deSOpnZzjEGuIzbWiDwgnzN9V/lPwfucs7x3RYC59yT8n2aYzKzvYLld8q3tBpvZvc7575toxDRRLJzFlgo6Uj5i1K3OOfebfXAsNWj2TZyDclzDnLOLZC/Ir9IfoCOQZL+Jekw59ztYcaWg66X7yf5mnxt6qHRI7imoWNwH2+gtkKzPrjvmLBUHnPOPS/fb/k/8lPyfV9+FPuRkuYFxV6V6rtEPCLf9PcE51zTbiz/K9+l4gQzO6zVg996Rb6LrgkGbZOUuLVAlEi/55HyTVB/1xoBomXMbDf5+bofcc79Tr6lR7F86w7ktgXyXVkqJF0bcizYSlDzjFxD8pyjnHP/kk+YEUcwH+xv5Ad0OknScfK1TlfIf8mnY11w3yGN/f9WfjT7dNzgnFuW5jqtIZI0r0tYKs8552ZJmhVj0WOK+rHunFuqhtYLsbZTKalr1gNEU2m1FmhiYdTf4+NNuYPwmNkO8t9rr0j6mSQ55zaY2R8kXWtmk+K0KkBuWCI/A8HlDMKHtmJ0K0UuMbNP5ZOFDs652rDjAVJlZgMlPSffomeoc25BMPjVh/JNkfdxziWadqPp9naS9Jmk95xze6W4zlxJg9MM/Wjn3Nw422uVLwjnnDV9zMzmy/d57ksfXuQSMztN0tXyF6a+lf/B/mdJR8lfGDsjVtcVMztAvv/+25IOdM4xvRiQRWY2TdJuzrmhYceCrQfJMwC0UNDs72VJ35F0vnNuctSy30v6b0l/cc79LM3tfio/5VUH59zmLIacc4KBXyokbZ/GAGlAzjKzOZKGSTo2mEcYQAuZWbGkHSSdJukmSQfR1xltiT7PANACZtZevm/qdyT9KTpxDtwpaaP8vLxd0tz8PPnuNZkOOpYXzKyn/I+hl0icUQiCPtHDJM0gcQayarD8LBGXy7f6IHFGmyJ5BoCWuVd+rtcnFWOAGefcV5Imy4/Km1bNs6TZwf3RLQmwJczsR2b2rJl9YWYbzGyxmY2OUa5nMBfnajOrMrNPzGyyme2Zwm6GBPf/zGrwQBsysz5mdreZ/UvSXfJdNi4JOSygoDjnnnHOFTnn9nLOzQk7Hmx9SJ4BIENm9jtJo+TndR6VoJ/+LfKjJl9iZtuksYuZ8gNondOiQFvme5IelR9t/SBJMyRNM7OzIgXM7GD5fqB7SvqRpD7B/R6SXg2WJ3KO/NyqU7IdPNCGhkq6QH5e2sckfS+4eAYAKBD0eQaAHGZmf5L0S0kDcmWOWDP7p6TNzrnTgnmtl8rPR32Ec64uqpzJjzK+g6R9Y11cMLOu8jV0Dznnzmq6HAAAIFdQ8wwAue16+RF+fxt2IFG2l/Rl8PfBkvaR9MfoxFmSnL86e5P8nMbxap9/LalO0lWtEikAAECWkDwDQA5zzn0un4CeZmb7hh2Pmf1Qvo/3/wYPRfo0vxNnlcjje8TY1nfk58+9yzkXb30AAICcUBJ2AACAxJxz10i6Juw4zOwk+aT5wqgm5OuD+85xVos8vq7pAufcp/IDqQEAAOQ8ap4BAEmZ2ShJD0q6qMl0XAskVUo6Mc6qIyVVSVrYuhECAAC0LpJnAEBCZvYT+ZGwz3POTYle5pz7RtKNkn5mZvs3WW9/SRdLujEoBwAAkLdIngEAcZnZryT9RdJYSXPNbJfgtmOwfFtJf5X0rKT/397dx1p2lXUc//6czpS+Tl8UmkJpqa8tYotBhFTSViZGC9RUpWhD2lEhaWhAQYwVSmYKKIIIQgyWhsZWAkjQEEs12FqdYosWm1CKCg1Ghr6lKn2F2pa3xz/WOvRyeu/Z595z7pk7zPeT7Ox79l7r7LXW3OTOs9fb1Um29evbgKuB64BLejpJkqS9lltVSZJWlGQ3cOwyt75UVccl2QnsWHL9uqo6Lcku4NQl1y+uqp3rVU5JkqT1ZvAsSZIkSdIAh21LkiRJkjTA4FmSJEmSpAEGz5IkSZIkDTB4liRJkiRpgMGzJEmSJEkDDJ4lSZIkSRpg8CxJkiRJ0gCDZ0mSJEmSBhg8S5IkSZI0wOBZkiRJkqQBBs+SJEmSJA0weJYkSZIkaYDBsySJJDuTVJLa02WZpyTbe91Om/F7vt0+Q8d8Sj67edVdkiQ1++3pAkiStI62A6f2n3fN6Tv/e07fs962M/+6S5K0zzJ4liRpFarqqD1dBkmStHgO25YkSZIkaYDBsyRpoiS7+nzenWlenuTGJA8m+UqSf07y0inzb0lyYZJbkjyU5L4k1yT5uQn5d/f82yekubynubx/3t7nH4+GLe9YZn7ycWtqkDVKclSSP0jymSQPJHkkyX8leV+SE1fIszXJLyf5QJLPJrm35/tSkg8mec4yeVZV97W079i9pf++m5P8VpKbktzfr582azvMKskpS+r+4hXS/GSSr/Y0b1uPckiS9m4Gz5KkaW0CPgpcCvw4UMDBwHOA9ye5eCD/FuDvgbcAJwBfAw4DtgF/m2TnHMv6MG1u8tf754f656XHN+f4vImSvBD4AvA7wI8BBwDfAJ4G/Drw6STnLpP11cCHgHOAHwU29+tPBX4F+GSSV43l2VN1fwJtbvXbgZOAb40nmKEdZlJVNwBX9o9vTLJprFw/DFwFHARc0csnSdJ3MHiWJE3rAuA02kJUh1bVVuAY4GP9/kVJfnBC/lcAzwbOBw6pqsNpQeBf9vs7kpw5j4JW1Yf73ORP9ktvr6qjxo7b5/GsIUmeDfwV7UXDe2kvDg6oqoOBY4H30F4sXJbkWWPZ7wbeSXtBcXhVHUILOI8H3tXTvCPJM0cZ9mDdL6AFxL9K+/04Avhe4BaYuR3m4ULaS4MfAb49UiLJ0cDf9bJeBbysqjbMqumSpI3D4FmSNK3DgbOq6oqqehigqu4AXgzcRfubcvaE/FuBV1TVe6vqkZ7/duAlwCd6mresV+HnJcndE46nL5PlT2hB4Zuq6vyq+nxVfROgqm6rqguAd9MW8bxoacaquqSqXlNVN1bV/f1aVdUXq+o3aQHnJlrguqcdDJxTVZcv+f24p6ru