{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latitude-dependent grey radiation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a quick example of using the `climlab.GreyRadiationModel` with a latitude dimension and seasonally varying insolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import climlab\n",
    "from climlab import constants as const"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (90, 1) \n",
      "  Tatm: (90, 30) \n",
      "The subprocess tree: \n",
      "Grey Radiation: <class 'climlab.model.column.GreyRadiationModel'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.FixedInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = climlab.GreyRadiationModel(name='Grey Radiation', num_lev=30, num_lat=90)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (depth: 1, depth_bounds: 2, lat: 90, lat_bounds: 91, lev: 30, lev_bounds: 31)\n",
       "Coordinates:\n",
       "  * lat           (lat) float64 -89.0 -87.0 -85.0 -83.0 ... 83.0 85.0 87.0 89.0\n",
       "  * depth         (depth) float64 0.5\n",
       "  * lat_bounds    (lat_bounds) float64 -90.0 -88.0 -86.0 ... 86.0 88.0 90.0\n",
       "  * depth_bounds  (depth_bounds) float64 0.0 1.0\n",
       "  * lev           (lev) float64 16.67 50.0 83.33 116.7 ... 916.7 950.0 983.3\n",
       "  * lev_bounds    (lev_bounds) float64 0.0 33.33 66.67 ... 933.3 966.7 1e+03\n",
       "Data variables:\n",
       "    Ts            (lat, depth) float64 288.0 288.0 288.0 ... 288.0 288.0 288.0\n",
       "    Tatm          (lat, lev) float64 200.0 202.7 205.4 ... 272.6 275.3 278.0</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-70d118d8-d0da-49e3-92b3-032eda01117c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70d118d8-d0da-49e3-92b3-032eda01117c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>depth</span>: 1</li><li><span class='xr-has-index'>depth_bounds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 90</li><li><span class='xr-has-index'>lat_bounds</span>: 91</li><li><span class='xr-has-index'>lev</span>: 30</li><li><span class='xr-has-index'>lev_bounds</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b1c64bd5-00e9-4b40-9708-e52bb5e1d248' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1c64bd5-00e9-4b40-9708-e52bb5e1d248' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.0 -87.0 -85.0 ... 87.0 89.0</div><input id='attrs-85df8070-e7fa-40fa-8a6c-6e5c1dbb3cc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85df8070-e7fa-40fa-8a6c-6e5c1dbb3cc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe77ad3-5a9c-434a-a95c-2007d06c732b' class='xr-var-data-in' type='checkbox'><label for='data-7fe77ad3-5a9c-434a-a95c-2007d06c732b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([-89., -87., -85., -83., -81., -79., -77., -75., -73., -71., -69., -67.,\n",
       "       -65., -63., -61., -59., -57., -55., -53., -51., -49., -47., -45., -43.,\n",
       "       -41., -39., -37., -35., -33., -31., -29., -27., -25., -23., -21., -19.,\n",
       "       -17., -15., -13., -11.,  -9.,  -7.,  -5.,  -3.,  -1.,   1.,   3.,   5.,\n",
       "         7.,   9.,  11.,  13.,  15.,  17.,  19.,  21.,  23.,  25.,  27.,  29.,\n",
       "        31.,  33.,  35.,  37.,  39.,  41.,  43.,  45.,  47.,  49.,  51.,  53.,\n",
       "        55.,  57.,  59.,  61.,  63.,  65.,  67.,  69.,  71.,  73.,  75.,  77.,\n",
       "        79.,  81.,  83.,  85.,  87.,  89.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5</div><input id='attrs-f3f271db-4f0e-4d04-a751-60fdd4415089' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3f271db-4f0e-4d04-a751-60fdd4415089' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df01e9c6-2394-4c3e-8c05-0f24d94d64fc' class='xr-var-data-in' type='checkbox'><label for='data-df01e9c6-2394-4c3e-8c05-0f24d94d64fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([0.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_bounds</span></div><div class='xr-var-dims'>(lat_bounds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.0 -86.0 ... 88.0 90.0</div><input id='attrs-571e82e3-d724-4864-9265-073eab686484' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571e82e3-d724-4864-9265-073eab686484' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e2d85eb-1d52-4ccc-a9ef-e2b60c7505d2' class='xr-var-data-in' type='checkbox'><label for='data-9e2d85eb-1d52-4ccc-a9ef-e2b60c7505d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([-90., -88., -86., -84., -82., -80., -78., -76., -74., -72., -70., -68.,\n",
       "       -66., -64., -62., -60., -58., -56., -54., -52., -50., -48., -46., -44.,\n",
       "       -42., -40., -38., -36., -34., -32., -30., -28., -26., -24., -22., -20.,\n",
       "       -18., -16., -14., -12., -10.,  -8.,  -6.,  -4.,  -2.,   0.,   2.,   4.,\n",
       "         6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,  24.,  26.,  28.,\n",
       "        30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,  48.,  50.,  52.,\n",
       "        54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,  72.,  74.,  76.,\n",
