{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#The-code\" data-toc-modified-id=\"The-code-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>The code</a></span><ul class=\"toc-item\"><li><span><a href=\"#Experiment-1\" data-toc-modified-id=\"Experiment-1-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Experiment 1</a></span></li></ul></li><li><span><a href=\"#Diurnal-variations\" data-toc-modified-id=\"Diurnal-variations-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Diurnal variations</a></span><ul class=\"toc-item\"><li><span><a href=\"#Exercise-2\" data-toc-modified-id=\"Exercise-2-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Exercise 2</a></span></li></ul></li><li><span><a href=\"#Canopy-scale-assimilation\" data-toc-modified-id=\"Canopy-scale-assimilation-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Canopy scale assimilation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Experiment-3\" data-toc-modified-id=\"Experiment-3-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Experiment 3</a></span></li><li><span><a href=\"#Experiment-4\" data-toc-modified-id=\"Experiment-4-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Experiment 4</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carbon Modelling Practical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:28.500655Z",
     "start_time": "2018-02-19T19:44:27.368941Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initial set-up\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [15, 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main purpose of this practical is to allow students to explore a model of the terrestrial carbon. The model implemented is based on that in JULES ([Best et al., 2011](http://www.geosci-model-dev-discuss.net/4/595/2011/gmdd-4-595-2011.html); [Clark et al., 2011](http://www.geosci-model-dev.net/4/701/2011/gmd-4-701-2011.html)) with some minor modifications. That model is in any case very similar to that of [Sellers et al. (1992)](http://amazonpire.org/PDF/fc2009/readings/Sellers_1992_RSE.pdf). You should probably refresh your memory of the Sellers paper.\n",
    "\n",
    "The style of the practical will be to give you access to a piece of (modelling) code that you can explore. There are set ‘experiments’ around the codes, e.g. to explore the limiting factors on carbon assimilation at the leaf level under different conditions.\n",
    "\n",
    "In the last half hour of the session, the course tutor will tell you to stop experimenting and will expect some discussion around the insights gained. You should be prepared to show some figures and possibly tables to support this discussion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main computer code is implemented in Python and already available for use in this practical. In case you want to see the code, you can do so [here](https://github.com/jgomezdans/geog0133-practicals/blob/master/photJules.py) \n",
    "\n",
    "The next code snippet (execute it by putting the cursor inside anywehre in the grey box and pressing together Ctrl + Enter) will check that the main photosynthesis model code is available, and will import it for further use within this notebook.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:28.515828Z",
     "start_time": "2018-02-19T19:44:28.504855Z"
    }
   },
   "outputs": [],
   "source": [
    "from photJules import photosynthesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A starting point is to produce a function that uses the model in an easy way. The function `do_photosynthesis` does just that. It takes a large number of options, and allows us to do different plots, etc. The parameters are:\n",
    "\n",
    "* `n`: Length of array (default value: 100 bins)\n",
    "* `name`: Plot name (plots will be saved as PDF file. Use e.g. `evince` to visualise them)\n",
    "* `ipar`: Incoming radiation in units of $\\mu mol\\, m^{−2}s^{−1}$ (default: 200)\n",
    "* `Tc`: Temperature in degrees Celsius\n",
    "* `x`: $x$-axis for plots\n",
    "* `xlabel`: Label of the $x$-axis (for plots)\n",
    "* `co2_ppmv`: $CO_2$ concentration in units of ppmv\n",
    "* `C3`: Logical flag for C3 or C4 vegetation\n",
    "* `pft_type`: type of PFT (see JULES paper for details)\n",
    "* `plot`: Logical flag indicating whether to do plots or not.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:30.135173Z",
     "start_time": "2018-02-19T19:44:28.520462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Saved result in test1_a.pdf\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<photJules.photosynthesis at 0x7f0d64fdc2e8>"
      ]
     },
     "execution_count": 3,
     "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": [
    "def do_photosynthesis(n=100,\n",
    "                      name='a',\n",
    "                      ipar=200.,\n",
    "                      Tc=None,\n",
    "                      x=None,\n",
    "                      xlabel=None,\n",
    "                      co2_ppmv=390,\n",
    "                      C3=True,\n",
    "                      pft_type='C3 grass',\n",
    "                      plot=True):\n",
    "    '''\n",
    "    A function do run the photosynthesis model. Function allows the user to change\n",
    "    a number of important photosynthesis parameters: incoming PAR radiation, canopy\n",
    "    temperature, CO2 concentration, C3/C4 pathway and the PFT type. The first three\n",
    "    parameters can be provided as arrays, and the function will show the variation\n",
    "    of photosynthesis as it sweeps over that parameter range.\n",
    "    '''\n",
    "    from photJules import photosynthesis\n",
    "    photo = photosynthesis()\n",
    "    photo.data = np.zeros(n)\n",
    "\n",
    "    # set plant type to C3\n",
    "    photo.C3 = np.zeros([n]).astype(bool) + C3\n",
    "\n",
    "    photo.Lcarbon = np.ones([n]) * 1\n",
    "    photo.Rcarbon = np.ones([n]) * 1\n",
    "    photo.Scarbon = np.ones([n]) * 1\n",
    "\n",
    "    # set pft type\n",
    "    # options are:\n",
    "    # 'C3 grass', 'C4 grass', 'Needleleaf tree', 'Shrub'\n",
    "    # 'Broadleaf tree'\n",
    "    # Note that if C4 used, you must set the array\n",
    "    # self.C3 to False\n",
    "\n",
    "    photo.pft = np.array([pft_type] * n)\n",
    "\n",
    "    # set up Ipar, incident PAR in (mol m-2 s-1)\n",
    "    photo.Ipar = np.ones_like(photo.data) * ipar * 1e-6\n",
    "\n",
    "    # set co2 (ppmv)\n",
    "    photo.co2_ppmv = co2_ppmv * np.ones_like(photo.data)\n",
    "\n",
    "    # set up a temperature range (C)\n",
    "    try:\n",
    "        if Tc is None:\n",
    "            photo.Tc = Tc or np.arange(n) / (1. * n) * 100. - 30.\n",
    "        else:\n",
    "            photo.Tc = Tc\n",
    "    except:\n",
    "        photo.Tc = Tc\n",
    "    # initialise\n",
    "    photo.initialise()\n",
    "    # reset defaults\n",
    "    photo.defaults()\n",
    "\n",
    "    # calculate leaf and canopy photosynthesis\n",
    "    photo.photosynthesis()\n",
    "\n",
