{
 "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": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import climlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 30) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <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(num_lev=30, num_lat=90)\n",
    "print model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-89. -87. -85. -83. -81. -79. -77. -75. -73. -71. -69. -67. -65. -63. -61.\n",
      " -59. -57. -55. -53. -51. -49. -47. -45. -43. -41. -39. -37. -35. -33. -31.\n",
      " -29. -27. -25. -23. -21. -19. -17. -15. -13. -11.  -9.  -7.  -5.  -3.  -1.\n",
      "   1.   3.   5.   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.  55.  57.  59.\n",
      "  61.  63.  65.  67.  69.  71.  73.  75.  77.  79.  81.  83.  85.  87.  89.]\n"
     ]
    }
   ],
   "source": [
    "print model.lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "insolation = climlab.radiation.DailyInsolation(domains=model.Ts.domain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.add_subprocess('insolation', insolation)\n",
    "model.subprocess.SW.flux_from_space = insolation.insolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 30) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <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": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.compute_diagnostics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115f4a5d0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ksJg7XltCMNwZL5So6EUkJFzQqy0TLujOG4s38fi7OnO2LiL9DiAiUls3ndeF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\nRCTMqehFRMLc/wOKWrNqwsn7dQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f98310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.lat, model.SW_down_TOA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/br546577/code/climlab/climlab/model/column.py:141: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  self.subprocess['SW'].flux_from_space)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total elapsed time is 0.999336878378 years.\n"
     ]
    }
   ],
   "source": [
    "model.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116570f10>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LM9sA3A/cFSyWBWyos9rGYExEjsGArmlM+UYe63eUcsOf8ymv0g3HpWEaXPRm1g54AbjN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\n+uarxRNqbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116498110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.lat, model.Ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 1.99867375676 years.\n"
     ]
    }
   ],
   "source": [