       "        78.,  80.,  82.,  84.,  86.,  88.,  90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bounds</span></div><div class='xr-var-dims'>(depth_bounds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0</div><input id='attrs-4d475128-f352-4ce7-8a29-c013a5385203' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d475128-f352-4ce7-8a29-c013a5385203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13fad87e-dee9-4a18-b0ae-20faa8a2eca4' class='xr-var-data-in' type='checkbox'><label for='data-13fad87e-dee9-4a18-b0ae-20faa8a2eca4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([0., 1.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.67 50.0 83.33 ... 950.0 983.3</div><input id='attrs-907e3ec9-df84-4322-bba4-120214bd579a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-907e3ec9-df84-4322-bba4-120214bd579a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-002eded9-1792-46d4-84e0-265f06a81400' class='xr-var-data-in' type='checkbox'><label for='data-002eded9-1792-46d4-84e0-265f06a81400' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mb</dd></dl></div><div class='xr-var-data'><pre>array([ 16.666667,  50.      ,  83.333333, 116.666667, 150.      , 183.333333,\n",
       "       216.666667, 250.      , 283.333333, 316.666667, 350.      , 383.333333,\n",
       "       416.666667, 450.      , 483.333333, 516.666667, 550.      , 583.333333,\n",
       "       616.666667, 650.      , 683.333333, 716.666667, 750.      , 783.333333,\n",
       "       816.666667, 850.      , 883.333333, 916.666667, 950.      , 983.333333])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev_bounds</span></div><div class='xr-var-dims'>(lev_bounds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 33.33 66.67 ... 966.7 1e+03</div><input id='attrs-1d6c8961-3e63-4a48-9386-5b04cb6e5111' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d6c8961-3e63-4a48-9386-5b04cb6e5111' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-767d5b93-749a-415d-a140-012f5da03b50' class='xr-var-data-in' type='checkbox'><label for='data-767d5b93-749a-415d-a140-012f5da03b50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mb</dd></dl></div><div class='xr-var-data'><pre>array([   0.      ,   33.333333,   66.666667,  100.      ,  133.333333,\n",
       "        166.666667,  200.      ,  233.333333,  266.666667,  300.      ,\n",
       "        333.333333,  366.666667,  400.      ,  433.333333,  466.666667,\n",
       "        500.      ,  533.333333,  566.666667,  600.      ,  633.333333,\n",
       "        666.666667,  700.      ,  733.333333,  766.666667,  800.      ,\n",
       "        833.333333,  866.666667,  900.      ,  933.333333,  966.666667,\n",
       "       1000.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8abb8f01-f6a5-41a0-8094-e3bebb1e2edb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8abb8f01-f6a5-41a0-8094-e3bebb1e2edb' class='xr-section-summary' >Data variables: <span>(2)</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>Ts</span></div><div class='xr-var-dims'>(lat, depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>288.0 288.0 288.0 ... 288.0 288.0</div><input id='attrs-a55b79d0-608e-4878-a519-bc322baf0883' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a55b79d0-608e-4878-a519-bc322baf0883' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-707c7e3d-8ffc-4357-96b8-8201dbb6cc10' class='xr-var-data-in' type='checkbox'><label for='data-707c7e3d-8ffc-4357-96b8-8201dbb6cc10' 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([[288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "...\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.],\n",
       "       [288.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Tatm</span></div><div class='xr-var-dims'>(lat, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 202.7 205.4 ... 275.3 278.0</div><input id='attrs-0926b41b-35fb-4a8b-b03b-b5e410ecaa43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0926b41b-35fb-4a8b-b03b-b5e410ecaa43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3596538-d44c-4385-b6aa-9970237bf4d8' class='xr-var-data-in' type='checkbox'><label for='data-a3596538-d44c-4385-b6aa-9970237bf4d8' 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([[200.        , 202.68965517, 205.37931034, ..., 272.62068966,\n",
       "        275.31034483, 278.        ],\n",
       "       [200.        , 202.68965517, 205.37931034, ..., 272.62068966,\n",
       "        275.31034483, 278.        ],\n",
       "       [200.        , 202.68965517, 205.37931034, ..., 272.62068966,\n",
       "        275.31034483, 278.        ],\n",
       "       ...,\n",
       "       [200.        , 202.68965517, 205.37931034, ..., 272.62068966,\n",
       "        275.31034483, 278.        ],\n",
       "       [200.        , 202.68965517, 205.37931034, ..., 272.62068966,\n",
       "        275.31034483, 278.        ],\n",
       "       [200.        , 202.68965517, 205.37931034, ..., 272.62068966,\n",
       "        275.31034483, 278.        ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79850da6-1998-46a6-a938-03617d4b1868' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79850da6-1998-46a6-a938-03617d4b1868' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (depth: 1, depth_bounds: 2, lat: 90, lat_bounds: 91, lev: 30, lev_bounds: 31)\n",
       "Coordinates:\n",
       "  * lat           (lat) float64 -89.0 -87.0 -85.0 -83.0 ... 83.0 85.0 87.0 89.0\n",
       "  * depth         (depth) float64 0.5\n",
       "  * lat_bounds    (lat_bounds) float64 -90.0 -88.0 -86.0 ... 86.0 88.0 90.0\n",
       "  * depth_bounds  (depth_bounds) float64 0.0 1.0\n",
       "  * lev           (lev) float64 16.67 50.0 83.33 116.7 ... 916.7 950.0 983.3\n",
       "  * lev_bounds    (lev_bounds) float64 0.0 33.33 66.67 ... 933.3 966.7 1e+03\n",
       "Data variables:\n",
       "    Ts            (lat, depth) float64 288.0 288.0 288.0 ... 288.0 288.0 288.0\n",