7ffX3A7zUFWfAy7vH3f0IeaHAR+nBe/XA2dX1Tfm/WxJ0ncHg2dJ0rRuqKp/HL9YVY/Seu6g9Tyu5Hbgz5bJ/y3gzf3jiUmeMWtB19mTJhyblyZMchLwE7Qh1H804Tv/vJ+3jQ8pHvA3/fxTq8izXv69qj623I0FtMO0dtCGtT+N9sLhr4FnAJ8FXjQK+scluTPJR9ahPJKkvYhbVUmSpnXjhHt39fMRE9LsmjAc9hO0ua/7Ac+iBTMbUlVlFclHQe33ALcmK2YdBYoHAUcC/zO6keR42pD304HvBw7h8S+/n7KKMq2XGybcm7kd5qGq7kzybtqc5nf2y7uBnx317I9L8kTgaOAz8yyLJGnvY/AsSZrWVybcGw113TwhzZ0r3aiqR5PcQ+u9feIayrZRHd3Pm2h1m8aBox+SnEVbMGz/JfcfBB6hLdi2hTac/qCZSzq7SYHuTO0wZ+8CfpsWyN8L/ExV3TUh/f/S5pl/fUIaSdI+wGHbkqRF2RcXYRr1pH6+qjLlsRsgyZG0Obr7A/9AW6ztwKraWlVP6ouCLbvt0h4yaQXvNbfDPCXZj7Za/Oj/PwfShnGvqM8xf2Q0P1uStO8yeJYkLcqKQ4uT7E8bpguP78Ec9Wo/YcJ3b52hXOvp7n4+Pslqe4fPAA4F7qPNx71umTm5R81aQBbTvrO0w1ykjRV/H/BCWm/yF2l1nrjFWpLL+p7k/p9JkvZx/iGQJC3KqVl5suvzeGwq0U1j9+7r52OWy9iDmpW2NhrtNbyaecrzNJoHvAU4a5V5R/W9tar+b4U02ybkn7bus7TvtGZph3l5G3Ae8FXgBcDr+/Xzkpw4Id/JwC19YTtJ0j7M4FmStChPpQUv36EHZ6/rHz9XVeOLhY0WajprheD7PFbu1X6wnw9bXVHn5ibg0/3n30vyfZMSJ1m64NoD/fxDSR7XK5zkZOCcCV83bd1nad9pzdIOM0vyWuC1tHnLv1hV/wr8BW0P6k2ssEVaH+b9dODmeZZHkrR3MniWJC3KA8CfJnn5KBhMcgxtQazTe5rXL5PvQ/18AnBpnwtMkkOTvBq4hLbw03L+rZ/PSPLkOdRhVfrq4ucDj9JeHtyY5JeSLF0U7MlJXprkGuCtS7JfTes9PgL4wKj8SbYkObvfn7SI27R1n6V9pzJjO4zuH5ek+rFz2mcnOZfW61zA9qq6ekmZ3tCTnZnklGWyn0Cbc37ztM+TJH33MniWJC3Ke2g9kJcCDya5F7gNOLvff3NVfXQ8U1Vdy2P7/74M+HKS+2jDjd/Rv2/Z/YWBK2grU/8AcFuSu5Ps7sdCtneqqk8BLwLuoe0v/BFa/b+c5CHgDuD9jA3BrqovAH/YP/4CcEeS+2nDjj/cz6+a8Oip6j5j+05tre0wiyRnAJfRhq6/pqo+OFamK3lsC7bHBey0Idtg8CxJwuBZkrQ4XwOeTxuifSutR+8B4FrgBVX1hgl5fw34DVoQ8zDt79cNwEuq6pUrZeoB6OnAlbRFoo4Eju3HwrZrrKpraEHs7wLX0+p9GK1n+T9oAd6ZwCvH8l0InAt8ilbvzcB/Ar8PPJPH9tde7pmrqfua2ne11toO3dLe838ZelaS59IC9P2At1bVH6+QdDTa4ZQkPz927+Retg2777gkaXHSRi1JkrQ+kuwCTgUurqqde7Y02lsluQh4E3B9VT1vQc+8Fji6qk5YxPMkSRubPc+SJGlv8NP9/LqJqebrJByyLUnqDJ4lSdKG1vcBfy7w8ar6pwU98xjaUPebF/E8SdLGt7D5XpIkSWtRVY8CByz4sSf1880Lfq4kaYMyeJYkSRpTVVfRVumWJAlwwTBJkiRJkgY551mSJEmSpAEGz5IkSZIkDTB4liRJkiRpgMGzJEmSJEkDDJ4lSZIkSRpg8CxJkiRJ0gCDZ0mSJEmSBhg8S5IkSZI0wOBZkiRJkqQBBs+SJEmSJA0weJYkSZIkaYDBsyRJkiRJAwyeJUmSJEkaYPAsSZIkSdIAg2dJkiRJkgb8P+5ToavCTvaqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4), tight_layout=True, dpi = 150)\n",
    "xlabs = [r'$ \\eta$', r'$ \\theta$', r'$ X$ $(=\\frac{f}{2\\Omega})$', r'$ dx$', r'$ dy$', r'$ \\tau_x$', r'$ \\tau_y$' ]\n",
    "ylabs = [r'$\\Delta(x_i) = 0.5\\sigma(x_i)$', r'$\\Delta(x_i) = 1\\sigma(x_i)$', r'$\\Delta(x_i) =2 \\sigma(x_i)$', ]\n",
    "\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1,)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2,)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3, )\n",
    "plt.xticks([0,1,2,3,4,5,6], xlabs)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.ylabel(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta (x_i)}$ ')\n",
    "plt.title(r'$\\frac{\\Delta \\mathbf{M}(x_i,x_j)}{\\Delta (x_i)}$, $(x_i, x_j) \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$, $ \\mathbf{M} =$ Mean Absolute Error ')\n",