    "    if plot:\n",
    "        # plot\n",
    "\n",
    "        if x is None:\n",
    "            x = photo.Tc\n",
    "        plt.plot(x, photo.Wc * 1e6, label='Wc')\n",
    "        plt.plot(x, photo.Wl * 1e6, label='Wl')\n",
    "        plt.plot(x, photo.We * 1e6, label='We')\n",
    "        plt.plot(x, photo.W * 1e6, label='W')\n",
    "        plt.plot(x, photo.Al * 1e6, label='Al')\n",
    "        plt.plot(x, photo.Rd * 1e6, label='Rd')\n",
    "\n",
    "        plt.ylabel('Assimilation rate $(\\mu mol\\, CO_2 m^{-1} s^{-1})$')\n",
    "        if xlabel is None:\n",
    "            plt.xlabel('Temperature (C)')\n",
    "        else:\n",
    "            plt.xlabel(xlabel)\n",
    "        plt.legend(loc='best', fontsize=10)\n",
    "        plt.tight_layout()\n",
    "        plt.savefig('test1_{:s}.pdf'.format(name))\n",
    "        print(\">>> Saved result in test1_{:s}.pdf\".format(name))\n",
    "    return photo\n",
    "\n",
    "\n",
    "do_photosynthesis(name='a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we plot leaf assimilation ($W$) as a function of temperature for a $CO_2$ concentration of 390 ppmv, for a C3 grass for incident PAR of 200 $\\mu mol\\, m^{−2}s^{−1}$.\n",
    "\n",
    "Also shown on the figure are the Rubisco limiting rate ($Wc$), the light limited rate ($Wl$) and the rate of transport of photosynthetic products (for C3, this would be PEPCarboxylase limitation for C4 plants) ($We$). As in other models, the rate taken for photosynthesis here is the minimum of these limiting rates (actually in JULES, a blending of the rates, but made close to the minimum here).\n",
    "\n",
    "Finally, the graph also shows the net leaf assimilation rate $Al$, which involves water limitation and the subtraction of leaf dark respiration $Rd$.\n",
    "\n",
    "We can see that at various temperatures, the different limiting rates take over: up until around 19 C it is limited by the rate of transport of photosynthetic products, then by light, for higher temperatures, by Rubisco. In this model, both $Wc$ and $We$ are scaled by the term $Vcmax$, the maximal rate of carboxylation, which has a temperature limitation (essentially a maximum and minimum temperature for operation) being 36 C and 0 C for C3 grasses here.\n",
    "\n",
    "$Vcmax$ is also dependent on the leaf nitrogen concentration.\n",
    "\n",
    "The light limiting rate is dependent on the internal leaf CO2 pressure, $ci$ and the photorespiration compensation point $\\Gamma$ that has a temperature dependence. Here, we assume that $ci$ is proportionate to $ca$, which is related to the external CO2 concentration and the atmospheric pressure.\n",
    "\n",
    "If we increase the light level (the default before was 200, we ramp it up to 500 $\\mu mol\\,m^{-2}s^{-1}$, we can remove the light limitation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:31.206087Z",
     "start_time": "2018-02-19T19:44:30.138315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Saved result in test1_b.pdf\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<photJules.photosynthesis at 0x7f0d64fd10f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u4GeH3qJhoDvY4Snz2LnKbZSUlLB9+3Y2bdpEfn4+zz77LCdPnpy2+HztQd04JVEoyjxQGJvKvauSea/lJM/XHORYbxs/Ln2V9amLuG7BcsJNIcEOUQmi8Xo6U2m8chuLFy8mJyeHiooKALq7uxkeHp622HzqQUkpW6ciCCGEQQixWwhxpRAiVwhRKoSoE0L8bCrupyjBYtAMXJy6mB+tuZZL0vLQgK3Nx/n3919mV2sV8+XwZmXmKCkp4ZFHHjl9mvnatWt56623SE9PJyUlBaPRyMGDB/F4PHzxi188PWc1HXyegxJCXA/cO/LeSqAMKAfKpJTNfsbxz8Dikc8fAO7DW6H3ZSHERinlO362qygzktUcwqcWrqEkZRF/PrGPE33tPHZsNztaqvjs4rWkhEcHO0RlnjhXuY3HHnuMLVu20NXVxebNm7n22munLTafTzMXQrQBPwYO4y33vgxYDiyRUkb4GoAQIgd4EGgHngIeArKllLoQYgsgpJT/5mu7Z7mP2qirzEi6rrO7rZq/VR/A5hzGpBm4KnMJV2QWYlbVfec0tVF3fP6s4hsCHhw5/ujtU08KIfwN5JfAN4B/AxKAbinlqazZBKz3s11FmRU0TaM4OZdlcen8rfoAO1qreKnuEO+31/K5vHXkqvP9lHnKnwT1APBVvInltFFJZcKEEF8E9kgppRACvPusXKNeogPjbhoRQmwANvh6b0WZaazmUD6Xt47zk3L404m9NA/18Z/lb3BZumBz9jJCVLFEZZ7xZ4ivAHgFOIh3nqgMOCil9Hn3lhDicWAd4AFSgT4AKWXmyPXP4x3u+6GvbZ/lXmqIT5k1HG4XL9Ud4o2GSnR0ksIi2ZK3joVRc2M4SPFSQ3zj8ydBVeKdf6rAOwe1Am99qONSyrMVOJxou48BfwHuAH4BbAdeBb4rpdzvb7uj2lcJSpl1amyd/PHYbpoGe9HQ2JRRwLXZS9Xc1ByhEtT4/BkzSAI+OXpITwgRiXexRCDcjTdRxQOPBSI5KcpstSAynntWXsnLtYd4reEorzVUcLiriS+IYnVSujLn+dOD+jXwlJRy65RENEVUD0qZ7U72tfOY3EWbvR+TZuDjOSu4JE1gUMclzVqqBzU+X486AsgFnhZCfEMIkTeZmyuKMnELoxK5d9XVXJyyCJfu4emqUn55+B16HUPBDk2ZpeZauQ2Ap4E/A9cBu4UQNiHEzpGelaIoUyjUaOIzi8/jK4UXYzWFUtHTwo9K/8GR7qZgh6bMQjO93IbPc1BSykdHfy2EyMK7UXd5oIJSFGV8K+IzWLAqjj8c20VlTyv/e3grmzIKuD57OUaDP393KvNRSUkJ99xzD/BBuY0nnngC+KDchiGIP0+T3lghpawD6oCXJh+OoigTFRMazj8XbeTV+qO8VHuQ1xuOcqK3ndsLLiQu1Brs8BQftfzXe1PSbsrdJWNem0i5jWDyOzUKIYxCiE8IIa4XQphGPf/JwISmKMq5GDQDV2ct4V+XXUpsSDhVtg5+XPqqGvJTJmQi5TaCaTI9qMeBarwnP3xXCPFZKeUJ4Ct456kURZkmi6KTuHfVlTwqd1HR3cwvD2/l6qwiPpZVhEFTQ36zwXg9nak0XrmNtWvXBiWmUybzk5smpbxXSvkD4GbgdyPHDimKEgQRZgt3LtnA5uylAPy97jAPHtnGgNMR5MiUmWy8chsWiyWosU0mQYUKIUIBpJTVwLXAt4CiQASmKIrvDJrGNVlLuatoI1ZTCIe7m7m/7FUaB3oCfq/uPjulFa0cOdGBwznukZnKDDZWuY1gD++BHxt1TxFCnA/USilbRj1nBG6WUj4ZoPgCRm3UVeabDns/v6l4l/qBbkIMRrbkFbM6MWtSbbrcHg4cbaWyuov2rg/2X5lMBhZnxbBcJJGW5HPVnXlLbdQdn08JSgjxBeBrQA7e+k2vAT+TUtZOJojpoBKUMh853C6ePLGXPW01AHwsq4hrspb6dfrEsMPNi++coL7FBkCI2UBORgwDgw4aWvtPv+66jYtYmBUTkPjnOpWgxjfhBCWE+BLwI+BnwEkgHfg4sAr4gpTyb5MJZKqpBKXMV7qu82ZjJX+rLkNHZ1V8JltEMaE+lO8YGHLy7JvHaO8awhpm5tJ1WSxIj8Zk9M4S9NiGef9wCwePtWMyGfjUlYLkeLXU/VxUghqfLwnqMPAlKeXODz1/Od6TJa6RUu6dTDBTSSUoZb473NXE7yp3MOR2kmmN5WtL1hMbGn7O9/X2D/PMa8fo7R8mJjKUGy7PIzoy9COv03Wd13bUUHGyE2uYmVuuKSDSGjIV38qcoRLU+HxZJJH54eQEIKV8A29F3PsmE4iiKFOrKC6N7664giRLBPUD3fy/steo6+8a9z0ut4eX3jlJb/8wyfHhfPqq/LMmJ/BWBr6sOJuM5AgGhpw899ZxtXhCmRRfElS7ECJzjGtP4S08qCjKDJYSHsV3VlzB4qgkehxD/Ff5G5R1Noz5+u3vN9DWNUh0RCg3bMojPMw8bvsmo4FrNy4iNiqUju4h9le0BvpbUOYRXxLU/wE/HeOakXOUZlcUZWaIMIfyL0s3UpyUg8Pj5jcV23m78aPD38druymrbMNg0LhmfS6WkInNWYWFmriseAEApUdasTtcgQxfmUd8SVD3A0uEEP8QQnz4YNjvAXsCF5aiKFPJZDDy+bx1bM5ehg78tWo/z1SV4hmZk+61DfP6jhoALl6dQUqCbwseMlMiyUyJZNjpplT1oma8Z555Bk3TqK+vB2DDhg1UVlYGOSofEpSUchC4BOgE9gshqoQQ24UQtcCteOehFEWZJTRN45qsIr6QV4xB03ijsZLfVe7A4Xbx6o5qhp1uFmbGsLIgya/2L1iRBkBpRStDw6oXNZM99dRT5Ofn8+yzzwY7lDP4dJKElLJHSnkrsBBvj+pV4LtA4cg5fIqizDLrknO4a8lGLEYz+zvq+NW7O2ls7SfMYmLThQvQ/KzYm54cSXZqFA6nh9Ijqhc1Uw0MDLBt2zZ+8pOf8Le/zazdQhPeCCGEiAeypZSlIxtzfzt1YSmKMp0KYlP41rLL+GX5Vly1IZiAtcuTCQudXEWe4hVp1Db3UXq0lVWFSYRZxl9kMZ/9n5yaQhA3i/HP7n755ZcpKSnhqquu4tZbb6WlpWXc108nX3pQ9wG3n+3CSPJSFGUWy4yIZT1LMblNDFoGea5vL+1Dtkm1mZYUwYL0KJwuDwcq2wIUqRJIf/3rX9m8eTNhYWGUlJTw/PPPBzuk03z58+ga4KIxrv2HEKJFSnlvAGJSFCUI2joHOXayB00DY+YwHcMD/Gf5G/zL0ktIt/p/dNGaJSnUNPZRcbKT4uVpfg8ZznXn6ulMBZvNxmuvvca+ffu47777sNlseDyeaY9jLL70oBKklGNVQfsFcF0A4lEUJQh0XeftPbXoOqzMT+Ib521ARCfT57Tz3wffpLqvw++2M1MiibSG0NfvoHHUmX1K8L344ots3LiR+vp6ampqOH78ODt37qSzszPYoQG+Jag2IUTuGNeOAmNt4lUUZYY7XtdDU/sA4RYTxSvSsJjM3Fm0geXxGQy6HPzPobep7PFvbkLTNApy4wCoqJoZv/gUr6eeeootW7ac/jouLo5LL72U3t7e4AU1ii8J6mngP8e4FgbYJx+OoijTze3xsKPUe5pE8fI0Qkc25JoNRr5cUMK6pAUMe1z86sg2Dnf5V0q+INc7TX2spguna+YMIc13L7zwAjfeeOMZz7344ovU1dWRn58fpKg+4EuC+imwUAjxphDivA9d+x6wK3BhKYoyXY4c76S7z3sQbFFewhnXjJqBz+cVc3HKIpweN7+u2M6Bjnqf7xEfE0ZyfDgOp4eT9YEvnqjMTb