       "    Tatm          (lat, lev) float64 200.0 202.7 205.4 ... 272.6 275.3 278.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "insolation = climlab.radiation.DailyInsolation(domains=model.Ts.domain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add_subprocess('insolation', insolation)\n",
    "model.subprocess.SW.flux_from_space = insolation.insolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (90, 1) \n",
      "  Tatm: (90, 30) \n",
      "The subprocess tree: \n",
      "Grey Radiation: <class 'climlab.model.column.GreyRadiationModel'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compute_diagnostics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16c3f65e0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.lat, model.SW_down_TOA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.Tatm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 0.9993368783782377 years.\n"
     ]
    }
   ],
   "source": [
    "model.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16c4f2070>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.lat, model.Ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 1.9986737567564754 years.\n"
     ]
    }
   ],
   "source": [
    "model.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16c562910>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wb93PonW7uP2cbvawkjmley/sTkzr5jz09kbKK11OxzEnYcXdUFJeyYPvbKBrZEvuGJvodBzj5VqGBPH7n/Zmy95CXrWpgb2WJ2uoxonI5yKSISLpIjLD3T5QRFaKSJqIpIrIsGrHzBSRTBHZIiIXNeQ3YM7c819sJ/tAMX+5vK/NHWM8ckHvDoztEcX/fLKVvUds7Ls38uTKvQK4V1V7ASOA6SLSG3gc+KOqDgQedn+Oe9skoA8wHnhWRKxieKmcgmKeXZbJpf07MTIx0uk4xkeICH+4rA9llS4eeT/D6TimBrUWd1Xdrapr3a8LgQwgBlCglXu3COD4s8kTgHmqWqqqO4BMYBjGK/3l/U0EiPDgJbYWqqmbLu1acvs53ViyPo9vtu13Oo75kTr1uYtIPDAISAHuBp4QkRzgSWCme7cYIKfaYbnuth9/rWnu7pzU/Pz8uic3Z+yL7/L5MH0vvzw/0ca0m9Ny+7ndiGvbnIcXp9vNVS/jcXEXkTBgIXC3qh4BbgfuUdU44B7gpeO71nD4CavtquosVU1W1eSoKFvZp7GVVbj445J0EiJbctPZCU7HMT4qNDiQhy/tQ+a+o8xeme10HFONR8VdRIKpKuyzVXWRu3kKcPz1m3zf9ZILVJ+QJJbvu2yMl3jl6x1s31/E73/am5AguyViTt+4Xu05OzGS//l0K4eKbd53b+HJaBmh6qo8Q1WfqrYpDzjH/fo8YKv79RJgkoiEiEgCkASsqr/I5kztP1rKM59lcn7P9pzbo73TcYyPExEeurQXR46V87+fbq39ANMogjzYZxQwGdggImnutgeAW4D/FZEgoASYBqCq6SKyANhE1Uib6apaWd/Bzen7n0++o7i8kpkX201UUz96dmzFpGGdeX1FNtcN70Ji+zCnIzV5tRZ3VV1Ozf3oAENOcswjwCNnkMs0kK17C5m7Kofrh3e2N6CpV7+6oDvvpuXx16UZvHzjUKfjNHn2hGoT89elGbRoFsiMcd2djmL8TGRYCHeel8hnm/fx1VYbAec0K+5NyFdb8/l8Sz6/PC+Rti2bOR3H+KEbR8UT17Y5jy7djMt1wiA504isuDcRlS7lkfcziGvbnCkj452OY/xUSFAg917Qg027j/DutzZIzklW3JuIxWm72LynkN9c1NOGPpoGddmAaHp1asWTH22hrMIebHKKFfcmoLSikqc+/o6+Ma24pF8np+MYPxcQIPz2Jz3JKTjGnBR7sMkpVtybgLkpO8k9eIzfXNTTFuEwjWJMUiQju7XjH59l2pJ8DrHi7ueOllbwf59lMqJrW0Yn2ayPpnGICPeP70lBURkvfLXD6ThNkhV3P/fy8h0cKCrjN+N7UvWwsTGNY0Bcay7p14mXvtpOQZFNS9DYrLj7sYKiMmZ9uZ0Le3dgcOc2TscxTdA9FyRRXF7J819uczpKk2PF3Y89/8U2isoq+PVFPZyOYpqoxPbhTBgQzWvfZJNfWOp0nCbFiruf2n+0lNdWZHPZgGi624LXxkEzxnWnrNLFv5bZ1XtjsuLup57/YhulFZXcdX6S01FME5cQ2ZKfDYrhjZRsW2+1EVlx90P5haW8vjKbCQNj6BZlk4MZ5804PwmXS/nn55lOR2kyrLj7oee/2EZZhYtfnpfodBRjAIhr24KrkuOYtyqHXYeOOR2nSbDi7mf2FZbwRko2lw+KoatdtRsvcud5ibhUmfWF9b03Bivufua5Zdspr1TuOs/62o13iWndnCsHxzJ3dQ77Cq3vvaF5ssxenIh8LiIZIpIuIjPc7fNFJM39kVVtlSZEZKaIZIrIFhG5qAHzm2r2Hy1lzqpsLh8YQ3xkS6fjGHOC28/tRkWlixftqdUG58kyexXAvaq6VkTCgTUi8rGqTjy+g4j8HTjsft0bmAT0AaKBT0Skuy211/BeXr6D0goXd4zt5nQUY2oUH9mSywZE88bKbG47p5utK9CAar1yV9XdqrrW/boQyABijm93L6B9NTDX3TQBmKeqpaq6A8gEhtV3cPNDh4+V8/qKbC7u28lGyBivNn1sIsVllbzytV29N6Q69bmLSDwwCEip1jwa2Kuqx5c9jwFyqm3Ppdovg2pfa5qIpIpIan6+Lcl1pl5fkUVhaYVdtRuvl9QhnJ/07ci/v87i8DGbMbKheFzcRSQMWAjcrapHqm26hu+v2qHmxbRPWG9LVWeparKqJkdFRXkaw9SguKyCl7/OYmyPKPpERzgdx5haTR+bSGFpBa+vyHI6it/yqLiLSDBVhX22qi6q1h4EXAHMr7Z7LhBX7fNYwNbbakBzV+VQUFTGnTau3fiIvjERjO0RxStfZ1FSbrfjGoIno2UEeAnIUNWnfrR5HLBZVXOrtS0BJolIiIgkAEnAqvoKbH6otKKSF77czvCEtgzp0tbpOMZ47NZzunGgqIy31uTWvrOpM0+u3EcBk4Hzqg19vNi9bRI/7JJBVdOBBcAm4ANguo2UaTiL1+Wx50gJ08faVbvxLcMT2jIgrjUvfLWdStcJPbfmDNU6FFJVl1NzPzqqeuNJ2h8BHjmjZKZWLpcy66vt9O7UylZZMj5HRLhtTFdun72WD9P3cLGt71uv7AlVH7bsu31k7jvKtDFdbZUl45Mu7NOR+HYteP6Lbaja1Xt9suLuw2Z9uZ3oiFAu6W9XPMY3BQYIt4zpyvrcw6zcXuB0HL9ixd1HfZt7iJXbC5h6dgLBgfbPaHzXlYNjiQxrZkvx1TOrCj7q+S+3Ex4SxMShcbXvbIwXCw0O5MaR8Szbks93ewudjuM3rLj7oJyCYv6zYTfXjuhMeGiw03GMOWPXDe9CSFCATUlQj6y4+6CXlu8gMED4xcgEp6MYUy/atGzGFYNjWbR2FwVFZU7H8QtW3H3MkZJy3kzN4dL+0XSMCHU6jjH1ZuqoeEorXMxJyXY6il+w4u5jFqzOoaiskqmj7Krd+JekDuGM6R7FayuyKatwOR3H51lx9yGVLuXVFVkkd2lDv1ibIMz4n5vOTmBfYSnvb7DpqM6UFXcf8mnGXnIKjjH1bLtqN/5pTFIkie3DeGn5Dnuo6QxZcfchL3+9g5jWzbmwdwenoxjTIESEqaMS2LjrCKt22ENNZ8KKu4/YlHeEldsLmHxWF4LsoSXjx342KIbWLYJ51eZ6PyNWJXzEK1/vIDQ4gEn20JLxc82bBTIxOY4P0/ey+/Axp+P4LCvuPuDA0VIWr8/jysGxtG5hCwob/3f9iC64VJmbstPpKD7LirsPWJCaS1mFiykj452OYkyjiGvbgvN7tmfOqp2UVthyEKfDiruXq3Qpb6zMZnhCW7p3CHc6jjGNZvJZ8ew/WsYHG/c4HcUnWXH3csu27GPXoWPccFa801GMaVSjEyNJiGzJq99kOR3FJ3myhmqciHwuIhkiki4iM6pt+6WIbHG3P16tfaaIZLq3XdRQ4ZuC11Zk06FVCBf2seGPpmkJCBAmj+jC2p2H2LjrsNNxfI4nV+4VwL2q2gsYAUwXkd4iMhaYAPRX1T7AkwAi0puqtVX7AOOBZ0UksEHS+7nsA0V88V0+1wzrbHO2mybpyiGxNA8O5DUbFllntVYMVd2tqmvdrwuBDCAGuB14TFVL3dv2uQ+ZAMxT1VJV3QFkAsMaIry/e2NlNkEBwjXDOjsdxRhHRDQP5meDY1iclsfh4nKn4/iUOl0Oikg8MAhIAboDo0UkRUS+EJGh7t1igJxqh+W62378taaJSKqIpObn559WeH92rKySBam5XNSnIx1a2eyPpum6fngXSitcLFyb63QUn+JxcReRMGAhcLeqHgGCgDZUddXcByyQqlWaa1qp+YRJIlR1lqomq2pyVFTUaYX3Z+9+m8fhY+VMPquL01GMcVTv6FYMjGvN7JRsm2+mDjwq7iISTFVhn62qi9zNucAirbIKcAGR7vbqj1HGAjbFWx3NSdlJYvswhie0dTqKMY67bnhntuUX2XwzdeDJaBkBXgIyVPWpapveAc5z79MdaAbsB5YAk0QkREQSgCRgVT3n9mub8o6QlnOIa4d1pur0G9O0Xdo/mlahQcy2J1Y9FuTBPqOAycAGEUlztz0AvAy8LCIbgTJgilb9nyldRBYAm6gaaTNdVe0RszqYsyqbkKAArhh8wq0KY5qk5s0CuWJwLHNSdnLgaCntwkKcjuT1ai3uqrqcmvvRAa4/yTGPAI+cQa4mq7isgnfW5XFJv042j4wx1Vw3vDP//iaLt9bkcus53ZyO4/Vs8LSXeXd9HkdLK7h2uA1/NKa6pA7hDEtoy5xVO3G57MZqbay4e5k5KTtJah/GkC5tnI5ijNe5bnhnsg8U8/W2/U5H8XpW3L3Ixl2HWZ97mGuH241UY2oyvm9H2rQIZt7qnNp3buKsuHuRuat2Vt1IHRTrdBRjvFJIUNWN1Y/S93DgaKnTcbyaFXcvUVxWweK0PC7p34mIFsFOxzHGa00aGkd5pfL2ul1OR/FqVty9xPvf7uZoaYXNI2NMLZI6hDOkSxvmrtppT6yeghV3LzF/dQ5do1qSbDdSjanVxKFxbMsvIjX7oNNRvJYVdy+Que8oqdkHmZgcZzdSjfHApf07ERYSxLxVdmP1ZKy4e4EFqTkEBQhXDLYbqcZ4okWzIC4bGM37G6om2DMnsuLusLIKFwvX5DKuVweiwu2RamM8dc3QzpSUu1iy3uYlrIkVd4d9tnkvB4rKmDg0rvadjTH/1S82gj7RrZi3yiYTq4kVd4fNW51Dx1ahjOluc9obU1cTh8aRnnfE1litgRV3B+UdOsYX3+VzdXIsgQF2I9WYupowIIZmQQG8mWo3Vn/MiruDFq7JRRWuSrYuGWNOR0SLYMb36cg7aXmUlNvM4tVZcXeIy6W8uSaXUYntiGvbwuk4xvisiUPjOHysnA/T9zgdxatYcXdIyo4CdhYUc7VdtRtzRs7q2o64ts1ZYF0zP+DJMntxIvK5iGSISLqIzHC3/0FEdolImvvj4mrHzBSRTBHZIiIXNeQ34KveTM0hPDSIi/p0dDqKMT4tIEC4akgcX2ceIKeg2Ok4XsOTK/cK4F5V7QWMAKaLSG/3tqdVdaD7YymAe9skoA8wHnhWRAIbILvPOlJSztKNu5kwMJrQYDs1xpypK4fEIgJvrsl1OorXqLW4q+puVV3rfl0IZACnWtxzAjBPVUtVdQeQCQyrj7D+4r31uykpd1mXjDH1JKZ1c0YnRfFWag6VtkoTUMc+dxGJBwYBKe6mO0XkWxF5WUSOz3gVA1Tv/Mrl1L8MmpwFqTn07BhOv5gIp6MY4zcmJseRd7iEr7bmOx3FK3hc3EUkDFgI3K2qR4B/Ad2AgcBu4O/Hd63h8BN+lYrINBFJFZHU/Pym84/x3d5C0nIOcZVNEmZMvRrXuz1tWgTbjVU3j4q7iARTVdhnq+oiAFXdq6qVquoCXuD7rpdcoHp/QyxwwuQPqjpLVZNVNTkqquk8nblgdQ7BgcLlA6OdjmKMXwkJCuRng2L5eNNeW6UJz0bLCPASkKGqT1Vr71Rtt58BG92vlwCTRCRERBKAJGBV/UX2XWUVLt5et4vze3agXZhNEmZMfZtoqzT9V5AH+4wCJgMbRCTN3fYAcI2IDKSqyyULuBVAVdNFZAGwiaqRNtNV1R4dwyYJM6ah9egYzsC41sxfncNNZyc06a7PWou7qi6n5n70pac45hHgkTPI5ZcWpObSoVUIo5MinY5ijN+aODSOmYs2sC7nEIM7N92VzewJ1Uay53AJy7bs4+dDYgkKtNNuTEO5tH8nmgcHsmB1076xalWmkSxcm4tL4aoh1iVjTEMKDw3m0v6deHd9HkWlFU7HcYwV90agqryZmsPwhLbER7Z0Oo4xfm/i0DiKyip579umu0qTFfdGkLKjgKwDNkmYMY1lSJc2JLYPY04TXkDbinsjWJCaQ1hIEBf361T7zsaYMyYiXD+8M+tzDvFt7iGn4zjCinsDO1JSztINu/npgGiaN7NJwoxpLFcMiaV5cCBvrMx2OoojrLg3sHfW7aKk3MUkG9tuTKNqFRrM5YNiWJyWx+HicqfjNDor7g1IVXl9RTb9YiIYENfa6TjGNDmTR3ShtMLFm2uaXt+7FfcGtGpHAVv3HWXyiC5ORzGmSeod3YrkLm14Y2U2riY2FbAV9wb0RspOWoUG8dMBNkmYMU6ZfFYXsg4Uszxzv9NRGpUV9waSX1jKBxt3c+WQWLuRaoyDxvftSGRYM15vYjdWrbg3kAWpOZRXKtdbl4wxjgoJCmTi0Dg+zdhL1v4ip+M0GivuDaDSpcxJ2cnIbu3oFhXmdBxjmrwpI+MJDgzgX8u2OR2l0VhxbwDLtuxj16FjdiPVGC/RPjyUa4Z1ZuHaXHYdOuZ0nEZhxb0BvPjVDjq0CmFc7w5ORzHGuE0b0xUReP6LpnH1bsW9nq3JPsiK7Qe4ZXRXgm1qX2O8RnTr5vx8SCzzVuew70iJ03EanFWfevbPzzNp0yKYa4d3djqKMeZHbj8nkUqXMuvL7U5HaXCerKEaJyKfi0iGiKSLyIwfbf+1iKiIRFZrmykimSKyRUQuaojg3ig97zCfbd7H1FEJtGjmyQqGxpjG1LldCyYMjGZ2yk6/X0Tbkyv3CuBeVe0FjACmi0hvqCr8wAXAzuM7u7dNAvoA44FnRaRJDPR+9vNthIcEccPIeKejGGNO4o5zEympqPT7q/dai7uq7lbVte7XhUAGEOPe/DTwG6oWyT5uAjBPVUtVdQeQCQyr19ReKHPfUZZu3M0NI7sQ0TzY6TjGmJNIbB/GFYNieeWbLL8eOVOnPncRiQcGASkichmwS1XX/2i3GKD6LD25fP/LoPrXmiYiqSKSmp+fX7fUXujZZZmEBgUydVSC01GMMbX41YXdAXjqo+8cTtJwPC7uIhIGLATupqqr5kHg4Zp2raHthBl7VHWWqiaranJUVJSnMbzS9vyjLE7L45phnWkXFuJ0HGNMLWJaN+fGkfEsWpdLxu4jTsdpEB4VdxEJpqqwz1bVRUA3IAFYLyJZQCywVkQ6UnWlXn3y8ljArxcyfPKjLYQGBXDH2G5ORzHGeOiOc7sRHhLE3z7Y7HSUBuHJaBkBXgIyVPUpAFXdoKrtVTVeVeOpKuiDVXUPsASYJCIhIpIAJAGrGuw7cNi6nQdZumEPt4zpSqRdtRvjM1q3aMb0sYks25LPN9v8b8ZIT67cRwGTgfNEJM39cfHJdlbVdGABsAn4AJiuqpX1ktbLqCqP/WczkWHNuHl0V6fjGGPqaMrIeKIjQnl06WYq/Wy+d09GyyxXVVHV/qo60P2x9Ef7xKvq/mqfP6Kq3VS1h6r+pyGCe4Nl3+WTsqOAu85PIizExrUb42tCgwP5zfiebNh1mPmr/Wu1JntC9TRVupS//WczXdq1YNJQexrVGF81YWA0wxLa8viHmykoKnM6Tr2x4n6aFq3NZfOeQn59YQ+aBdlpNMZXiQh/ntCXwpIKnvjQf26uWlU6DYePlfO3DzYzMK41l/Tr5HQcY8wZ6tExnF+MjGfe6hzW7TzodJx6YcX9NDz98XccKCrjzxP6EhBQ07B+Y4yvmTEuiaiwEB5enO4XN1etuNdRxu4jvLYii2uHdaZfbITTcYwx9SQ8NJgHL+nFhl2HefWbLKfjnDEr7nWgqjy8eCMRzYO576IeTscxxtSzywZEc17P9jz+4WZ2+Ph6q1bc6+CdtF2szjrIb8b3pHWLZk7HMcbUMxHhrz/rR3BgAL95az0uH+6eseLuoYNFZTzy/mYGxEYwMTmu9gOMMT6pY0Qov/9pH1ZnHeQVH+6eseLuoT++m86h4jIevaK/3UQ1xs9dOTiG83q25wkf7p6x4u6Bj9L38E5aHneel0jv6FZOxzHGNLDq3TP3zE+jvNLldKQ6s+Jei0PFZTz4zkZ6dWrFHecmOh3HGNNIOkaE8ugV/UjLOcQTH25xOk6dWXGvxZ/e3cTBojKevKq/PYlqTBNzaf9orh/RmVlfbufTjL1Ox6kTq1an8MHG3Sxat4s7xibSJ9rGtBvTFD10SW96d2rFvW+uJ8+HluWz4n4SOQXF3PfWtwyIjeDOsdYdY0xTFRocyD+vG0x5hYtfzl1HWYVv9L9bca9BWYWLO+euA+CZawdbd4wxTVxCZEseu7I/a7IP8tA7G1D1/vHvNgl5DR7/YDPrcw7x3PWDiWvbwuk4xhgv8NMB0WzdW8g/PsskPrKl1w+w8GSZvTgR+VxEMkQkXURmuNv/LCLfuldm+khEoqsdM1NEMkVki4hc1JDfQH37ZNNeXly+gylndWF8X5vx0RjzvXsu6M5lA6J5/IMtLN2w2+k4p+RJf0MFcK+q9gJGANNFpDfwxPHVmYD3gIcB3NsmAX2A8cCzIhLYEOHr25Y9hdwzP42+Ma2YeXEvp+MYY7yMiPD4z/uT3KUN98xPY022904P7Mkye7tVda37dSGQAcSo6pFqu7UEjndCTQDmqWqpqu4AMoFh9Ru7/uUXljL136tp3iyQF25IJjTYJ34fGWMaWWhwIM9PHkKniFBufGUVaTmHnI5UozrdKRSReGAQkOL+/BERyQGuw33lDsQA1RcjzHW3ea2S8kqmvZ7KgaJSXpoylE4RzZ2OZIzxYu3CQph9ywjatGjG5JdSvLLAe1zcRSQMWAjcffyqXVUfVNU4YDZw5/Fdazj8hFvLIjJNRFJFJDU/P7/uyeuJy6X8+s31pOUc4n8mDrI52o0xHolp3Zy507y3wHtU3EUkmKrCPltVF9WwyxzgSvfrXKD6tImxQN6PD1DVWaqarKrJUVFRdUtdT1wuZeaiDbz37W5+O74n4/t2dCSHMcY3VS/w17+Y4lVPsXoyWkaAl4AMVX2qWntStd0uA46vLLsEmCQiISKSACQBq+ovcv1wuZQH3t7A/NQcfnleItPGdHU6kjHGB8W0bs78W0cQH9mCm19L5V/LtnnFOHhPxrmPAiYDG0Qkzd32AHCTiPQAXEA2cBuAqqaLyAJgE1UjbaaramV9Bz8TLpfy4DsbmLc6hzvHJvKrC7pT9TvMGGPqrlNEc968dST3vbWev32wmc17jvDYFf1p3sy5gRniDb9hkpOTNTU1tVH+rpLySn678FveSctj+thu/PrCHlbYjTH1QlV5dtk2nvhwC3Ftm/OXy/txTveG63YWkTWqmlzTtib1XP3eIyVMnLWSd9LyuO+iHlbYjTH1SkSYPjaR+dNGEBwYwJSXV3HX3HXsKyxp/CxN5cp9fc4hpr2eSmFJBU9PHMhFfezmqTGm4ZRWVPKvZdt49vNtiMCEgdFMGRlfrzPMnurK3e+Le0l5Jc98lsnzX26jQ6tQXpySTM+OtpqSMaZxbM8/yovLd7BobS4l5S6Su7Th7KRIBsa1ZmBca1q3aHbaX7vJFvdvMvfzwNsbyDpQzBWDY/jdJb1p0/L0T6Qxxpyuw8XlLEjNYeHaXLbsLeR46b1icAxPXT3wtL7mqYq7380KWelSPtu8j1e/yWJ55n7i27Vg9s3DGZUY6XQ0Y0wTFtEimFvGdOWWMV0pLClnQ+5h1uUcIqZ1wzwR7xfF/XBxOWm5h1iTfZC31+WSU3CMThGh/GZ8D6aOSrB5YowxXiU8NJiRiZGMbMCLTp8u7htyDzNj/jq25xcBIAJDu7Rl5k96cWHvDgQFNqnBQMYY818+Xdw7tAqha2