    "plt.xlim([-0.5,6.5])\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "employed-wealth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f06983faee0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4), tight_layout=True, dpi = 150)\n",
    "xlabs = [r'$ \\eta$', r'$ \\theta$', r'$ X$ $(=\\frac{f}{2\\Omega})$', r'$ dx$', r'$ dy$', r'$ \\tau_x$', r'$ \\tau_y$' ]\n",
    "ylabs = [r'$\\Delta(x_i) = 0.5\\sigma(x_i)$', r'$\\Delta(x_i) = \\sigma(x_i)$', r'$\\Delta(x_i) =2 \\sigma(x_i)$', ]\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1)\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2)\n",
    "plt.plot(np.array(list(mev_ds_mae.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3)\n",
    "plt.hlines(np.array(list(mev_ds_mae.values()))[-1], -0.5, 6.5, linestyle = 'dashed', linewidth=3, alpha=0.6, color = colors['11'])\n",
    "plt.xticks([0,1,2,3,4,5,6], xlabs)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.ylabel(r'Mean Absolute Error $[cm/s]$ ')\n",
    "plt.yscale('log')\n",
    "plt.xlim([-0.5,6.5])\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'$ \\mathbf{M} =$ Mean Absolute Error $[cm/s]$')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1,)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2,)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3, )\n",
    "plt.xticks([0,1,2,3,4,5,6], xlabs)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.ylabel(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta (x_i)}$ ')\n",
    "plt.title(r'$\\frac{\\Delta \\mathbf{M}(x_i,x_j)}{\\Delta (x_i)}$; $(x_i, x_j) \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$,' )\n",
    "plt.xlim([-0.5,6.5])\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "grateful-leather",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '$\\\\frac{\\\\Delta \\\\mathbf{M_k}(x_i,x_j)}{\\\\Delta (x_i)}$, $(x_i, x_j) \\\\in \\\\{\\\\eta,\\\\theta,X,dx,dy,\\\\tau_x,\\\\tau_y\\\\} ; \\\\mathbf{M_k} \\\\in \\\\{mae, mse, accuracy\\\\}$ ')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4), tight_layout=True, dpi = 150)\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3)\n",
    "plt.xticks([0,1,2,3,4,5,6], xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.title(r'Mean Absolute Error ')\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$')\n",
    "\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.plot(np.array(list(dMdvar2.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1)\n",
    "plt.plot(np.array(list(dMdvar2.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2)\n",
    "plt.plot(np.array(list(dMdvar2.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3)\n",
    "plt.xticks([0,1,2,3,4,5,6] , xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.title(r'Mean Square Error ')\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in {\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y}$')\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.plot(np.array(list(dMdvar3.values()))[0:-1][2::3], 'v', label= ylabs[0], color=color1)\n",
    "plt.plot(np.array(list(dMdvar3.values()))[0:-1][::3], 'o', label= ylabs[1], color=color2)\n",