    "model.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1166d0610>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AXs+wi7InY4XeQQtX56MKl6VZt40xpyKiTTCT+nVlceYuyqts2YsTsULvEJdL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1165c3c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.lat, model.timeave['Ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z3Y3iy7Mp5Y9OXvcLjTl7Xh2MYkVyb9qaxmGdeY/amz/WJHX6Eq26Tm37yzaocwnoOi2w\ndQoCM+tfVqWPXMLZrK7qejEB6y6bHnXTQ7rs+rR70/Xgz8mmIpugNjOziTmozUasLr3qOu2faToH\ndRd1WVDrsuLbaDTxeOq2c1CPgFccM5sOB3XNuTddT/7cbBAOajOzaZB0jKQ7JD0oab2kS9P4/y5p\no6QfSfpbSa9M4+dL+kXH9RWv6TUPB/Uk6lKftvoqs1c9rHKb14O+7AE+EhEnAmcAl0g6EVgFnBwR\n/xL4B+CKjsdsiYhT0t/FvWbgoC5ZmfVpbz6bVS8itkXE2jS8E9gAzImIb0fEnjTZ3RTXj50SB7WZ\nWXezJK3u+LtosgklzQdOBe4Zd9f7gG923F6Qyh7flfTGXg3I6ifk1j/3ppvBl+qqhnZrkJ/aPx0R\ni3s+p3QIsBK4LCKe6xj/hxTlkevTqG3AvIh4RtJpwFckndT5mPEc1JaNc169sec0q544YQQtaYZh\nnfdj57Hi0EdjCC1qLkkHUoT09RFxS8f43wbeCZwdEQEQEbuAXWl4jaQtwCJg9WTP76CewLB2oPj4\n6e76CeZej2lCcLtXXW+SBFwLbIiIz3SMPw/4KPCmiPinjvGzgWcjYq+k44CFwMPd5uGgrqG6lz2m\nEtD9PFcTQttq6UzgvcD9ktalcVcCfwHMBFYVWc7d6QiPs4BPSHoBeBG4OCKe7TYDB7WNzDADutvz\nO7BtlCLi+8BEm+HfmGT6lRRlkr45qK10ZQf0ZPOrU2C7/GHd+PC8kpRVn65T2eOcV28ceUiPn3/b\n+YcvzeCgHscL5HDkEpJVf1mYDYOD2oYux2DMsU3j1WlryUbLQV0jua/Iufdec26bWTcOahuKuoRg\nXdo5TK5T15+DuoN/6DI1dQu/nNub+1aTVcNBXRO5rsA5h143dW23tZOD2qas7mFX9/ZbeziobUqa\nEnI5vo4ytp7aVo5rGgf1kJWxQuRW9sgx3Kajaa+nTN6hWA0HdeIFsD9NDbWmvi5rBge1WZJTWOe2\nFWXV6iuoJb1S0s3pirobJP26pCMlrZK0Kf0/omP6KyRtlvSQpHPLa37z5bTC5hRkZm3Sb4/6c8Bt\nEXEC8DqKizdeDtweEQuB29Nt0tV3lwInAecBn5c0Y9gNt9FqS0g3+XX6hy/11TOoJR1OcaLrawEi\nYndE/BxYAqxIk60Azk/DS4AbI2JXRDwCbAZOH3bDh8k/dOmuyeE1kVxeb05bU1atfnrUC4DtwJck\n/VDSFyW9AjgqIralaZ4AjkrDc4DHOh6/NY2zAXlFrU4uYW0G/QX1AcDrgS9ExKnAP5LKHGPSRRsH\nuvqlpIvGLr++fbsDKVcOLLPq9RPUW4GtEXFPun0zRXA/KelogPT/qXT/48AxHY+fm8btJyKWR8Ti\niFg8e7avbJGjtod0219/N65T7yPpGEl3SHpQ0npJl6bx70m3X5S0eNxjBjrgomdQR8QTwGOSXptG\nnQ08CNwKLEvjlgFfTcO3AkslzZS0gOIKu/f28XrNslN1WA+7/NXU/SgV2wN8JCJOBM4ALkkHVTwA\nvAu4s3PiqRxw0e81Ez8IXC/pIIrLmv8ORcjfJOn9wKPABQARsV7STRRhvge4JCL29jmfkct1R2LV\n9emqA8qsLtK+um1peKekDcCciFgFkK5A3umXB1wAj0gaO+Dirsnm0VdQR8Q6YPEEd509yfRXAVf1\n89yWH4f0/s559cZaXSjXhm6WpNUdt5dHxPKJJpQ0HzgVuGei+5M5wN0dt3secOGrkJv1ocqwzvUK\n5TuPFYc+OtAxBNmYsZtB2v50REzUUd2PpEOAlcBlEfHcdNo3nn9CnqEqyx7uTZsNTtKBFCF9fUTc\n0mPyvg646NTqoPae6/05pLtryvvjHYrDpaIIfS2wISI+08dDBj7gwqWPIfCCb9ZqZwLvBe6XtC6N\nuxKYCfwPYDbwdUnrIuLcqRxw4aA2oDm9xbJVVat2nTpfEfF9YLLN87+d5DEDHXDR6tJHjqo+LM96\n85eajVprg9r16X0cPGZ5a21Qm01H3b/cvF+lXhzU0zTMBb6KskfdA6dNci2Leeu0fA5qsynyl5yN\nSiuD2j2AgoPGrB5aGdRmwzLqLzuXP9rJQZ2JUa+A7k2b1YeDehq859ygvl96Xn7ro3VB7U20+gaL\nWVu1LqjNyjDKLz/XqdvHQZ2BUa547k2b1Y+D2mxI/CVoZWlVULd908xBYuN5h2I9tCqoh8kLuFXJ\ndep2cVBXLNcVzqbGWy1WhtYEddu/6R0gZvXVmqA2G5W2fym2vVNUBgd1C7Q9OMzKJOk6SU9JeqBj\n3Osk3SXpfkl/J+mwNH6+pF9IWpf+rulnHg5qsxKM4stxWPs3vGN82r4MnDdu3BeByyPiX1BcN/EP\nOu7bEhGnpL+L+5lBK4J62Jtiw1qwR7Ej0b1ps3JFxJ3As+NGLwLuTMOrgN+czjxaEdRmNloNq1PP\nkrS64++iPh6zHliSht8DHNNx34JU9viupDf204ADBmywmfXpnFdvZNUTJ1TdDJvAjOeDw7fs6nfy\npyNi8YCzeB/wF5L+C3ArMLYZvg2YFxHPSDoN+IqkkyLiuW5P5h51g7ns0Xw+Dj9PEbExIt4eEacB\nNwBb0vhdEfFMGl6Txi/q9XyND+pcN8G8glnT5brujYKkV6X/LwM+BlyTbs+WNCMNHwcsBB7u9XyN\nD+q2cm86D3X5HHzkx9RJugG4C3itpK2S3g9cKOkfgI3AT4EvpcnPAn4kaR1wM3BxRIzfEfkSrlEP\nyAu0mXWKiAsnuetzE0y7Elg56Dwa3aNu86aX5aMuveoyeB0cjkYHdVu1ORjayPs7