QYVw6OZVBca/rFRhAeGux0LGOMcZxPF/f27tEvxhhjfsj6LYwxxg9ZcTfGGD9kxd0YY/yQFXdjjPFDVtyNMcYPWXE3xhg/ZMXdGGP8kBV3Y4zxQ14xK6SI5FO1VJ83igT2Ox3CQ5a1YfhKVl/JCZa1vnRR1RqXevKK4u7NRCT1ZFNqehvL2jB8Jauv5ATL2hisW8YYY/yQFXdjjPFDVtxrN8vpAHVgWRuGr2T1lZxgWRuc9bkbY4wfsit3Y4zxQ1bcjTHGD1lxPwkRmS8iae6PLBFJc7fHi8ixatueczgqIvIHEdlVLdPF1bbNFJFMEdkiIhc5mdOd5wkR2Swi34rI2yLS2t3ujed1vPu8ZYrIb53OU52IxInI5yKSISLpIjLD3X7SnwUnud9DG9yZUt1tbUXkYxHZ6v6zjcMZe1Q7b2kickRE7vbWc1ob63P3gIj8HTisqn8SkXjgPVXt63Cs/xKRPwBHVfXJH7X3BuYCw4Bo4BOgu6pWNnrI7zNdCHymqhUi8jcAVb3f286riAQC3wEXALnAauAaVd3kaDA3EekEdFLVtSISDqwBLgeupoafBaeJSBaQrKr7q7U9DhSo6mPuX55tVPV+pzJW5/733wUMB36BF57T2tiVey1ERKh6w8x1OstpmADMU9VSVd0BZFJV6B2jqh+paoX705VArJN5TmEYkKmq21W1DJhH1fn0Cqq6W1XXul8XAhlAjLOp6mwC8Kr79atU/XLyFucD21TVW5+cr5UV99qNBvaq6tZqbQkisk5EvhCR0U4F+5E73V0dL1f7720MkFNtn1y8qwBMBf5T7XNvOq/efu7+y/2/nkFAiruppp8FpynwkYisEZFp7rYOqrobqn5ZAe0dS3eiSfzwgs4bz+kpNeniLiKfiMjGGj6qX6Fdww//kXcDnVV1EPArYI6ItHI467+AbsBAd76/Hz+shi/V4P1wnpxXEXkQqABmu5scOa+n4Mi5qysRCQMWAner6hFO/rPgtFGqOhj4CTBdRMY4HehkRKQZcBnwprvJW8/pKQU5HcBJqjruVNtFJAi4AhhS7ZhSoNT9eo2IbAO6A6kNGLXWrMeJyAvAe+5Pc4G4aptjgbx6jnYCD87rFOBS4Hx13/Rx6ryegiPnri5EJJiqwj5bVRcBqOreatur/yw4SlXz3H/uE5G3qer22isinVR1t/sewj5HQ37vJ8Da4+fSW89pbZr0lbsHxgGbVTX3eIOIRLlvtiAiXYEkYLtD+Y5n6lTt058BG92vlwCTRCRERBKoyrqqsfNVJyLjgfuBy1S1uFq7t53X1UCSiCS4r+QmUXU+vYL7XtBLQIaqPlWt/WQ/C44RkZbum76ISEvgQqpyLQGmuHebAix2JuEJfvC/dW88p55o0lfuHvhxvxvAGOBPIlIBVAK3qWpBoyf7ocdFZCBV3QZZwK0AqpouIguATVR1gUx3cqSM2zNACPBxVX1iparehpedV/donjuBD4FA4GVVTXcqTw1GAZOBDeIepgs8AFxT08+CwzoAb7v/vYOAOar6gYisBhaIyE3ATuAqBzMCICItqBohVf281fj+8nY2FNIYY/yQdcsYY4wfsuJujDF+yIq7Mcb4ISvuxhjjh6y4G2OMH7LibowxfsiKuzHG+KH/B7mJVOOQYal3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.lat, model.timeave['Ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_temp_section(model, timeave=True):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    if timeave:\n",
    "        field = model.timeave['Tatm'].transpose()\n",
    "    else:\n",
    "        field = model.Tatm.transpose()\n",
    "    cax = ax.contourf(model.lat, model.lev, field)\n",
    "    ax.invert_yaxis()\n",
    "    ax.set_xlim(-90,90)\n",
    "    ax.set_xticks([-90, -60, -30, 0, 30, 60, 90])\n",
    "    fig.colorbar(cax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = climlab.RadiativeConvectiveModel(name='RCM', num_lev=30, num_lat=90)\n",
    "insolation = climlab.radiation.DailyInsolation(domains=model2.Ts.domain)\n",
    "model2.add_subprocess('insolation', insolation)\n",
    "model2.subprocess.SW.flux_from_space = insolation.insolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 0.9993368783782377 years.\n"
     ]
    }
   ],
   "source": [
    "model2.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 1.9986737567564754 years.\n"
     ]
    }
   ],
   "source": [
    "model2.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing out multi-dimensional Band Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 64, lev: 59, lon: 128, time: 12)\n",
       "Coordinates:\n",
       "  * lev        (lev) float64 0.2842 0.3253 0.3719 ... 849.5 959.0 1.004e+03\n",
       "  * lon        (lon) float64 0.0 2.812 5.625 8.438 ... 348.8 351.6 354.4 357.2\n",
       "  * lat        (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86\n",
       "  * time       (time) float64 4.382e+04 4.384e+04 ... 4.412e+04 4.415e+04\n",
       "Data variables:\n",
       "    P0         float64 1.004e+05\n",
       "    date       (time) int32 19900116 19900214 19900316 ... 19901115 19901216\n",
       "    datesec    (time) int32 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "    OZONE_old  (time, lat, lev, lon) float64 ...\n",
       "    OZONE      (time, lev, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:                     NCAR-CSM\n",
       "    Source:                          AMIP II (symmetric for APE project)\n",
       "    Written_By:                      olson\n",
       "    Date_Written:                    August 22 2003\n",
       "    Host:                            zen\n",
       "    Command:                         ncgen\n",
       "    history:                         Wed Jul 30 08:35:58 2008: ncrename -v OZ...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</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-5d41aa6a-a496-4c2a-9722-f861e162ff91' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d41aa6a-a496-4c2a-9722-f861e162ff91' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 64</li><li><span class='xr-has-index'>lev</span>: 59</li><li><span class='xr-has-index'>lon</span>: 128</li><li><span class='xr-has-index'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2c12635f-4df1-4a4c-b18d-8c6078917365' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c12635f-4df1-4a4c-b18d-8c6078917365' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2842 0.3253 ... 959.0 1.004e+03</div><input id='attrs-0dd9d030-600b-4ad0-b20a-dceb2fe0fd47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dd9d030-600b-4ad0-b20a-dceb2fe0fd47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d832fa3-1538-4aba-9961-24b2731d80bb' class='xr-var-data-in' type='checkbox'><label for='data-9d832fa3-1538-4aba-9961-24b2731d80bb' 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>hybrid level at layer midpoints (1000*(A+B))</dd><dt><span>units :</span></dt><dd>hybrid_sigma_pressure</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([2.84191e-01, 3.25350e-01, 3.71936e-01, 4.24615e-01, 4.84153e-01,\n",