    "plt.plot(np.array(list(dMdvar3.values()))[0:-1][1::3], '^', label= ylabs[2], color=color3)\n",
    "plt.xticks([0,1,2,3,4,5,6] , xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9, loc=0)\n",
    "plt.xlabel(r'Input Feature, $x_i$ ')\n",
    "plt.title(r'Model Accuracy ')\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in {\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y}$')\n",
    "\n",
    "plt.suptitle(r'$\\frac{\\Delta \\mathbf{M_k}(x_i,x_j)}{\\Delta (x_i)}$, $(x_i, x_j) \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\} ; \\mathbf{M_k} \\in \\{mae, mse, accuracy\\}$ ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "olive-filling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '$\\\\frac{\\\\Delta \\\\mathbf{M}}{\\\\Delta \\\\mathbf{x_i}}$, $\\\\mathbf{x_i} \\\\in \\\\{\\\\eta,\\\\theta,X,dx,dy,\\\\tau_x,\\\\tau_y\\\\}$')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4), tight_layout=True, dpi = 150)\n",
    "\n",
    "xlabs = [r'$\\Delta \\eta$', r'$\\Delta \\theta$', r'$\\Delta X$', r'$\\Delta dx$', r'$\\Delta dy$', r'$\\Delta \\tau_x$', r'$\\Delta \\tau_y$' ]\n",
    "ylabs = [r'$\\Delta = 0.5\\sigma$', r'$\\Delta = 1\\sigma$', r'$\\Delta =2 \\sigma$', ]\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][2::3], 'v', label= ylabs[0])\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][::3], 'o', label= ylabs[1])\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][1::3], '^', label= ylabs[2])\n",
    "plt.xticks([0,1,2,3,4,5,6], xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'Mean Absolute Error ')\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$')\n",
    "\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][2::3], 'v', label= ylabs[0])\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][::3], 'o', label= ylabs[1])\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][1::3], '^', label= ylabs[2])\n",
    "plt.xticks([0,1,2,3,4,5,6] , xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'Mean Square Error ')\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in {\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y}$')\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][2::3], 'v', label= ylabs[0])\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][::3], 'o', label= ylabs[1])\n",
    "plt.plot(np.array(list(dMdvar.values()))[0:-1][1::3], '^', label= ylabs[2])\n",
    "plt.xticks([0,1,2,3,4,5,6] , xlabs)\n",
    "plt.grid()\n",
    "plt.legend(fontsize=9)\n",
    "plt.title(r'Model Accuracy ')\n",
    "# plt.title(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in {\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y}$')\n",
    "\n",
    "plt.suptitle(r'$\\frac{\\Delta \\mathbf{M}}{\\Delta \\mathbf{x_i}}$, $\\mathbf{x_i} \\in \\{\\eta,\\theta,X,dx,dy,\\tau_x,\\tau_y\\}$')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "covered-calvin",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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