ms9BXQGvWGY2\nCAe12Qh4K8emo6+glvRhSeslPSDpBkkHSzpS0ipJm9L/Izqmv0LSZkkPSTq3vOZPrq21MQeCTVVZ\nO8rbui4OU8+gljQH+BCwOCJOBmYAS4HLgdsjYiFwe7qNpBPT/SdRnKjk82PHDdadj/iwXLmc1mz9\nlj4OAH5F0gHAyymOC1wCrEj3rwDOT8NLgBvTL3AeATYDpw+vyfXmFaq9vLVjU9UzqCPiceBTwE8o\nfqe+IyK+DRwVEdvSZE8AR6XhOcBjHU+xNY0bmbZuajkILFdtXSeHpZ/SxxEUveQFwGuAV0j6rc5p\nIiKAGGTGki4aOxvV9u3uZZqZTaaf0sfbgEciYntEvADcArwBeFLS0QDp/1Np+sfZ/5R+c9O4/UTE\n8ohYHBGLZ8+ePZ3XYFYb3uqxqegnqH8CnCHp5ZIEnA1soDh137I0zTLgq2n4VmCppJmSFlCcdOTe\n4TZ7cm3dxHIA2DD2f5S5w7yt6+Yw9DzXR0TcI+lmYC2wB/ghsBw4BLgpnYDkUeCCNP16STcBD6bp\nL4mIvSW1f2SGsQB7R6KZTUVfJ2WKiI8DHx83ehdF73qi6a8Crppe08yayRcUsEE16peJbd20ctnD\n6qKt6+h0NSqozcyayEFtVoGytoLqsB/EverBNSaoc//wy1qBXPawYfOpEvLTmKAukxdcM6uSL8Vl\nWXj3YWv7nvbm515fYktGp81Hf+w8Vhz66EA/Zs6WpOuAdwJPpRPXIekUigvaHkxxmPLvRcS96b4r\ngPcDe4EPRcS3es2jEUGde9mjLHUvewwSzpM9rimhbbX2ZeAvgb/qGPdJ4I8j4puS3pFuv3nc2UVf\nA3xH0qJevzVx6cNG7t2HrZ1ySJf5XE1Rhx2K0JwOVkTcCYy/kngAh6XhwynOOApTPLtoI3rUVg9l\nBurYc7uHbZm4DPiWpE9RdIjfkMbPAe7umK6vs4vWPqjr8K1clx5OmUbV661bYLe5Tl0lPb+bgzZu\n7XfyWZJWd9xeHhHLezzmA8CHI2KlpAuAaylOcDclLn3UVJ3q01WUJlwOmZ5RHelUh44W8PTYmT7T\nX6+QhuJEdbek4b9hX3mjr7OLjueg7sGH5k1d1fVjh7VV6KfAm9LwW4FNaXhKZxetdVDX5Nu4lXIJ\nyVza0U2dto7KUPf1WNINwF3AayVtTWcU/V3g05LuA/4UuAiKs4sCY2cXvY0+zy5a+xp1G+W+YucW\nju8+bG1tatbDMn/udn681RfkGIWIuHCSu06bZPqBzy5a2x51Xb6F27YjMbeQHpNru6xQl/W5KrUN\nastP7mGYe/vMJuOgtqGoSwjm2s4cy1mj3pHuXvXkahnUbf5Ac1yhcw2/ydStvWa1DOpR8aF5zdWG\nsK7j/pE2d8K6qV1Q1+mDrOOKMqg6B15ubc9xa6kKdVrHR6V2QW35yC3ozJqqVkHd9m/anHpcTQnp\npryOpmn7uj5erYLarAwO6zw5rPepTVD7Q8uHg608OW01QfU71L3eF2oT1G2Xywrc1JBu6utqwg5t\nh3VNgrqKD2q6PYkmrCBt09SwboKdx6rVgZ19ULf5w8mNg8ysGtkHteWhLSGdw+vMpcxl+cg6qN2b\nLnjFHa0cwtqsU7ZB7ZDOh4Or3ao+8sMyDWqHtFWtSV9O3rFdf1kFdVP27DZpxWhSYNWJy13WKZug\nzimgvaln4C8py0c2QW0Tq7Jn5aAy603SdZKekvTAuPEflLRR0npJn0zj5kv6haR16e+afubhi9ua\nddHGC+PawL4M/CXwV2MjJL0FWAK8LiJ2SXpVx/RbIuKUQWbgHrVNyL1ps/5ExJ3As+NGfwC4OiJ2\npWmems48FBHTefxQSNoJPFR1OwY0C3i66kYMqG5trlt7wW0ehddGxKHTeQJJt1G87n4cDDzfcXt5\nRCwf93zzga9FxMnp9jrgq8B56bH/OSJ+kKZbD2wCdgAfi4jv9WpALqWPhyJicdWNGISk1W5zuerW\nXnCbR0HS6uk+R0ScN4y2dHEAcCRwBvBrwE2SjgO2AfMi4hlJpwFfkXRSRDzX7clc+jAzG76twC1R\nuBd4EZgVEbsi4hmAiFgDbAEW9XoyB7WZ2fB9BXgLgKRFwEHA05JmS5qRxh8HLAQe7vVkuZQ+lvee\nJDtuc/nq1l5wm0chq/ZKugF4MzBL0lbg48B1wHXpkL3dwLKICElnAZ+Q9AJFL/viiBi/I/Kl88hh\nZ6KZmU3OpQ8zs8w5qM3MMldpUEt6naS7JN0v6e8kHdZx3xWSNkt6SNK5VbZzvIl+GprGZ9dmSX8i\n6Ufp56rflvSajvuya+8YSeeldm2WdHnV7RlP0sGS7pV0X1oO/jiNP1LSKkmb0v8jqm5rJ0mvlHRz\nWn43SPr1nNss6VJJD6T3+LI0Ltv2liYiKvsDfgC8KQ2/D/iTNHwicB8wE1hAcQjLjCrb2tHmtwDf\nAWam26/Kuc3AYR3DHwKuybm9qW0zUnuOo9hbfh9wYtXtGtdGAYek4QOBeyiOmf0kcHkafznwZ1W3\ndVy7VwD/IQ0fBLwy1zYDJwMPAC+nOPDhO8A/y7W9Zf5VXfpYBNyZhlcBv5mGlwA3RnHM4SPAZuD0\nCto3kcl+Gpplm2P/A+lfAYztPc6yvcnpwOaIeDgidgM3UrQ3G1H4f+nmgekvKNq5Io1fAZxfQfMm\nJOlw4CzgWoCI2B0RPyffNv9z4J6I+KeI2AN8F3gX+ba3NFUH9Xr2rYDvAY5Jw3OAxzqm25rG5WAR\n8EZJ90j6rqRfS+OzbbOkqyQ9Bvw74I/S6GzbS95t+yVJM9JPhZ8CVkXEPcBREbEtTfIEcFRlDXyp\nBcB24EuSfijpi5JeQb5tfoBiXftVSS8H3kGREbm2tzSlB7Wk76Qa0/i/JRTljt+TtAY4lOJ4w8r1\naHPnT0P/gOKnoZWeTLtHe4mIP4yIY4Drgd+vsq1NEhF7ozgL2lzgdEknj7s/2LcFk4MDgNcDX4iI\nU4F/pCgd/FJObY6IDcCfAd8GbgPWAXvHTZNNe8tU+g9eIuJtPSZ5O/zy1zu/kcY9zr7eNRQrwuPD\nb93EurVZ0gdIPw0F7pX0IsXJXSprcx/v8ZjrgW9QHJBf6XvcQ85te4mI+LmkOyhOwPOkpKMjYpuk\noyl627nYCmxNPX+AmymCOts2R8S1pFKNpD+leA3ZtrcsVR/18ar0/2XAx4Cxk2jfCiyVNFPSAoqf\nWd5bTStfYsKfhpJpmyUt7Li5BBi7EkGW7U1+ACyUtEDSQcBSivZmQ8VPgV+Zhn8FOIfivb0VWJYm\nW0ZxBrVX0exvAAAAz0lEQVQsRMQTwGOSXptGnQ08SMZt7siIeRT16b8m4/aWpeqfkF8o6ZI0fAvw\nJYCIWC/pJoqFaA9wSUTsneQ5Rm3Cn4YCubb56rRivgg8ClwMeb/HEbFH0u8D36I4AuS6iFhfcbPG\nOxpYoeK8DS8DboqIr0m6i6Ic9n6K9/uCKhs5gQ8C16cvwIeB3yG1P9M2r5T0q8ALFMvozyVdTb7t\nLYV/Qm5mlrmqj/owM7MeHNRmZplzUJuZZc5BbWaWOQe1mVnmHNRmZplzUJuZZe7/A+Nu1iwCPDF7\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134e5750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model2 = climlab.RadiativeConvectiveModel(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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 0.999336878378 years.\n"
     ]
    }
   ],
   "source": [
    "model2.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 1.99867375676 years.\n"
     ]
    }
   ],
   "source": [
    "model2.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rMMqJVRoj1DFQZ48nRhEciarY6VSea4GvSNpGMvHK4pA+qolV/GFiRYnJE6ua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\njuNEjgu14zhO5Px/icB32cuPCqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118f3b7d0>"
      ]
     },
     "metadata": {},
     "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": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Put in some ozone\n",
    "import netCDF4 as nc\n",
    "\n",
    "datapath = \"http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/BrianRose/CESM_runs/\"\n",
    "endstr = \"/entry.das\"\n",
    "\n",
    "ozone = nc.Dataset( datapath + 'som_input/ozone_1.9x2.5_L26_2000clim_c091112.nc' + endstr )\n",
    "\n",
    "#  Dimensions of the ozone file\n",
    "lat = ozone.variables['lat'][:]\n",
    "lon = ozone.variables['lon'][:]\n",
    "lev = ozone.variables['lev'][:]\n",
    "\n",
    "# Taking annual, zonal average of the ozone data\n",
    "O3_zon = np.mean( ozone.variables['O3'],axis=(0,3) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (96, 26) \n",
      "  Ts: (96, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  make a model on the same grid as the ozone\n",
    "model3 = climlab.BandRCModel(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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96, 26)\n",
      "[   3.544638     7.3888135   13.967214    23.944625    37.23029     53.114605\n",
      "   70.05915     85.439115   100.514695   118.250335   139.115395   163.66207\n",
      "  192.539935   226.513265   266.481155   313.501265   368.81798    433.895225\n",
      "  510.455255   600.5242     696.79629    787.70206    867.16076    929.648875\n",
      "  970.55483    992.5561   ]\n",
      "[   3.544638     7.3888135   13.967214    23.944625    37.23029     53.114605\n",