       "       5.51432e-01, 6.27490e-01, 7.13542e-01, 8.11039e-01, 9.21699e-01,\n",
       "       1.04757e+00, 1.19108e+00, 1.35511e+00, 1.54305e+00, 1.75891e+00,\n",
       "       2.00739e+00, 2.29403e+00, 2.62529e+00, 3.00884e+00, 3.45369e+00,\n",
       "       3.97050e+00, 4.57190e+00, 5.27278e+00, 6.09073e+00, 7.04636e+00,\n",
       "       8.16371e+00, 9.47075e+00, 1.10000e+01, 1.27894e+01, 1.48832e+01,\n",
       "       1.73342e+01, 2.02039e+01, 2.35659e+01, 2.75066e+01, 3.21283e+01,\n",
       "       3.75509e+01, 4.39150e+01, 5.13840e+01, 6.01483e+01, 7.04289e+01,\n",
       "       8.24830e+01, 9.66096e+01, 1.13155e+02, 1.32514e+02, 1.55122e+02,\n",
       "       1.81445e+02, 2.11947e+02, 2.47079e+02, 2.87273e+02, 3.32956e+02,\n",
       "       3.84573e+02, 4.42610e+02, 5.07601e+02, 5.80132e+02, 6.60837e+02,\n",
       "       7.50393e+02, 8.49521e+02, 9.58981e+02, 1.00369e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.812 5.625 ... 354.4 357.2</div><input id='attrs-75a4d22d-e8a8-4612-8b56-9f6191550ba5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75a4d22d-e8a8-4612-8b56-9f6191550ba5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d3d2a3e-3005-434b-b205-343b77bb3591' class='xr-var-data-in' type='checkbox'><label for='data-8d3d2a3e-3005-434b-b205-343b77bb3591' 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>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,\n",
       "        19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,\n",
       "        39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,\n",
       "        59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,\n",
       "        78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,\n",
       "        98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,\n",
       "       118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,\n",
       "       137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,\n",
       "       157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,\n",
       "       177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,\n",
       "       196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,\n",
       "       216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,\n",
       "       236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,\n",
       "       255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,\n",
       "       275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,\n",
       "       295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,\n",
       "       315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,\n",
       "       334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,\n",
       "       354.375 , 357.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.86 -85.1 -82.31 ... 85.1 87.86</div><input id='attrs-80522638-7dd1-4f6a-8419-93ec1fc40444' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80522638-7dd1-4f6a-8419-93ec1fc40444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2375a127-4cb4-4246-bd49-caa37584bb88' class='xr-var-data-in' type='checkbox'><label for='data-2375a127-4cb4-4246-bd49-caa37584bb88' 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>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-87.8638, -85.0965, -82.3129, -79.5256, -76.7369, -73.9475, -71.1577,\n",
       "       -68.3678, -65.5776, -62.7873, -59.997 , -57.2066, -54.4162, -51.6257,\n",
       "       -48.8352, -46.0447, -43.2542, -40.4636, -37.6731, -34.8825, -32.0919,\n",
       "       -29.3014, -26.5108, -23.7202, -20.9296, -18.139 , -15.3484, -12.5578,\n",
       "        -9.7671,  -6.9765,  -4.1859,  -1.3953,   1.3953,   4.1859,   6.9765,\n",
       "         9.7671,  12.5578,  15.3484,  18.139 ,  20.9296,  23.7202,  26.5108,\n",
       "        29.3014,  32.0919,  34.8825,  37.6731,  40.4636,  43.2542,  46.0447,\n",
       "        48.8352,  51.6257,  54.4162,  57.2066,  59.997 ,  62.7873,  65.5776,\n",
       "        68.3678,  71.1577,  73.9475,  76.7369,  79.5256,  82.3129,  85.0965,\n",
       "        87.8638])</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'>float64</div><div class='xr-var-preview xr-preview'>4.382e+04 4.384e+04 ... 4.415e+04</div><input id='attrs-abce7343-e54a-43da-947e-ddec329de591' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abce7343-e54a-43da-947e-ddec329de591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15fd314a-4127-449b-9512-a65bdb869761' class='xr-var-data-in' type='checkbox'><label for='data-15fd314a-4127-449b-9512-a65bdb869761' 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>time</dd><dt><span>units :</span></dt><dd>day number (Jan 1 = 1)</dd><dt><span>calendar :</span></dt><dd>365_days</dd></dl></div><div class='xr-var-data'><pre>array([43816., 43844., 43875., 43905., 43936., 43966., 43997., 44028., 44058.,\n",