5s1O0HNgD1wA4hRKMQYpcQog64Bbh7akJUFGWqOJ1udpV7e0Ulq9IxGj76K8GgadyyaC2XpgvcuodHjr7H3pH6Ur4oXOhNfkdPqmE+ZWJ83ajbK6X8At6Chd8Dnge+AyxRG3UVZfYpPdrKwJCT5PhwFmfHjvk6TdP4ZM4qrs5cgged38td7G6r9ule+TmxGDSNmqZeBoackw1dmQf82oUnpWwAHgtsKIqiTKchu5N9h70LHy5anXHO5d+apnHdguUYNQMv1R3iMbkLXdcpTh5r7dSZwixmcjKiOVnfQ2VVJ6uXpEz6e1Dmtgn1oIQQy4QQagu4oswh7x9pxeH0kJ0WRVZq1ITf97HspVw3csjsH4/tZkfLyQm/t2Chd7FEZfX4dagUBSbeg3oZSBZCHAMOAQdHHoeklL7PmCqKElQDQ87TJztcuDLd5/dfnVWEpmk8X1POE8f3YNC0CfWkctKjMBo1WjsH6R90EBGuKu4qY5tQgpJSZgkhYoClwLKRx2agSAjhZiRpSSm/7svNhRCReMvF5wEDeI9SMgB/BMKBX0opH/ClTUVRzm3f4RZcLg8LM2N8LqVxylWZS9CA52rK+eOx3Rg0jfOTcsZ9j9lkJDs1iqqGXqobelmaNzfKnc9WNTU1FBQUkJ2dDcDg4CBFRUX86U9/Ijb2gznJDRs28Jvf/Gbal577soqvR0r5rpTyQSnll6WUF0gpo4BVwAOAP7v4vgnsllIK4F7g/wMeAm4GFgOfHWdzsKIofugfdFAuvb2n4pGSGP66MnPJ6ZpSf5C72ddee8735GZ6j01Sy81nhuXLl1NZWUllZSV1dXXExcXxq1/9KthhAX4skhjp9fw7sAZoBt4F/iClfMGP+78OVI18HoN3w68mpSwfudcLwOXAw360rSjKWew91ILbrbM4O5akuPBJt3dNVhEe3cPLdYf5feVOTJqBlQljHyyTmxENQF2zDafLg9nk02JiZYpdeOGFlJWV8ZOf/IQ//OEPpKWl0d3dHZRY/FnF91vAAjwKJAA3AV8XQlwipfSp6IuUcheAEOIAsBz4N+CCUS9pAlL9iFFRlLOwDTg4dKwd8J4aESgfy1qKW9d5pf4Iv63cwVcLL6Yo7uztR4SHkBwfTmvnIPXNfad7VPPesRVT025e2YRf6nQ6ef3111mxYgUvvvgihw8fpqmpiSVLlkxNbOfgT4LaBKRIKR0jX/9cCPFD4H+BT/kThJRypRBiPbAVGF3SUQfc471XCLEB7wZiRVHOYe+hZtweHbEgjoTYsIC1q2ka12Uvw+F28VaT5DdH3+XOJRsQMclnfX1uRgytnYOcbOhRCSrIysvLT88ttbe3k5CQwIYNG7jhhhuwWCzk5uayevXqoMTmT4LqwzscN7q4y38Cdb42JIT4H+ABKWWDlHKbEAJg9J9dacC4uwGllFvxJrZz3es+X+NTlLnENuDg0HHvqeTrlgd+YELTND6Zuwqnx832lhM8eGQb/7L0EnKjEj7y2tzMaHaVN1FV34u+TlclOMCnnk4gLV++nN27dwPeU+1vvPFG+vr6sFgsp1/jdo/bT5gy/gz+PgT8RQgx+k+jJPw7LNaId4gQIUQxsBMwCSHyhBBWvCsF3/CjXUVRPmTvoWY8Hh2RE0d8TOB6T6NpmsbNi9aePmD2l0feoWHgo/MXSXHhRISbGRhy0tY1OCWxKL7TNI2lS5ei6zrPPPMMDoeD48ePs3///qDE43MPSkr5HyMLJaQQ4iDQCxQDP/Hj/j8B/iyEuAPowLvMPBLvMJ8V+KmUstmPdhVFGaWvf/iD3tOyqZ3WNWgan8tbh93toqyzgV8ceoe7l19OUljk6ddomkZuRgwHj7Vzsr6H5Hj/lrorgRceHs7x48e58sorKSgooKCggAsuuODcb5wCmq7rfr1xpAdVAiQC+6SUwUmxEySE0KWUwQ5DUYLizV21HDzWjsiJ45qLp2fnhtPj5ldHtlLZ00p8qJW7l19ObOgHqwarGnp4/q0TJMWF89lrC6clppmmvb2dxMS5sRdsjO9lUmO3fp3FBzCyYu9vk7m5oihTr69/mMMnpqf3NJrZYOQrhRfz80NvU23r5BeH3+HuZZdhNYcCkJniPVWirWuQgSEn1jB1mppyJn/2QV2Pd1OtCagEyoByoEwNxynKzLP3UMuUzz2NxWI0c+eSDfz3wTdpGuzlVyMLJ0KNJswmAxnJkdQ29VHX3He6qKGinOL4hBj5AAAgAElEQVTPIolHgMfxngLxHpAL/AA4HriwFEUJhGD1nkazmkO5q2gjcaHhVNk6eOTou7g93qq62SOH1NY19QUlNmVm82eIbwh4UErpBt4+9aQQQq0TVZQZ5lTvKT8IvafRYkPD+ZeiS/jP8jc43N3MH4/vZkteMVlpUbAfapv70PX5t9zcYDBgt9vPWNI9G9ntdgxnKXY5Wf4kqAeArwK/HP2klNK/1RaKokyJU70nTZuafU++Sg6P4q6ijTxw6E32tNUQHRLGJxasIMxion/QSVevPahJNBji4uLo6urCZrMFO5RJMRgMxMXFBbxdfxLUG8ArQojL8ZbhKMN7krk/+6AURZkiew569z3l58YRFz0zfvFnR8ZxR8FF/PLIVl5vOEpMSBhZqVHI6i5qm/vmXYLSNI34eDX3NhZ/+mTPAe/jTUyb8JbLsAkhKgIZmKIo/uvtH+bIiU5v72lZ4M7cC4TC2FQ+v3gdAE9XleKJ8JZ/V/NQyof504NKAj45ekhvZOPusoBFpSjKpOw52IxH1ynIjSMueubNb6xLzqHHMcRzNWW80XeQBeRQ32LD7fFgnIK5DGV28ucn4S/A+tFPSCltUsodgQlJUZTJ6Omzc+TU3NMM6z2NdkVGARtS8xg2OnGGOHC6PDS3DwQ7LGUG8SdB5QJPCyG+IYTIC3RAiqJMzu6Dzeg6FObGEzsDe0+naJrGpxauYnl8Bv0Wb2I6Vt8V5KiUmcSfBPU03nmn64DdQgibEGKnEOLXgQ1NURRfdfYMcbSqE4OmsS6A9Z6mikEz8E/iAiLijADsr27C7nYGOSplpvAnQT0hpfxnKeUGKWUcsAS4H2gMbGiKovhqV3kTug5FixOIjgwNdjgTEmI08U9rzkdHxzBo4reHd+DRPcEOS5kB/Fkk0S+EqGTkeKORx1HgE4EMTFEU37R3DXKsphujQeP8IJ0a4a94q5WkhHDaO4aobu7l6YhSPrVwTbDDUoLMnx5UEnAXsB8oAn4HSLwnmyuKEiQ7y5oAWCYSibSGBDka3y1M91bWjbBbebvpGG83quoD850/9aB68Faw3XrqOSHEjwF1UKyiBElzez8n63swGQ2ct3R29Z5OyUyJYnd5M2meeFpp46mqUhIsESyLTw92aEqQBGrDwY+B7waoLUVRfKDrOu+WeqeAVxYkzdqyFamJVoxGjUGbm6tSitDR+V3lDur7P1qRV5kffE5QQogHhRC3CyHWCiFOrWFNBVTdZkUJgtqmPhpabISGGFm7NCXY4fjNZDSQnhQBQL4pnfMSvWXjH6zYRq9jKMjRKcHgTw+qEbgE+APQJYQ4zkhNKCHEx4UQ+UIIYyCDVBTl7HRd572R3tN5S1OwhPhdg3RGyBopv9HQ0s/n8s5nYVQC3cOD/PrINhxuV5CjU6abzwlKSvlTKeXNUsoiIBq4EfgaUA3cjvcwWbUdXFGmwbHabtq6BrGGmVmRnxTscCYtMyUSgLqWPswGI3cUXEx8qJWa/i4eO7YbXVdFE+aTSf25JaV04l1uXj76eSFEzGTaVRTl3NweDzsOeHtPxSvSMJtm/8BFcryVELORnr5hbAMOoqwWvr5kPf9R/jr7O+pIrYvm2uylwQ5TmSYT6kEJIe4SQoy7608IESqEuAtOr/RTFGUKHTrWQU/fMDFRoSxZNDdKNhgMGhnJ3nmo+hbv6eZp1hj+Kf9CNODlukPsb68LYoTKdJpoDyoFOCGE+AewDe++JxsQCeQBG4Cr8JaCVxRlig07XOwa2fd00aqMOXUCeFZqFFUNvdQ12yhcmADA0rh0bshZyTPVB/jDsV3EW6wsiJwbSVkZ24R+qqWU9wArgePAbcArwGHgH8AXgUpgpZTy3imKU1GUUfYdbmFo2EVaUgSLsubWiPrpeaiRMvCnXJaezwXJuTg9bh6q2K5W9s0DE56DklJ2AP898lAUJUj6+ofZX9EKwPo1GWiaFuSIAishNoywUG8Z+B7bMLFR3t0smqbxmUVraRuycaKvnYcqtvOvyy7DbJj9c2/K2c2dcQFFmSd2HGjC7dYRC2JJTYwIdjgBp2naGb2o0UwGI18uuIjY0HCqbZ386fhetbJvDlMJSlFmkdbOAY5WdWI0aJSsygh2OFPm1H6oumbbR65FhVj4WuF6QgxGdrVV82Zj5XSHp0wTlaAUZZbQdZ139tYDsCI/adaU0/BHZqq3B1XfYjtrDykzIpYtecUA/K26jIpudRToXDSpBCWEmH1HJivKLCWru2hq6yfcYmLd8tl5IOxExUSGEmkNwT7sor3r7IshVidmcXXmEnR0flu5g/ahj/a2lNnNn7P4YoQQvxdCdAE2IcRKIcRTQoiEKYhPURTA6XSzfX8DABeuSid0lh9pdC6appGVevZ5qNGuzV7Gsrh0Bl0Ofl2xHbtLVeOdS/zpQf0G0IBVgAfvcvM24NEAxqUoyih7D7XQP+gkOT6cokXz42/B0/NQLWMnKIOm8UVxAalhUTQN9vKHY7vwqEUTc4Y/f4ZdDWRIKfuEELqU0imE+A7Q4mtDQggD3sR2KdANfBVwAH8EwoFfSikf8CNGRZkzemzDvH/E+89r43lZc25Z+VhOreRraO3H7fZgNJ797+kwk5mvFF7M/WWvUdbZwCv1h7kmSx2HNBf404OqBgo+9Fwm0OpHWx8H4oBs4GbgoZHHzcBi4LNCiFw/2lWUOWPbvnrcHp2C3DjSkgK3rFz3uNEHbegOe8DaDKSI8BDioy24XB6aO8Y/fzo5POr0cUgv1R7iYGfj9ASpTCl/elDfBF4QQjwNmIQQ9wOfxr+ChSnAH6WUOlAhhFgKlEopywGEEC8AlwMP+9G2osx6J+q6OVnfQ4jZwEWr/VtWrrtdeOpP4jxRj7OxF1ePB4/LiMcTgodQNNwYtGEMJhdGK4QuSiBk5VIMMXEB/m58l5kaRWevnbrmPjKSI8d9bVFcGpuzl/NCbTmPyp3cs+IKksOjpilSZSr4k6AOAquBzwC/BezAtfhR8l1K+eCpz4UQt4+01TDqJU14iyGOSQixAe9ZgIoypzic7tPLyi9YmU5E+MQXzeouJ+5jh7GXVTHcDE5PImAZeXzotYBHjwAn0AND74P2fhkhYd1ElCzEtHx50IYVs1IjKatso77ZBivO/fqrMgup6+/iQGc9D1Vs5zsrriDMNDsrDCs+JCghxKliMw1AOvDYqMsJI8+H+RqAEMIK/BwoBtYD3xl1WQfc471fSrkV2DqB+9zna2yKEky7y5uwDThIigtnhZhYrSdPdxvD7+5h8MQwTncykHzqCqawYcyJIZgz4zEmxWGIjsAQFoLucuPu6cfT2YOzuoXhRjtuRxjDQ8kMv9GHZddzRGxahmnhoin7XseSkRKJpkFz+wAOp5sQ8/jHGmmaxpa8dbSU99E82Mtjx3ZxR8FF82bebq7xpQfVgjdhaHjnm0YvlfEAL/p6cyFEOPAu3hPS1wDxQNqol6ThnfNSlHmlvXuQ0oo2AC4rzsZgGP8XrKfuOANv7WeoIxoPsaeeJSTOiWVJOpYVCzFYTDjdg9icLbg93XjowKO7CbVEEZmVQuiCRMJWLwbA2d7L4BulDDUasfcnYX+2gcjcY4R/4ko0bfr291tCTCTHW2npGKCh1UZuxrkPxrWYzHyl8CLuP+BdNPFqQwVXZS6ZhmiVQPPlsFgDgBDikJQyUEtk7gDek1J+Y+TrRiGESQiRh7e0/GbgYwG6l6LMCrqu89auWjy6znKRSEqCdczXeqqPMvDWfga7k9HxzlEZzA7Ci+IJLV5Mj1bHycFy2tv/Sp+jgSFX95hthZliiQ3NJSuymIz484i+ZSPhLZ30/72U4a4wbFVROH/7AlG3XoEhLDzg3/dYslIjaekYoK55YgkKIDksii+KC3iwYhsv1JSTFRHLkti0c79RmVG0QB20KIT4qpTy1z6+50/AxZxZIn4L8DvACvxUSvnbAMWnSykD0ZSiTKkDR1t5Z2891jAzn79+CZazbMr1tNQw+Mq7DHSkoI+MrJusTsIuzqUnu5Ma23aaBkpxec48hcGgmYk0p2A2hmPAhKYZsLt6sDlb8OgfbHI1ambSI85jacJNRIWkMbjtAH17+wAjZnMXMTevxZg8PadZ1DX38czrx4iPsfD564p8eu9LtQd5ue4w4aYQ7llxJYlhc+9w3RluUmOrPicoIUQRcB/e4bhTzECelDL57O8KPpWglNmgt3+Yx184gtPlYfPGhSzKij3juqe3neFX36C/LhL3yFCeKdKFtiGBurhSavq2Y3f3nn59pDmVFOtyUsKXEhOaTbg5AYP20Xkcj+5m0NlB80AZtbYdtA8dBcCAkbzYqymKvxHPiTZ6Xz6Bx2PBaOwlbksxxripX+nndnv49V/KcLo83H7jMiKtE18s4tF1fl2xjUNdTWRYY/jO8k2EGOf2KRwzzLQnqB3ATsAFFOItWvhV4JtSyrcmE8xUUglKmel0XefZN45T29xHXnYsH9uw8INrzmFc772Obf8ADn1kKC/UheMSI9Wxe2js34s+Mi0cGZJGTtTFZEeWEBHi39+MA852Dnc+Q1XvO4COxRjNutQ7SbRn0/3Hvbjd4ZhDuoi9/VIM4WMPQQbK828dp6qhl00XLKBosW8naQy6HNx/4FXa7P2sS8phS946tWhi+kzqP7Q/s53LgX/Du6E2Ukr5MHAr8IPJBKIo892RE53UNvdhCTWy8fwswJu0PHI//b/5I53vR+LQM9AMbhwXDXH4+m28a/5fGvr3AAYWRF3E5Vk/5ZoFP2dJ/A1+JycAqzmR81O+wqbs+0mwCOzuXrY1/JRqwx5ib1mFQbPjdMTR+9ibeKbh/LsF