      "   70.05915     85.439115   100.514695   118.250335   139.115395   163.66207\n",
      "  192.539935   226.513265   266.481155   313.501265   368.81798    433.895225\n",
      "  510.455255   600.5242     696.79629    787.70206    867.16076    929.648875\n",
      "  970.55483    992.5561   ]\n"
     ]
    }
   ],
   "source": [
    "#  Set the ozone mixing ratio\n",
    "O3_trans = np.transpose(O3_zon)\n",
    "#  model and ozone data are on the same grid, after the transpose.\n",
    "print O3_trans.shape\n",
    "print lev\n",
    "print model3.lev\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Put in the ozone\n",
    "model3.absorber_vmr['O3'] = O3_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96, 26)\n",
      "(96, 26)\n"
     ]
    }
   ],
   "source": [
    "print model3.absorber_vmr['O3'].shape\n",
    "print model3.Tatm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model3.step_forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1.0 years.\n",
      "Total elapsed time is 1.00207478763 years.\n"
     ]
    }
   ],
   "source": [
    "model3.integrate_years(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1.0 years.\n",
      "Total elapsed time is 2.00141166601 years.\n"
     ]
    }
   ],
   "source": [
    "model3.integrate_years(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4ZGYlNXdwkg4AT9fdjj7NAPbX3Yg+pdbm1NoL3uYqfMzMjp/IHUh6gOx5FzEd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\ncy5yHtTOORc5D2rnnIucB7VzzkXu/wPWAnh74MdvuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a009c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.contour(model3.lat, model3.lev, model3.Tatm.transpose())\n",
    "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.RadiativeConvectiveModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 30) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.RadiativeConvectiveModel'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\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 model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffmodel = climlab.process_like(model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.46414342629e-07\n"
     ]
    }
   ],
   "source": [
    "# thermal diffusivity in W/m**2/degC\n",
    "D = 0.05\n",
    "# meridional diffusivity in 1/s\n",
    "K = D / diffmodel.Tatm.domain.heat_capacity[0]\n",
    "print K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = climlab.dynamics.MeridionalDiffusion(K=K, state={'Tatm': diffmodel.Tatm}, **diffmodel.param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffmodel.add_subprocess('diffusion', d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.RadiativeConvectiveModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 30) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.RadiativeConvectiveModel'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print diffmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffmodel.step_forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 3.00074854439 years.\n"