       "       44089., 44119., 44150.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0b2a1cf-eaf2-42b5-9484-5187ae0806a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e0b2a1cf-eaf2-42b5-9484-5187ae0806a2' class='xr-section-summary' >Data variables: <span>(5)</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>P0</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-5b781280-065e-4f0f-a0d3-deae362bfb22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b781280-065e-4f0f-a0d3-deae362bfb22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e361c42-abee-4b66-b9cd-64d38700c63a' class='xr-var-data-in' type='checkbox'><label for='data-2e361c42-abee-4b66-b9cd-64d38700c63a' 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>reference pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array(100369.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ed9410a-b5be-4a85-bebb-653b2b4d5ea1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ed9410a-b5be-4a85-bebb-653b2b4d5ea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea1d82bf-572e-4523-94bd-3e04d1bdb5a6' class='xr-var-data-in' type='checkbox'><label for='data-ea1d82bf-572e-4523-94bd-3e04d1bdb5a6' 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>current date as 8 digit integer (YYYYMMDD)</dd></dl></div><div class='xr-var-data'><pre>array([19900116, 19900214, 19900316, 19900415, 19900516, 19900615, 19900716,\n",
       "       19900816, 19900915, 19901016, 19901115, 19901216], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>datesec</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-23fdb203-255e-42ac-b9e3-f7dd1e4391f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23fdb203-255e-42ac-b9e3-f7dd1e4391f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf1bd428-41d7-43d1-8a35-916fb5643957' class='xr-var-data-in' type='checkbox'><label for='data-cf1bd428-41d7-43d1-8a35-916fb5643957' 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>seconds of current date</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OZONE_old</span></div><div class='xr-var-dims'>(time, lat, lev, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36a389d2-6b0a-47bc-9938-1f0d3f79c9b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36a389d2-6b0a-47bc-9938-1f0d3f79c9b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d832567-10aa-427c-8d19-698f7b764da5' class='xr-var-data-in' type='checkbox'><label for='data-8d832567-10aa-427c-8d19-698f7b764da5' 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>OZONE</dd><dt><span>units :</span></dt><dd>FRACTION</dd></dl></div><div class='xr-var-data'><pre>[5799936 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OZONE</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbac33f6-5376-4bf2-8112-47319406dcb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbac33f6-5376-4bf2-8112-47319406dcb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2590e99-fa09-4eca-9a12-0c3a464d561b' class='xr-var-data-in' type='checkbox'><label for='data-b2590e99-fa09-4eca-9a12-0c3a464d561b' 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>OZONE</dd><dt><span>units :</span></dt><dd>FRACTION</dd></dl></div><div class='xr-var-data'><pre>[5799936 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af949a84-2e72-401c-be3d-735e3a30a2f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af949a84-2e72-401c-be3d-735e3a30a2f7' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>NCAR-CSM</dd><dt><span>Source :</span></dt><dd>AMIP II (symmetric for APE project)</dd><dt><span>Written_By :</span></dt><dd>olson</dd><dt><span>Date_Written :</span></dt><dd>August 22 2003</dd><dt><span>Host :</span></dt><dd>zen</dd><dt><span>Command :</span></dt><dd>ncgen</dd><dt><span>history :</span></dt><dd>Wed Jul 30 08:35:58 2008: ncrename -v OZONE,OZONE_old apeozone_cam3_5_54.nc\n",
       " </dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 64, lev: 59, lon: 128, time: 12)\n",
       "Coordinates:\n",
       "  * lev        (lev) float64 0.2842 0.3253 0.3719 ... 849.5 959.0 1.004e+03\n",
       "  * lon        (lon) float64 0.0 2.812 5.625 8.438 ... 348.8 351.6 354.4 357.2\n",
       "  * lat        (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86\n",
       "  * time       (time) float64 4.382e+04 4.384e+04 ... 4.412e+04 4.415e+04\n",
       "Data variables:\n",
       "    P0         float64 ...\n",
       "    date       (time) int32 ...\n",
       "    datesec    (time) int32 ...\n",
       "    OZONE_old  (time, lat, lev, lon) float64 ...\n",
       "    OZONE      (time, lev, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:                     NCAR-CSM\n",
       "    Source:                          AMIP II (symmetric for APE project)\n",
       "    Written_By:                      olson\n",
       "    Date_Written:                    August 22 2003\n",
       "    Host:                            zen\n",
       "    Command:                         ncgen\n",
       "    history:                         Wed Jul 30 08:35:58 2008: ncrename -v OZ...\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Put in some ozone\n",
    "import xarray as xr\n",
    "\n",
    "ozonepath = \"http://thredds.atmos.albany.edu:8080/thredds/dodsC/CLIMLAB/ozone/apeozone_cam3_5_54.nc\"\n",
    "ozone = xr.open_dataset(ozonepath)\n",
    "ozone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Dimensions of the ozone file\n",
    "lat = ozone.lat\n",
    "lon = ozone.lon\n",
    "lev = ozone.lev\n",
    "\n",
    "# Taking annual, zonal average of the ozone data\n",
    "O3_zon = ozone.OZONE.mean(dim=(\"time\",\"lon\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (64, 1) \n",
      "  Tatm: (64, 59) \n",
      "The subprocess tree: \n",
      "Untitled: <class 'climlab.model.column.BandRCModel'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  make a model on the same grid as the ozone\n",
    "model3 = climlab.BandRCModel(model='Band RCM', lev=lev, lat=lat)\n",
    "insolation = climlab.radiation.DailyInsolation(domains=model3.Ts.domain)\n",
    "model3.add_subprocess('insolation', insolation)\n",
    "model3.subprocess.SW.flux_from_space = insolation.insolation\n",