6dEA1DT1nuOVHxVuCuGOwosJMRjZ3VbNtubjgQ5PmSL+JKgOIB+oBxYJITTgJN6S8Iqi+ME24GDb+949TxvWZmENM6P3deB46nd0vtjIgL0QMDGc382RG7axI/YRWocOYzaEURj3cTYv/DXFqXeRELY4oL2DeMtCLsv6IUvib0DHw/ttv6Nce5Go6xei4WR4IJ7+J14N2P3Gkp3m3XBb29SHx+P7vHm6NYZbF58PwFNVpZzsaw9ofMrU8Gcw9kfAbmAtsB3vEnEj3uXiiqL4SNd1Xt9Rw7DDTU5GNPkLonHvfZWB96oYdBcBBhwxNuo3HKXOsxuGdUyGMETs1eTHfowQ49RO/GuagWUJnybSnMre1t9wrOcV7JE9rLrsSnrfbGewIxbztl2ErS+eshhioyxER4bSaxumpXOAND8qCZ+XtIBqWydvN0keOfoe31t5JVEhPm/dVKaRzz0oKeWjeE93qAJuAx4HngBuDGxoijI/lMv2kaE9E5cLM64n/4fubXYG3cvwaDqNJYfZvf5P1Hl2YcCAiP0Ym3MfZFnCp6c8OY2WE72eSzLuw2wIo862i4r0PYTnea/17R3A3dY2pfdfcKoX1Tj26ebncmPOShZFJdLjGOK3lTtw655AhadMAZ8WSYycPr4fWCOlHPeEh5lGLZJQZqLuXjtPvFSBy+3hmpROMo7sp9+9Dp0Q+lKbOHbee9h07yliGRHnsyLxM0SGBLdYYevgYbbW/xgPbpbHf5bkv4TgHIokJKyLmK9eg8Ew/mkP/jpZ18ML75wgNdHKzVd/+Lzqiet1DPHj0lfoc9rZlFHADTlqdmIKTd8iCSmlB2+Cun0yN1UUBTwenVfeq8bl9lCgN5B8qB6b+2KcRjhR8h771z6NTW8mwpzCxozvc1H6t4KenACSw4tYl3onAOWdT9J7HWg4cAzFMfTy1im7b2ZqJAaDRkvHAPZhl9/tRIeE8aWCEgxovN5wlNKOugBGqQSSP4sklgEPCiGahBAVox+BDk5R5rJdZY20dAwQ6+pnTbsbu55Pb3wT+696ivq4/RgwUhR/I1cveIAU67Jgh3uG7KgLWZF4KwDvDz2JVuxdyWeTRlx1DeO91W8hZiNpSRHo+vhVdidicXQSN+R6e06PHdtNy+Dk2lOmhj+LJO4OeBSKMs/UHa9hz6F2Ip0erugZxkUiNUv3UJOzB9CJCc2mOPVOYkKzgx3qmPJjr6VnuI6avm2UZrzF2piLcfbEYPt7KbFf8a80yLksSIuiocVGTVMfeQsmt0n40jRBVV8H+zvqePjou3x3xRWEqk28M4rP/zeklNumIhBFmQ90XWewdBuvHNSIc4dxeY8dt8nI0eK/0xlTBWgUxF3P0vibMBpmdpkITdNYk/RF2oeO0j1cTe1li0h7JoLh/jiGd5cSum5VwO+5ID2a90obqW7oRdf1SS2p1zSNzy0+n8aBHpoGe3ni+B5uExeoTbwzyPQdS6wo85w+2If7uV/wemkHIe4wLu0Zxh7RQ+klT9MZU0WoMZJLMv+dFYmfmfHJ6RSzMZzi1LvQ0JCONxla6t3L1bezFY8z8Bt4E2PDiLSGMDDkpOUcVXYnwmIy8+WCiwg1mNjXXstWtYl3RlEJSlGmgV59CM/j91HWotGjZXFpj4P+uAb2X/wUg6FdxIbmcEX2f5Ac7tthqDNBYphgSfyNgE75ovfwhPTgdkcy+PL2gN9L0zRyM7ynSlTV+36qxNmkWaO5Nc+7iffpqlKq+joC0q4yeSpBKcoU0l1OPO/8H57nfk6TI5zDIedzSY8DW0INB4tfxG10kBVZzGVZP8JqTgx2uH5bEn8D8ZZFDLm7aFjvPWh24ISGuz3wv+wXZXlLbpyo7wlYm2sTs7kkTeDWPTxy9D1sDnvA2lb85/MclBBiDfAfQOaH3y+lzA1QXIoy6+ldLXj+/htor2fAGMHW6GtZ3+GiL/kkR9a8gm5wsyj6ctYk/9O0FgGcCgbNyJrk23m99rtUhb1PUmIalvZs+v+xl+jPXx3Qe2UkRxJiNtLZM0SPbZiYyNCAtHtDzgpqbJ1U2Tp4VO7krqINGGb5/5fZzp//+o8DpXj3Qt38oYeiKIDnyA48f/ohtNfjiU7mrawtnN/ppj/pBEfW/gPd4CYv9mrWJN8+65PTKXGWXBbFbELHw/HicnR0htqsuBqbA3ofo9FATrr3VImqAPaiTAYjXyooIdIcytGeFl6uPRywthX/+LOmMgH4npTSEehgFGW2053D6G89iV6xEwAtfx27Yy5h8Z4WXLG1HFnzKrrmoSDuOpYnfGbOrRhblvBp6mw76XBX07WojPgTKxl4dT/RtwW2MPbCzBhkTTcn6ntYVRi4MnSxoeHcJi7kF4ff4e/1h8mJimdpXHrA2ld848+fbn8G7gx0IIoy2+kdDXj+9CNvcjKFoG36ApWLryd8XyvmiGYOnfeyt+cUc9WcTE4AIcaI0xt4jxUewGWyM9QVhbO2PqD3WZARjUHTaGy1MTSJUyXOpiA2hc3Z3o3Rv5e76LD3B7R9ZeL8SVDFwH8JIVrUSRKK4uWp2Innzz+BrmaIT8Nwy720Ja+g+/UTRIe2cXDd83hMThZErWdV0pY5mZxOyYlaT4IlDzs2mop2AQYGXisP6D0sISYyUrynSlQ3BGY132hXZhayNC6NQZeDR46+h9Mzq44enTP8SVDfBjYCnwK+8qGHoswrusuJ540/or/6KLgcaIUXYLjlXgbCE6l4voJ0vZvy4udxhQyTHrGG81O+MmfmnMaiaQZWJn0OgJqsSoZDbdh7o3AerwrofRZmelfznQzgPNQpBk3jC3kXEB9qpba/i6dO7g/4PZRz86fcxraR0ySOAp3AgVHPKcq8ofe24/nL/eiHtoPRhHb5FgxX3oZLM7P3uQoWDfZQVvwcDssASWGFXJj6DQza1Jz0PdMkhAnSI9bixkHD8p2Agf63AzvIcipB1TT24nQFvmyG1RzClwsuwqQZ2N5ygt1t1QG/hzI+nxOUECJDCPEu3iq6rwFtQohnhRApAY9OUWYoveYwnid/CG21EJ2I4eZ7MCy9CF3X2fn3Sha1dnHw/BcYiughJjSbi9K/jdEQEuywp9XyhJvR0KhPOcagtZPhviic1bUBaz8qIpTk+HCcLg/VDYHvRQFkR8bx6YVrAHjy+F4aB6bmPsrZ+TPW8Gu8VXRjpZTpQDJQC/w+kIEpykyk6x48e17G8+zPYXgQcpZh+My/oyV5D3Xd+85JFpxso+K8l7HFtmE1J7Eh43uEGK1Bjnz6RYdmsiBqAzoe6pbtAAwMvHUooPfIz/EeGFtZ3RXQdkcrSVlIcVIOTo+bh4++y5Ar8Ec4KWfnT4K6CPh/UkoXgJSyF/g+sD6QgSnKTKM77Hheegh9x3MAaMXXYbj+TjRLOACH9zeQVNrE8ZWv0Z1YT6gxmo0Z9xJmig1m2EG1NOEmDJqZ5sRqbNEt2LujcDU0Baz9UyeaVzf0YncEdjXfKZqmccuitaSHx9A6ZOOJ43vwpdCr4j9/ElQ9sPpDzxUBgd2NpygziN7Tjuf/fgonSiE0DMP1d2Io3nx6wcNJ2Ubothpql71Ne9oJzIZwNmZ8b0YUGAwmqzmBvJgrAagtGulFvVEWsPYjrSFkpETi9uicqJ264bcQo4kvF5RgMZrY31HH202qOvd08CdB3Q08J4R4TAhxrxDiYeAfwA8CGpmizBB6XSWeP/8IOhshNgXDLfei5S4/fb2hrhvnP47RKt6lOfsIRi2E9enfJdaSE8SoZ46CuOswaaG0xzfQG9PCUIcVV0tbwNo/Ncwnp3CYDyA5PIrP5xUD8Ez1AU72tU/p/RT/VvG9BqzAu4ovFW/Pab2U8skAx6YoQec5uA3Psz8D+wDkLMVwy/fQYj9YD9TWZqP3uaN05+6hflEpGkZK0r5FYnhBEKOeWSymaBbHentRdYXvAUYGXg/csu3F2bEYDBp1LX0MDE3t/NCqhEwuS8/Ho+s8cvQ9+tShslNqwglKCHFw5ONR4FVgC3AJ3v1Qf1UbdZW5RPd48Gz7K/qbj4PHjbbmSgzX3YUWGn76NT29Q7T+9TADmfuoyd8NaFyQehdpESuDF/gMVRC7GZNmoSOhkd7YZoZaLbi7ugPSdlioiQVpUeg6HKuZ2l4UwCcWrGBRVCI9jiEelTvw6IFf4q54+dKD+vrIxzvwbsq9Y9RDbdRV5gzdOYznxV+h738dDEa0TVswXPxJNMMH/1xsNju1T5YznLqfk0veA2BdytfIirogWGHPaKGmKPJirwKgtuA9wMzg63sD1n5+bjwwtav5TjEaDNyefyGRZguVPa28VBvYlYnKByZ8WKyU8lT1sQop5UcGX4UQ8QGLSlGCRB/oxfP8/0JrDYSGY9j8NbTM/DNeMzDo4OST5bgTD3B8qXd/+trkL5ETrRayjic/7lqOdb9CZ0ITvbHNaPVxhPf3Y4yImHTbCzOiMZkMNLcP0NNnJybKEoCIxxYTGs7t+RfyP4fe5h/1R8iNSlCHyk4BX4b4koQQSUCjECLx1Ncjj0KgYerCVJSpp3e14PnLT73JKToBw83f+0hyGrI7OfZEGZ74Uo4tfxuAVUlbWBRzeRAinl1CjZFn9KJ0Qhl6fU9A2jabjSzO8i7nP3R8eiriiphkrl+gDpWdSr4M8bXgXRBhAlpHvj71KMe7ks9vQojrhBD/b+TztSMH0NYIIf51Mu0qykToTSe9yam3A5IXYPj0PWhxZx6OYrc7qXy8DD12P3IkOa1M/Dwi9ppghDwr5cddi0mznO5FDVZ58NiHA9L2cuGtSHz4RAdu9/TMC23KKGRZXDqDLgcPH31XHSobYBNOUFJKg5TSCBwZ+Xz0wyylvMGfAIQQmhDif4BHRj39EN4CiIuBzwohVKVeZcro1YfwPPPf3pV6ucsx3PRtNGv0Ga+x211UPn4AT8z7ZySn/LjA1jma687oRYkdePQwht4KTC8qNdFKQmwYQ3YXJ+qm50gig6axJa+YBIuVuv5u/qoOlQ0of5aZLz3b80KIr04ijrfw1plCCJEKaFLKcimlE3gBUOMnypTwVO7B88IvvSeRL7nQO+dkPrOE+PCwi8rHS3HG7zk9rKeSk/9O96KSGumNaWGgcgiPa/KnQGiaxvI8by+qXAZun9W5jD5U9t2WE+xsDeyp7fOZzxV1hRBFwH3A6EURZiAP7zl9PpFS6sDLQogEIB/I4sz5rCa8+63GimcDsMHX+yqKp3wr+ltPAjra6ivQLv7kR+o02e0u5OMHGE7cQVWht0ru6qTbyBvZ16P4LtQYyeLYKzna9Ty1+TuI3n0D9rf3EL7pwkm3nZ8bz/b9DTS09tPZM0R8TFgAIj63rIg4blm0lseP7+HPJ/aRaY0lM2L+HnEVKP6UfH8Y2AmcAArxzj19FbglQDHpgOtDX485sCul3ApsPVejQoj7JhuYMnd49r+Gvu0pALSSGzCc9/+3d97hcVTn/v/MbFGXrC7LRe7HNu7GxhQbYycUhxBaEkooISEhuSkQknuT8Eu5Nw+Qm5B+EyAJIQGS0MF0YooB29g023I9NpZlS7Ikq3etdmfm98cZWSuXYGlXu2vrfJ7nPFN29uw7u7PznXPOe953xRHHdHX1sOtvG+kofpN94h3AYGHhl5k4YnmMrT35mJb9SXY3vUhDQSWtI2rwbE0neZmF6Y0sHUmS38PUCTls2VVP6a46zlk4NkoWfzRnFk1kT2s9a2v3cO+Ot/j+3PNJ9Q6vCPbRZjChjmYD30ONE2VIKe8FriF6oY6qgOKw7WJgf5Tq1gxzHMfBXv9snzgtu/qo4tTZ3sPu+z+gpWTVIXFaVPQ1LU5RIsmbeSi6RLlYh2Wl0/3mu1Gpe/aUAgC272kgGIqt08IVE+czJi2buu527pdvY+ugshExGIGqR3XFVQCThBAGKjdUVKbPSymrAK8QYooQIg24CFgVjbo1wxvHcXDWPY2z7mkwDIxzP485Z9kRx7W3drPngfdomPICVRM2Y+DlrOJbGZ+1JA5Wn7xMzf4kXiOJhsIKWrKr6djchB0FL7iC3FSK8tII9FjsLBv6ibvhqKCyiyGbJj8AACAASURBVEn1+ihtrOLlSh1gJxIGI1A/AdajROpNVG6ofwFvRdGurwNPAluBe6SUOlK6JiIOidOG58AwMS64EXPGWUcc19rYSfmD71Iz/RlqxuzAQxJLR3+PMRmnxcHqk5tkbxZTslXrde/UtVihDAJrouMFN3eaakW9u7UG245tKyY/JZ3PT1ERRVaWl7KjqSamn38yYQwmr4kQIgPonbxwLeABHpRSdkbRtqgihHCk1CHyhyNHiNOKL2GKBUcc11jbRs3j77Fvzkqa86rwkcrSsd8nL0XEwerhQY/VzjNl/0HQ7mTO2kvJb8ki55vnY5qDeXbuw7Yd7n96Ky1tAS5YPJ5pE2If6GZl+WZeqNhGujeJ2+adT07S8EtaCRgffcixGUgkiR3u5NntwAZgk1u+BdwMvBeJIRrNUOGsW/mR4lRX0ULN4+vZs+BRmvOqSDZG8LFxP9HiNMT4PelMzbkIgL3T1hIMZRBYE/mtxDQNFs5UE63fKa2OS4LBT5bMZPqIItpDAe7dsUZP4h0EA/Hiu2nIrNBohgh7w/M4G55VY04rbjyqOB34sJ7mVzYgT3+K7rQW0jyFLCv5Iem+gjhYPPwQ2SuQTc/TklNLY8E+vO/lkXRG5B590yfksn5zNQ0t3Xy4v5nJJbF1+zYNky9MPZPbN75IeVsDj+55n6snL4ypDSc6g+riOxHRXXzDD/uDVTirHwYMjAu+iDlt0RHH7N9SQ+uGNexc+AzBpC5G+MZzztjbSPZmHVmhZsjY0fgMm+oeJKM5j/lvXkXmbJO0KMyL2rTzIK9t2E9BTipXXzjtiHlusaC8rYGfb15FyLG5fsoiTi8cVoFxIvrCj7sFJYQolVLOcvNBHVXVpJTTIzFGo4kWdulqV5zA+Ph1RxWnPe9U0LHjDXae/gKWN0hR8izOGvNtfGZsJndq+pg84jxk43O0jajnYPEuPFvGkLK0B9Mf2TyiUyblsaG0moONneytamHC6