     ]
    }
   ],
   "source": [
    "diffmodel.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 4.00008542277 years.\n"
     ]
    }
   ],
   "source": [
    "diffmodel.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134e5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Eb48rOd7kImB/RHw3Io4Dt1OMtzKi8H/T1dPSn6AY57q0fB3w7hEMb0qSXgb8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134e5850>"
      ]
     },
     "metadata": {},
     "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": {
    "collapsed": true
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1056bb790>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RYf48PG8T9Tr8sVWy9lbw8epcpo+Mp3OQrmeq1JE48xj7VcAXR/ukiFwnIqki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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1206b5f90>"
      ]
     },
     "metadata": {},
     "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Band model with diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffband = climlab.process_like(model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.92760732994e-07\n"
     ]
    }
   ],
   "source": [
    "# thermal diffusivity in W/m**2/degC\n",
    "D = 0.05\n",
    "# meridional diffusivity in 1/s\n",
    "K = D / diffband.Tatm.domain.heat_capacity[0]\n",
    "print K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (96, 26) \n",
      "  Ts: (96, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.DailyInsolation'>\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": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 3.00074854439 years.\n"
     ]
    }
   ],
   "source": [
    "diffband.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 4.00008542277 years.\n"
     ]
    }
   ],
   "source": [
    "diffband.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1206ce910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1056d6890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model3)\n",
    "plot_temp_section(diffband)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1218a91d0>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OY8aSIhqbbLqF1rLC9zKqynOLC8npHG2TpBnzLSLC1NFZ7DhwjE8273M6js+x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\nAQYe1toPfYm3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3a3e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(diffband.lat, diffband.timeave['ASR'] - diffband.timeave['OLR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11d275c90>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SbTqKEHZNit2QiTFBzBjZlzc2HiazuNJ0HLtX29DIom/TGRcZQHxkgOk4Qtg1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\niGh0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1218bf390>"
      ]
     },
     "metadata": {},
     "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": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}