    "print(model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put in the ozone\n",
    "model3.absorber_vmr['O3'] = O3_zon.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 59)\n",
      "(64, 59)\n"
     ]
    }
   ],
   "source": [
    "print(model3.absorber_vmr['O3'].shape)\n",
    "print(model3.Tatm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3.step_forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1.0 years.\n",
      "Total elapsed time is 1.0020747876340685 years.\n"
     ]
    }
   ],
   "source": [
    "model3.integrate_years(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1.0 years.\n",
      "Total elapsed time is 2.0014116660123062 years.\n"
     ]
    }
   ],
   "source": [
    "model3.integrate_years(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is now working. Will need to do some model tuning.\n",
    "\n",
    "And start to add dynamics!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding meridional diffusion!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.RadiativeConvectiveModel'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (90, 1) \n",
      "  Tatm: (90, 30) \n",
      "The subprocess tree: \n",
      "RCM: <class 'climlab.model.column.RadiativeConvectiveModel'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffmodel = climlab.process_like(model2)\n",
    "diffmodel.name = \"RCM with heat transport\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5946637.413346613\n"
     ]
    }
   ],
   "source": [
    "# thermal diffusivity in W/m**2/degC\n",
    "D = 0.05\n",
    "# meridional diffusivity in m**2/s\n",
    "K = D / diffmodel.Tatm.domain.heat_capacity[0] * const.a**2\n",
    "print(K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = climlab.dynamics.MeridionalDiffusion(K=K, state={'Tatm': diffmodel.Tatm}, **diffmodel.param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffmodel.add_subprocess('diffusion', d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.RadiativeConvectiveModel'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (90, 1) \n",
      "  Tatm: (90, 30) \n",
      "The subprocess tree: \n",
      "RCM with heat transport: <class 'climlab.model.column.RadiativeConvectiveModel'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   diffusion: <class 'climlab.dynamics.meridional_advection_diffusion.MeridionalDiffusion'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(diffmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffmodel.step_forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 3.000748544390544 years.\n"
     ]
    }
   ],
   "source": [
    "diffmodel.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 4.000085422768781 years.\n"
     ]
    }
   ],
   "source": [
    "diffmodel.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model2)\n",
    "plot_temp_section(diffmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works as long as K is a constant.\n",
    "\n",
    "The diffusion operation is broadcast over all vertical levels without any special code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inferred_heat_transport( energy_in, lat_deg ):\n",
    "    '''Returns the inferred heat transport (in PW) by integrating the net energy imbalance from pole to pole.'''\n",
    "    from scipy import integrate\n",
    "    from climlab import constants as const\n",
    "    lat_rad = np.deg2rad( lat_deg )\n",
    "    return ( 1E-15 * 2 * np.math.pi * const.a**2 * integrate.cumtrapz( np.cos(lat_rad)*energy_in,\n",
    "            x=lat_rad, initial=0. ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot the northward heat transport in this model\n",
    "Rtoa = np.squeeze(diffmodel.timeave['ASR'] - diffmodel.timeave['OLR'])\n",
    "plt.plot(diffmodel.lat, inferred_heat_transport(Rtoa, diffmodel.lat))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Band model with diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffband = climlab.process_like(model3)\n",
    "diffband.name = \"Band RCM with heat transport\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (64, 1) \n",
      "  Tatm: (64, 59) \n",
      "The subprocess tree: \n",
      "Band RCM with heat transport: <class 'climlab.model.column.BandRCModel'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   diffusion: <class 'climlab.dynamics.meridional_advection_diffusion.MeridionalDiffusion'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "d = climlab.dynamics.MeridionalDiffusion(K=K, state={'Tatm': diffband.Tatm}, **diffband.param)\n",
    "diffband.add_subprocess('diffusion', d)\n",
    "print(diffband)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 3.000748544390544 years.\n"
     ]
    }
   ],
   "source": [
    "diffband.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 4.000085422768781 years.\n"
     ]
    }
   ],
   "source": [
    "diffband.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model3)\n",
    "plot_temp_section(diffband)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16d30c550>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(diffband.lat, diffband.timeave['ASR'] - diffband.timeave['OLR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16d36ad00>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot the northward heat transport in this model\n",
    "Rtoa = np.squeeze(diffband.timeave['ASR'] - diffband.timeave['OLR'])\n",
    "plt.plot(diffband.lat, inferred_heat_transport(Rtoa, diffband.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}