BFRsvj4GZeRy5WTTuXB3e/w9w/fZVTaCMam58TcjhORSPJBHa1oNHHH3rEe5xWV4Nk45yrMmYuPOGbn63toLnuR7QufxfIGGZe+hLPHfk+LU5zwmknMzPssAGXT3yKIn86X1kZcr89rMv+UQgDWfFAVc4++Xs4qmsRZRRMJ2hb3bH+L9mB0AuSe7AwkWOyb7vIN4APUhNrqw4pGE1ecPZtwXroPcFSEiLn9J9Y6jsPWZ7fT1Po0u+auwjFtpmV/ikXFX8M0BhNYRRMtxmctJcs/lu7UDionbKRjl4XV3hFxvbNFAZlpfuqbutiy64hUdjHjiomnUpKeQ0Ogg/vkOp2J9zgYsC+nEOLbqMm664C1YWVNdE3TaAaGs3879nN3g2NjLFxxRIQIy7IpfXQT9UmPqhTtjsH8gi8wp+BzcRmb0PTHNDzMK7gOgH1T3iXgt+h4NjqtqCWnjgZg7aYqugKRB6YdlB2mh5umLSbNm8T2pmqe0Zl4P5LBTDb4DjBbSlkgpcwPK9rlSRM3nOoy7JX/B1YIY/Y5GGde2u/1YNBiy0PrqRn5d6pLtmHaPhaP+o4O+ppgFKXNojhtHpY3yF6xns7KJIL7Is+FOrkkm9FFGXQHLN7edCAKlg6OnOQ0bpx6JgYGL1ZsY1N9RdxsOREYjEDVAl3RNkSjGSxOfSX2k7+CYABj6iKMZVf1axF1dwTY9uBqKqY9RGPhPrx2GsvH/YjRGUe6nGviz9z8azEwOVCyjfbMRtqe2xxxnYZhcM6CMRiGSsVR1xS/mALTsou4ZPxsAO7f9TY1nS1xsyXRGYxA3Qa8IYS4Swjxw/ASbeM0mo/CaT6I/cQvIdAJE2ZjnPd5DKPvsm6t72DHoy9TNu8h2rPqSHEKOH/inXoCbgKTmTSKySPOA8NBznqFQGcG3VEIgZSfk8qsKfk4Dry2fn/cHCYAzh01jfl5Y+m2Qty9/S26QsG42ZLIDEagvo9K856BytMUXjSamOG0NWE/8QvoaIExUzEv/AqGp8/RoW5fM7tfXMnuBY8QSGlnhDmJCyb/lAy/vlQTnZl5nyXFm01rzkGqxpfSuqE+Kqnhz5g7itRkL1UH23l/e/xi5BmGwbVTTqM4NYuarlb+uktHPj8aA56oK4RoBwoSOe7e0dATdU8unM5W7Ed/Bo3VUDge89O3Yvj7XMQrt9ZSteNJ9s54Hcd0KPafxlkl38Bj6vw8JwqVbe/y1oGf4Ql5Wfj6NeTkesm88tyI691b2cJTr+7GNA2uXDGVwtz4xcir7Wrlzo0v02UFuahkFp8YOyNutgwRsYnFF8Zfgesi+VCNJhKcrnbVrddYDbmjMC+9uZ847V5TRtn+v1A26zUc02Fq5sUsGfctLU4nGKMzFjAmfRGWN4Sc9TodlUkESndGXO/40VnMmVqAbTu88ObemOeMCqcwJZMvTD0DA3h2XylbGqviZksiMhiBOgf4vRCiISyA7A43iKxGM6Q4gS7sp34NdRWQXYh5+a0YKenqNcdhy3Ob2GvdQ9XED8A2WZB/E3NHXt1vXEpz4jC/8AZ8ZhqNheXUjt5N66p9WB2Rd94snj+a3Kxkmlq7eePdyL0EI2FmziguKpmFA/x55zpqO1vjak8iMZh/7VdRInUpfVElbkIHk9UMMYfEqWYvZOZhXv5tjDQVMy8Ustj42JvsLbiXhqK9eKwUlo65jUk5OgPuiUyKN5u5+dcAsGvWq7Sl9ND2yOqI6/V5TVYsmYDHNCjdVUepjN8EXoALxpzC3NwxdFtB7t7+pnaacBlIuo1Sd/UeVLr38HKPWzSaIcEJdClX8gMfQkaOEqcMFc+ss6WbDx57mrKp99GRVU9yKJ8LJv0vI9NnxdlqTTSYkLWMsRmnY3lDbD31eTqaUqPm1bd8UQkAr27YR1llc8R1DhbDMLh+yiKKU7Oo7mrlL3KddppgYOk2wmPxaTQxwwl0KnGqLlPi9On/xBiRD0BDRTM73/knFXNW45g22bZg2dTv4vekx9lqTbQwDIOFhTfR1F1OW1Y1u2euxvv2Ejyj9+MbNzaiumdMzqOlPcCG0mqef6OMz5wv4uY0kez18dXpS7hj08uUNlbx3L4tXDRueD9kDTYWXwrwNiCBTwALUUkMNZqo4nS2YT/+CyVOmbmYn+kTp7L3ytm84zfsn/Eajmkz3n8u5079by1OJyE+TypnFt+CafioLtlG9eg9ND+xA6sx8lbPGXOKmTYhl2DI5ulXP6SppTsKFg+O/JSMQ5Emnq/Yygf1++NmSyIwmFh8dwK/RrW+7gYmA8vQXXyaKOO01GM/cifUlkNWnmo5ZeVj2zabnlvL9tBd1JZsxbC8nJp9E4vG34hpRJbkTpO4ZCePZ37BDQDsnP0KdTkNNP99LXZPT0T1GobBuWeUMKYog46uIA+/tJPahsiD1A6W6dkjuXT8HADul29T0d4UN1vizWCcJK4DlqByQi13t68APhlFuzTDHKeuEvvhO6CpFvLHYF7xfYysPLpau9nw1CPsLrmbtuxa/D1ZfKzkf5hcoJ0hhgMTs5YzZcQFOB6brQufpSG5i9YHVmFHmE7d4zG5eNkkSooz6eoO8djLkoqatihZPXA+PmoqiwrG0WNb3L39Tdp64teqiyeDESg/0AmsADZKKVtRXX56RE8TFZyyzdiP/FRFiBgtVLdeWhYV2ypYv/ZXlM94kpA/QHZwOhdO/xV5aZPjbbImRhiGwbyC6ynJWIzlDVF62koaghatf3kJO8L5TD6fh4uXTWLKuGx6gjZPvrKLHWUNUbJ8YBiGwecmn8a4jFwaAh3cu2MNoQhF+ERkMAL1J+Bd4I/An4UQM4CVbtFoBo3j2Njrn8V++nfQ04Ux5VTMS2/B9iTz/guvsrHzdmomfAC2wRTfZZx3yo9I8mTE22xNjDEMk0Ujv0px2jyCSd1sOv1J6q0eWv78UsTdfR6PyYrFE5g1JR/Lcnjxrb2sWldOMBT73E0+08NXpi0my5/C7taDPLLnfQYa+edEZ8ChjgCEEGeDcpgQQswCFgAPSCkT1nlfhzpKbJzuDux//RU+/AAwMM66BGPBCurK69m+7R/UTHgbx2OR1J3DmeNvoTBzarxN1sSZkB3gjco7Odi1DU/IxynvrmBkRzZZ156NJyMyRxnHcSjdVcfqdyqwbIe87BRWLJ5AXnbsMy7vbavnrs2vEHJsrpg4n3OKT6hAxxGFOhqsQJ0PvAZkA7cCdcDvpJQJ21GqBSpxcfZuUeLU0QxJKZgrvow1ahobX11Fdd5KOjJVN0tR8AwWT/8KXjM5vgZrEgbLDrKh5vfsa1uLYRtM3nIOYysmkPWJSfinToy4/oONnTz3xh6aWwOYhsHcaQWcPqcYvy+2zjjvHCznPrkOE4Ovz1jK9OwTJuBxbAXK9eK7BJgH/N3dnQzUSimvj8SYoUQLVOLhBDpx3nocp/QNtaN4EsZ5N/Dhnnr2NT9O/ZitAPi7RzC/+EuMy9f5mzRH4jg2W+ofYVvjkwAUVE1m8pYl5E5KIeWCxZhmZGGueoIWb75XSambLj4txcdZ80YxbUIuphm7TMxPl2/mxYptpHh8fHfOeRSlZsbssyMg5gJ1AJgDtKGSF452jSiTUuZGYsxQogUqcXBsC6f0DZy3V0JXO3i8GGdcTFXmdHaVP0b96PexvSEM22S0tZxF06/DaybF22xNglPWspr3a/9MyAngC6QgNi+nuDmPjPOm4Z8yIeL6a+o7eG3DfmrqlQt6VrqfBTNGMn1SLl7P0Md6tB2He3e8xaaGSgpSMvju7HNJ8yX8/yLmAlUPjAPOA74hpTxbCFEEbJVS5kVizFCiBSr+OLaFs/sDJUyN1QDYIyezf9w5lLetoXHkRkJ+lfMnq20ap4kvkJtREk+TNScY7T21bKj5Awe7VOzqnIMljN95GoXeNNIuXIg3P7JblOM47ChrYENpNU2t6lpNTfYyfWIup0zKI3fE0I5RdVtBfr55FZUdzYisQr4xYyleM6Hn/sWli+9ioAC4GdgI3Adsk1LeEIkxQ4kWqPjhBDpxtq7B2fgKtKrxpK7M0cjRM6lP2kJT0U4cU3lJpbWNZd7Y6xldMDOeJmsAHAecADjdYHeD0+OWEBBSr2Oh7kEmGCYYXjCSwUgCMwXMNLUvpmbb7Gp+kdK6hwk5alg8r3oCYz+cSwHppC2ehl9ENj5l2w679zfxTmk1dU1dh/YX5aUxuWQEE8dkk5M1NGOljYEO7tz4Mq3Bbs4snMg1kxdiGLHrahwgcXGSOJoX39+klKFIjBlKtEDFFifYA3tLseU7UFYKVpBuJ4k9RYKD+QFa83cSSHUnQjoGWa3TmFFyGWMLh3fssahhB8BuV8Vq7Vu328BqO2y7A+wOd7sD7E61dLqIyvRGIxnMDPBkgzcbPCPAkwfePPAWhJVCiKIDTMBqY0fjSnY1voCFcjBObctm5P4ZjDpYTFZRDsmnnYJ3ZOGgP8NxHKrrO9i2ux5Z3khPsM8dfURmEiUjMxlVmMHownTSU6OXj6y8rYG7Sl8haFtcNn4u546eFrW6o0zsBSocIUQScBFwhZTysogqG0K0QA0tTigI9ZU4FTtx9m+Hqg8JBGF/VjH1I3y057bRnltBMKnvadPXnUFuYC6zp11CTsboOFqfADiO21LpUgLhdIWJRXhp7xOQQ6LSfth6OzhRmvFh+MNaREnuthfwqhYTJuCAYwO2+lwnoIrtngMDmEPkyVZC5S0CX5FaeovAV+juzwfDN6BT6Ao1IZteoKzxVQL0RYdIby4gt3Yc+U255PrySS0pxjdzCp7Mwc2tCwYt9la1sKeihb1VzXQH+k+szUjzU5CTSkFOKnnZKeRkJZOVkTTo8av36/bzx51rMIDrpizi9MLIx9mGgLi0oDzAucCVwKeARuBZKeU3IjEmrH4D+AMqWkU1cLmUMqKsYlqgIsdxHOjphrYGaDqI01yLXV9NW10NDT0BWlL8tKdBd1o3PWmtdGXWY/n6T5z0d2WS0TWZCcXLmDj21MRMJOg4qC4s92Zr93ZtBcJuwD2qleL0KGE5tN3tvid8vbvvhn2ou6wrbOnujype8GSAma5aL2Z62Hb6YdsZqiuuX0lVXXSRds85DjidqhVntYDVCFYThBrAqoNQHYQOQrBGLfmoThgDPDlKqLx54MkHb667L1cJnCcLzCy1NJLB7f6ynRAH2jeyp3EVtZ1bsMywz3IM0lpzyWwuIK0jncxgBpneXNIzCvEX5OEpLsDMzcY8zvEe23aoqe+gsraNyto2Dhxs79e6OnQ2BmSmJZGZ7lclLYm0VB9pKaqkJntJTvLi85pH7cb7V+UOnti7EQO4XpzOooLxx2VfDImdQAkhlgBXAZcBrUAxcL4b4TxqCCE+BVwDfBr4DLBCShlRmvkTVaDU7+OE9bQ4WKGQu8/BscFxLBzbBsfCtm2wbRWbzLaxQyEcx8IOhbBCQWwrhG0HsXpCOFaQUCiAFQxiWT1YVg+hYA8huwfLChKyA1hOEIsgISOIZQaxvCEsXw+WP4Dl7yKU1EnI3+1ehcrI3ivSAPxdGaR0jSTTO57xYxdQkDPWvV9Y7lN3+DLkLt1CSO1zgmHjHsH++5xg2NhIMKz0HLbe8xHrgf774xG5y0hSwmCk9AmEmeqO46T2rZupbpdZGhhp4El394eJz4no9ejYYDW4YuWWYA2EaiFYrQTMamBALbJDQt0rxuo7tI0kuuxu2nuaaAs00WV0ETIMLAxs+pY2BmYwCU8gFU8wGU/QhyeUhNf247F9eI1kPCThMf14zGQ8vmRM04/Hk4TpS8bw+PH4ksD002F5aO4waOxwaO50aOkI0dZlYzkGDga2YYBj4AB22H3dAUzDJDnZT5Lfg9/vwe/z4Pcp4TrQ3UxZez2O4TCvYAzjs5RXocdj4PGYeEwDj2lgHl6MvqVhcGhpmAYGKtyS12uSkhTRQ0pEAnXcnyyEqERdGY+iBONdIUQdsDsSA47BCtSYliOEeAr42RB8xnFRsekfjEqJ28cflai0OY7SHR5+JUVtzDUVSN2k1lvcckLgVV1JRpJamn533e+WsHUzyX1S96sxFCMprEssvHssxW2VuA4ERoo63kxxj0tob6yhxzDdllE+cAwnGScEoXrV8rLq1brVCKFGt3XW6LbUWsBuUQ8bVpMqYZhAmlsKP+pr93PU/8tAyfBCURaQFXldx4XtPucdg+N5BDvYk0fKzFeiZtJAGYg07gUEkETfuQ3VY+ZYoBJAStkjhPAIIUwp5RGPTkKIpcDSIbIDgBjOxTuhcNRzFmBg9Mqb0bevz7srfL33buBxxzA87r7epddd9nqEecPGPHxh+3qFwtu3xBUSfIe91lt6hcZdN4623luGuVgkKoZXjU35io7v+EPOIq39HUDs3q7Vrr4u2EPdsX0tb8fpwba7sOxOHKsb2+rGsQLghHCwUF6MNurf4IBhH+pNMIy+26QRtjxab0P/9SNfO/yY432A/HfHHU8VRpxjgB+3QEkpFwshxgKfAx4QQphABjABOBBlu9xBgEOEjiZOrl2rgdUfVaEQ4keDMWTkKZ8mELrw3x5zfOMox74c+vct962bniPf4/Ec7cY5GAU91nuObovaNI55pEaTkJhu65XBxRAwAPfxacjo7cZ3cJeOg4OF7dj9/nOO7Xb3g9vL2St3zqH/oh0KhkmKw7H/pU7fQ+Vh+8MZcdT7TewYUOeilHI/cAdwhxBiPnAt8LgQohl4Qkp5W5TsqkKNb20RQviAuMX48/p8eH0D8xrSaDSa48UwenshencAHOWeczzPwdHzZE8IBj2cIaV8X0r5TWAU8C1UdIlo8TzKGQN3uSqKdWs0Go3mBCDiKd5SSgt4wS3RYiVwoRCiDKhAeQ1qNBqNZhgR2xgkx4mU0gG+GG87NBqNRhM/EnCWpEaj0Wg0WqA0Go1Gk6BogdJoNBpNQqIFSqPRaDQJSUI6SQwVQoh4m6DRaDTDCUdKOeg5/RGn29DEBiHEj6WUP463HfFCn//wPn/Q38FwPH/dxafRaDSahEQLlEaj0WgSEi1QGo1Go0lItEBpNBqNJiHRAqXRaDSahEQLlEaj0WgSEi1QGo1Go0lItEBpNBqNJiHRAnXisDreBsSZ1fE2IM6sjrcBCcDqeBsQZ1bH24BYoyNJaDQajSYh0S0ojUaj0SQkWqA0Go1Gk5BogdJoNBpNQqIFSqPRaDQJiRYojUaj0SQkwyph4YmGECINeBiYCdQC10kpdwohPgH8FjCA/yel/EcczRxShBAG8AdgBVANXC6lrIyvVUOLEMIE7gOWA03AV4Ee4G9AKvA7KeUv4mdhbHC/h3XAj4FdwONAHvC4lPJbcTQtJgghvgR8DwgCNwN1DLNrQLegEpubgL1SynHAD4FfCCH8wK+As4F5wP+4QnaychGQD4xDnfftcbUmNlwC5AAlwJXA3W65EpgMfE4IMSF+5sWMb6LOF+AXwI9Q34kQQpwTN6tigBBiPPB11MPp+cBvGIbXgBaoxCYX+Lu7vhaYCswHtkgpK6WUzcAa4Kw42RcLVgB/k1I6wFPA0viaExOKcM9ZSrkddZMypJSbpZRBYCXw8bhaOMS4N+iPA88BHtR1/5x7HTwGnBtH82LBRcBfpZTtUsoy4FqG2TUAuosvoZFSfj9s87sokRoLhHdxHUDd0E5WDp2vlLJHCOERQphSSjvOdg0ZUsrf964LIW4EujnyNx8Za7tizO+AW1BdXHlAkytOoM7/7HgZFiMmAbYQYgOQBPyT4XcNaIFKdIQQ+cCfUV0+lwDLgFDYIQ5gxcG0WOHQ/3xDJ7M49eJ22/4aOB11M/6vsJdP6t9cCHEDsEFKKYUQcOQ1cFKfv0sKUIz67ccAm4EXw14fDt+BFqhEQQjxA+Czh+3+J/B54E/AXVJKSwhRhbpweykGXo6NlXGh93y3CCF8qNbESY0QIhV4C3gDOBXV1Xv4b743DqbFiqXAIiHE1ahWwvLDXi8G9sfaqBjTBLwvpewGdrv/+3lhr5/s1wCgBSphkFL+BPhJ+D4hxG+BP0gpfxm2ewNwnxAiDzWGuAD4cswMjT3PA1ehRPgqYFV8zYkJNwFrpJS3uNtVQgivEGIKSrAvAi6Mm3VDjJTy2t51IcRfUZ6sN7mOEW8Cn0N1eZ/MvIY65z8ChajWUvNwuQZ60QKV2MwHLnTdTQGqpJTLhRD/iRqP8gC3SCl74mbh0LMS9R2UARXAZXG2JxbMB5YIIcIdAa4HngTSgDuklNXxMCyOfAclVLko54H342zPkCKlfFEIsQjYBnShHkIDDLNrQEcz12g0Gk1Cot3MNRqNRpOQaIHSaDQaTUKiBUqj0Wg0CYkWKI1Go9EkJFqgNBqNRpOQaDdzzbBCCPEqfWFyPICNmpUPsFxK+UZcDBsAQohy4Aop5foh/Iw5wI1Syv9wty8BbgNOARqBfwHflVLWuuGYuqSUDw2VPZrhiW5BaYYVUsrlUkqvlNIL7EOJktctCSFOQghPlOsz3NQVA+F/UaGWEEJcCTwA/B41aXQOam7OS250/QeAm4UQKdGzWqPRLSiNph9CiFzgXuBjqOCc35BSviaEWIrKS/U6atLsVlTqj9tRwXp/JqX8uXvcb1CRAL6IymP0eSllqVv/Lag0EhnAPah8Xo7bKvqj+9pFQohG4H5UeJt64OdSyt8JIZ5HpZxYI4Q4EyUk90gpH3brX927LYRwgFtRaSqEECIZFTZrEbAd+JKUcvNRvoP5gFdKudsVtp8DP5FS3h92zNdQkcanSyk3CSFeB25AiZhGExV0C0qj6c/fAAmMAn4APBGWb2sasAPVigD4P+BTwBLgdiFElrt/FlCOCvD7T+BhIYQphLgclTZhMUp4LnS3e1kGTJNSbgDuQIncCFSQ4F8KIbKklJ9AtfzOco/7KOa653IQeAZ4AjedByp9ydG4zP1sUCleRqGSBR5CSmlLKVdIKTe5u15zvwuNJmpogdJoXIQQhSix+aGUskNK+RQqivTH3EOagd9LKdvd/Y9LKcuklDuAVvqEqxH4rZu355eoG/wE4AuoEDUVUsp9qASMF4eZcJeUstFd/wHwU1RYmxTUOFn2IE7rdtfeBYBHSnmPe25/AEwhxOyjvKc3xA5hn3ngIz6nFDhjEPZpNMdEd/FpNH2UAOlAh5vmAdRD3D+BFuBgWE4iGyVK4fQ+8FX2HieltIUQDUCBW/9DQogH3eMM4L2w9zeHrS9EpVdodo8xjvMcDn/o7K2zBJgqhAiPBu9BtaYO7+YrREXTBmhwl3n0z0eEEGI5sF9KuRslymlCiEwp5eHfi0YzKHQLSqPp4yBQJ6VM7i0oh4BH3NePN3DlmN4VIUQSSgRq3PovDqt7NGo8qx9uuo0/ASuklHNR41LH+q/ah71W8G/O7b3Dzm0hKjr44XjpE0Tp2t6v+8618VFU8NZeO8KXGk3E6BaURuMipSwXQpQLIW5COSgsRonTKQOsKlsI8UXgIVRX3YdSyjIhxKPALUKId1BZUh9Dtc52H/Z+j1tS3YSVt7n7k9xlCNWiAdWqORX4hxDiLGDKMWxaDxQIIS5EpSy5HPgZMP4ox5bjCo/rwHEbagysDtWqywN+C7wd5uqeg3I1b/+334xGMwB0C0qj6c8VqJt3Iyrt+DVSypoB1rEHNZbVgEq29xl3/73A+yhHiy3AOyjPwH5IKduAbwOvABtRrZiHUYIHynvuSdfb7r9RqTm2oVKkv3Z4fW6d3Shnix+guu9uBS45RqqWdcDMsPf+BeWh919Arft6Gf0TbM4A3j7G96HRDAqdbkOjiSKum/k9Usqp8bZlsAghTgV+JaVcPID33Ikae9Nu5pqooVtQGo2mH1LK94BuEeYp8u8QQviA81HdohpN1NACpdFojsYtwM3HeezVqFZj5xDaoxmG6C4+jUaj0SQkugWl0Wg0moREvF4M+wAAACdJREFUC5RGo9FoEhItUBqNRqNJSLRAaTQajSYh0QKl0Wg0moTk/wMwp6JeUDQvzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_photosynthesis(name='b', ipar=500., co2_ppmv=390)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "Use the code `do_photosynthesis` to explore the limiting factors for the range of vegetation types (PFTs) available here. These are accessed by the optional argument `pft_type`. The list of PFT names understood by this code is\n",
    "\n",
    "* `C3 grass`\n",
    "* `C4 grass`\n",
    "* `Broadleaf tree`\n",
    "* `Needleleaf tree`\n",
    "* `Shrub`\n",
    "\n",
    "You should be interested in the temperature ranges for each PFT (see also tables of parameters in [Clark et al. 2011](http://www.geosci-model-dev.net/4/701/2011/gmd-4-701-2011.html), namely Table 2) and when the different limiting factors kick in for a reasonable range of light conditions and CO2 concentrations. \n",
    "\n",
    "You should generate some appropriate graphs and tables and be prepared to discuss these at the end of the session.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:32.210711Z",
     "start_time": "2018-02-19T19:44:31.209285Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Saved result in test1_shrub.pdf\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<photJules.photosynthesis at 0x7f0d62d8c6d8>"
      ]
     },
     "execution_count": 5,
     "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": [
    "do_photosynthesis(pft_type=\"Shrub\", name=\"shrub\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diurnal variations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now include a model of solar radiation to examine diurnal variations. We use the [PyEphem](http://rhodesmill.org/pyephem/) Python package, which allows us to calculate the position of the sun with respect to an observer on Earth. Thus we need time as well as longitude and latitude.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <strong>DON'T PANIC!!</strong> The next code snippet is given. If you're familiar with Python, you might want to have a look, otherwise, you're just supposed to use it!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:32.420722Z",
     "start_time": "2018-02-19T19:44:32.212816Z"
    }
   },
   "outputs": [],
   "source": [
    "import ephem\n",
    "import itertools\n",
    "\n",
    "import scipy.constants\n",
    "\n",
    "\n",
    "def solar_model(secs,\n",
    "                mins,\n",
    "                hours,\n",
    "                days,\n",
    "                months,\n",
    "                years,\n",
    "                lats,\n",
    "                longs,\n",
    "                julian_offset='2012/1/1'):\n",
    "    \"\"\"A function that calculates the solar zenith angle (sza, in\n",
    "    degrees), the Earth-Sun distance (in AU?), and the instantaneous \n",
    "    downwelling solar radiation in mol(photons) per square meter per\n",
    "    second for a set of time(s) and geographical locations. \"\"\"\n",
    "    solar_constant = 1361.  #W/m2\n",
    "    # energy content of PAR quanta\n",
    "    energy_par = 220.e-3  # MJmol-1\n",
    "    # Define the observer\n",
    "    observer = ephem.Observer()\n",
    "    # Additionally, add a julian date offset\n",
    "    if julian_offset != 0:\n",
    "        julian_offset = ephem.julian_date(julian_offset)\n",
    "    # Ensure we can easily iterate over all inputs\n",
    "    # Even if they're scalars\n",
    "    secs = np.atleast_1d(secs)\n",
    "    mins = np.atleast_1d(mins)\n",
    "    hours = np.atleast_1d(hours)\n",
    "    months = np.atleast_1d(months)\n",
    "    days = np.atleast_1d(days)\n",
    "    years = np.atleast_1d(years)\n",
    "    lats = np.atleast_1d(lats)\n",
    "    longs = np.atleast_1d(longs)\n",
    "\n",
    "    # What we return\n",
    "    julian_day = []\n",
    "    sza = []\n",
    "    earth_sun_distance = []\n",
    "\n",
    "    for second, minute, hour, day, month, year, lati, longi in \\\n",
    "        itertools.product(\n",
    "            secs,mins,hours,days,months,years,lats,longs):\n",
    "        hour = int(hour)\n",
    "        minute = int(minute)\n",
    "        second = int(second)\n",
    "        observer.date = \\\n",
    "            f'{year:04d}/{month:d}/{day:d} {hour:d}:{minute:d}:{second:d}'\n",
    "\n",
    "        observer.lon = f'{longi:f}'\n",
    "        observer.lat = f'{lati:f}'\n",
    "        solar_position = ephem.Sun(observer)\n",
    "        solar_altitude = max([0, solar_position.alt * 180. / np.pi])\n",
    "        this_sza = 90. - solar_altitude\n",
    "        this_distance_earth_sun = solar_position.earth_distance\n",
    "        jd = ephem.julian_date(f'{year:04d}/{month:d}/{day:d}') - julian_offset\n",
    "        jd += hour / 24. + minute / 60. / 24. + second / (3600 * 24)\n",
    "        julian_day.append(jd)\n",
    "        sza.append(this_sza)\n",
    "        earth_sun_distance.append(this_distance_earth_sun)\n",
    "\n",
    "    julian_day = np.array(julian_day)\n",
    "    sza = np.array(sza)\n",
    "    earth_sun_distance = np.array(earth_sun_distance)\n",
    "    iloc = np.argsort(julian_day)\n",
    "    julian_day = julian_day[iloc]\n",
    "    sza = sza[iloc]\n",
    "    earth_sun_distance = earth_sun_distance[iloc]\n",
    "    solar_radiation = solar_constant / (earth_sun_distance**2)\n",
    "    # Express radiation in mol(photons) / (m^2 s)\n",
    "    solar_radiation = solar_radiation / energy_par\n",
    "    return julian_day, sza, earth_sun_distance, solar_radiation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have this functionality, let's see plot how inputs and outputs to the carbon assimilation model change over a day. For example, the first of January 2012, and over London (latitude of 51 degrees).\n",
    "\n",
    "We first call the `solar_model` method with the relevant parameters. This provides us with the time axis in units of Julian days, as well as the solar zenith angle (in degrees), the Earth-Sun distance, as well as the solar radiation in mol(phtons)/m^2s.\n",
    "\n",
    "We can then calculate the amount of incoming PAR. To do this, we assume:\n",
    "* that PAR is around 50% of total downwelling radiation,\n",
    "* that the optical thickness of the atmosphere in the PAR region is 0.2\n",
    "* and that we multiply by all this by $\\cos(sza)$ to project on to a flat surface.\n",
    "\n",
    "The next snippet generates the following plots:\n",
    "\n",
    "1. the cosine of the solar zenith angle as a function of time\n",
    "2. the incoming solar radiation in the PAR region\n",
    "3. the different components that the photosynthesis model calculates\n",
    "4. the C assimilation rate over the course of a day\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "The last statement in the code snippet saves the entire figure into a PDF file that you can easily browse in the Jupyter notebook file navigation browser tab. The filename by default will be `assimilation_20120101.pdf`. You can add code similar to this at the end of each code block to save the whole image as a plot for reference later. E.g., just change the filename, leaving the `.pdf` extension in place.\n",
    "\n",
    "`plt.savefig(\"my_filename.pdf\", dpi=150, bbox_inches=\"tight\")`\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:35.885011Z",
     "start_time": "2018-02-19T19:44:32.423569Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Saved result in test1_c.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate solar position over a day, every 30 mins\n",
    "# for somewhere like London (latitude 51N, Longitude=0)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "latitude = 51.\n",
    "longitude = 0.0\n",
    "jd, sza, distance, solar_radiation = solar_model(0., np.array([0., 30.]),\n",
    "                                                 np.arange(25), 1, 1, 2012,\n",
    "                                                 latitude, longitude)\n",
    "\n",
    "sp = plt.subplot(2, 2, 1)\n",
    "sp.plot(jd, np.cos(np.deg2rad(sza)), '-')\n",
    "sp.set_ylabel(\"$\\mu$\")\n",
    "sp.set_xlabel(\"Fraction of day\")\n",
    "\n",
    "# assume PAR is 50% of downwelling radiation\n",
    "# and atmospheric optical thickness of PAR is 0.2\n",
    "# we multiply by cos(solar zenith) here to project\n",
    "# onto a flat surface (a 'big leaf')\n",
    "\n",
    "tau = 0.2\n",
    "mu = np.cos(np.deg2rad(sza))\n",
    "ipar = solar_radiation * 0.5 * np.exp(\n",
    "    -tau / mu) * mu  # u mol(photons) / (m^2 s)\n",
    "sp = plt.subplot(2, 2, 2)\n",
    "sp.plot(jd, ipar, '-')\n",
    "sp.set_ylabel('$PAR_{inc}\\,\\sim$ $\\mu mol\\, photons/ (m^2 s))$')\n",
    "sp.set_xlabel(\"Fraction of day\")\n",
    "\n",
    "sp = plt.subplot(2, 2, 3)\n",
    "p = do_photosynthesis(\n",
    "    n=len(ipar),\n",
    "    Tc=25.0,\n",
    "    name='c',\n",
    "    ipar=ipar,\n",
    "    co2_ppmv=390,\n",
    "    x=ipar,\n",
    "    xlabel='$PAR_{inc}$ $[\\mu mol\\, m^{-2} s^{-1}]$')\n",
    "\n",
    "sp.set_xlim(0, 550)\n",
    "sp = plt.subplot(2, 2, 4)\n",
    "# now plot Al + Rd over the day\n",
    "sp.plot(jd, (p.Al + p.Rd) * 1.e6, '-')\n",
    "sp.set_ylabel('Assim rate $[\\mu mol\\, m^{-2} s^{-1}]$')\n",
    "sp.set_xlabel(\"Fraction of day\")\n",
    "\n",
    "plt.tight_layout()\n",
    "# Can save the figure to a PDF file\n",
    "\n",
    "plt.savefig(\"assimilation_20120101.pdf\", dpi=150, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is quite an interesting figure! If the only thing that varies over the day is the solar radiation intensity, then at this time and latitude, the (leaf) assimilation is ‘pulse’-like over the day: its is limited by light intensity at high solar zenith angles (early morning and late afternoon), then essentially flat.\n",
    "\n",
    "Normally, the temperature will vary over the day as well, so we could e.g. assume a dependence on solar zenith angle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:36.766807Z",
     "start_time": "2018-02-19T19:44:35.887033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Fraction of day')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# make temperature a function of cos(solar zenith)\n",
    "temp = 35. * mu\n",
    "\n",
    "p = do_photosynthesis(n=len(ipar),Tc=temp, name='c', \\\n",
    "                  ipar=ipar,co2_ppmv=390,x=ipar,xlabel='',plot=None)\n",
    "sp = plt.subplot(2, 1, 1)\n",
    "sp.plot(jd, temp, '-')\n",
    "sp.set_xlabel('Fraction of day')\n",
    "sp.set_ylabel('Temperature $[^{\\circ}C]$')\n",
    "\n",
    "sp = plt.subplot(2, 1, 2)\n",
    "sp.plot(jd, (p.Al + p.Rd) * 1e6, '-')\n",
    "sp.set_ylabel('Assim rate $[\\mu mol\\, m^{-2} s^{-1}]$')\n",
    "sp.set_xlabel('Fraction of day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have dramatically reduced the temperature (remember, the temperature we had before was $25^{\\circ}C$ constantly through the day. It now oscillates between 0 and $9^{\\circ}C$) so we see a general lowering of the assimilation rate, but we also see a change in the shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "Using codes similar to those above, explore diurnal variations in leaf assimilation rate at different latitudes and different times of year, for different PFTs (hint: once you have set this up for one example, it should be easy to run for multiple cases).\n",
    "\n",
    "If possible, you should try to explain what the limiting factors are in each case (hint: plot terms other that `p.Al`, such as `p.Wc`, `p.Wl`, `p.We`).\n",
    "\n",
    "It would be interesting to summarise such results by calculating the total (leaf) assimilation over the day (N.B. in the above examples, $Al$ is sampled every half hour over the day: you want a result in μmol/m2).\n",
    "\n",
    "When performing this experiment, think about other complexities that might arise (e.g. how does the idea of phenology fit into this?)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canopy scale assimilation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the above experimentation was just at the leaf level. We have essentially looked at responses to temperature and light intensity. Of course, in a ‘real’ canopy, there will be varying amounts of leaf area, so we have to consider how to scale up the leaf-level assimilation to the canopy scale.\n",
    "\n",
    "Although there are various ways to scale from leaf-level assimilation to the canopy level, we have only implemented what is perhaps the simplest here. This is based on the assumption that there is an acclimatisation of leaf $N$ throughout the canopy (Sellers et al., 1992) giving:\n",
    "$$\n",
    "V_m = V_{m0} \\overline{f(L)}\n",
    "$$\n",
    "\n",
    "where $\\overline{f(L)}$ is the average fraction of absorbed PAR (as opposed to instantaneous) at leaf area index (LAI) $L$, $V_{m0}$ is the ‘maximum’ (top leaf) assimilation, and $V_m$ is the canopy-scale assimilation.\n",
    "\n",
    "Assuming a homogeneous canopy, the canopy scale PAR use efficiency $\\Pi$ is:\n",
    "\n",
    "$$\n",
    "\\Pi = \\int_{0}^{L} \\overline{f(l)}\\,dl. = \\left[ \\frac{1-e^{-\\overline{k}L}}{\\overline{k}} \\right] = \\frac{\\overline{fAPAR}}{\\overline{k}}\n",
    "$$\n",
    "\n",
    "\n",
    "where $\\overline{fAPAR}$ is the (average) fraction of absorbed PAR by the canopy and $\\overline{k}$ is an effective extinction coefficient:\n",
    "\n",
    "$$\n",
    "\\overline{k} = \\left[ \\frac{G(\\mu)}{\\mu} \\right] {(1-\\omega_l)}^{\\frac{1}{2}}\n",
    "$$\n",
    "with $\\mu$ the cosine of the (time mean) solar zenith angle (a path length term), $G(\\mu)$ the ‘Ross’ or ‘$G$’-function giving the average normalised leaf projection in the direction of the (time mean) incoming radiation, and $\\omega_l$ is the leaf single scattering albedo (unity minus leaf absorption) in the PAR region (see Sellers et al., 1992 for more details).\n",
    "\n",
    "Under these assumptions then, we can calculate canopy scale photosynthesis.\n",
    "\n",
    "$$\n",
    "GPP = A_l \\frac{\\overline{fAPAR}}{\\overline{k}}.\n",
    "$$\n",
    "\n",
    "\n",
    "Suppose we have an amount of leaf carbon of 0.07 $kg\\,C\\,m^{−2}$ and a specific leaf density of 0.025 ($kg\\,C\\,m^{−2}$ per unit of LAI) that is constant throughout the canopy (giving a LAI of 0.07/0.025 = 2.8), and a $G$ gunction of 0.5 (e.g. a spherical leaf angle distribution). We can model this as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:39.709835Z",
     "start_time": "2018-02-19T19:44:36.771061Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mubar = 0.20\n",
      "kbar = 2.23\n",
      "fapar = [0.99808459 0.99808459 0.99808459 ... 0.99808459 0.99808459 0.99808459]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "latitude = 51.\n",
    "longitude = 0.0\n",
    "jd, sza, distance, solar_radiation = solar_model(0., np.array([0., 30.]),\n",
    "                                                 np.arange(25),\n",
    "                                                 np.arange(1, 366), 1, 2012,\n",
    "                                                 latitude, longitude)\n",
    "sp = plt.subplot(2, 3, 1)\n",
    "sp.plot(jd, np.cos(np.deg2rad(sza)), '-', lw=0.2)\n",
    "sp.set_xlabel(\"DoY\")\n",
    "sp.set_ylabel('$\\mu')\n",
    "\n",
    "# assume PAR is 50% of downwelling radiation\n",
    "# and atmospheric optical thickness of PAR is 0.2\n",
    "# we multiply by cos(solar zenith) here to project\n",
    "# onto a flat surface (a 'big leaf')\n",
    "\n",
    "tau = 0.2\n",
    "mu = np.cos(np.deg2rad(sza))\n",
    "temp = 35. * mu\n",
    "ipar = solar_radiation * 0.5 * np.exp(\n",
    "    -tau / mu) * mu  # u mol(photons) / (m^2 s)\n",
    "sp = plt.subplot(2, 3, 2)\n",
    "sp.plot(jd, ipar, '-', lw=0.2)\n",
    "sp.set_ylabel('$PAR_{inc}\\,\\sim$ $\\mu mol\\, photons/ (m^2 s))$')\n",
    "sp.set_xlabel(\"DoY\")\n",
    "\n",
    "sp = plt.subplot(2, 3, 3)\n",
    "sp.plot(jd, temp, '-', lw=0.2)\n",
    "sp.set_xlabel(\"DoY\")\n",
    "sp.set_ylabel(\"Temperature $[^{\\circ}C]\")\n",
    "\n",
    "# run the leaf level model\n",
    "p = do_photosynthesis(\n",
    "    n=len(ipar), Tc=temp, name='c', ipar=ipar, co2_ppmv=390, plot=None)\n",
    "sp = plt.subplot(2, 3, 4)\n",
    "# now plot over days the leaf level response\n",
    "sp.plot(jd, (p.Al + p.Rd) * 1.0e6, '-', lw=0.2)\n",
    "sp.set_xlabel(\"DoY\")\n",
    "sp.set_ylabel(\"Leaf Assim rate\\n $[\\mu mol\\, m^{-2} s^{-1}]$\")\n",
    "\n",
    "# now we want the canopy level response\n",
    "\n",
    "p.Lcarbon = 0.07  # kg C m-2\n",
    "#self.sigmal = 0.025 # kg C m-2 per unit LAI for C3 grass\n",
    "# for Needleleaf tree: 0.10\n",
    "# for Broadleaf tree: 0.0375\n",
    "# for others: 0.05\n",
    "p.LAI = p.Lcarbon / p.sigmal\n",
    "\n",
    "# leaf single scattering albedo\n",
    "p.omega = 0.2\n",
    "\n",
    "p.G = 0.5\n",
    "p.mubar = np.mean(mu)\n",
    "p.kbar = (p.G / p.mubar) * np.sqrt(1 - p.omega)\n",
    "p.fapar = 1 - np.exp(-p.kbar * p.LAI)\n",
    "print(f'mubar = {p.mubar:.2f}')\n",
    "print(f'kbar = {p.kbar:.2f}')\n",
    "print(f'fapar = {p.fapar}')\n",
    "# kg C m-2 s-1: conversion factor from Clark et al. 2011\n",
    "p.GPP = 0.012 * (p.Al + p.Rd) * p.fapar / p.kbar\n",
    "\n",
    "sp = plt.subplot(2, 3, 5)\n",
    "# plot this\n",
    "sp.plot(jd, p.GPP * 1e6, '-', lw=0.2)\n",
    "sp.set_xlabel(\"DoY\")\n",
    "sp.set_ylabel('GPP $[mg\\,C m^{-2}s^{-1}]$')\n",
    "plt.subplots_adjust(wspace=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Net Ecosystem Productivity needs the plant respiration terms to be subtracted from the GPP. This is typically split into mainenance and growth respiration: $R_{pm}$ and $R_{pg}$ respectively. In Jules, $R_{pg}$ is assumed to be a fixed fraction of NPP:\n",
    "$$\n",
    "R_{p} = R_{pm} + R_{pg}\n",
    "$$\n",
    "\n",
    "$$\n",
    "R_{pg} = r_g\\Pi_{G}\n",
    "$$\n",
    "\n",
    "\n",
    "where $\\Pi_{G}$ is the GPP (the canopy scale assimilation). In Jules, $r_g$ is set to 0.25 for all PFTs (Clark et al., 2011). Leaf maintenance respiration in Jules is the (moisture-modified, through a term $\\beta$ that we have not dealt with here) canopy dark respiration (i.e. canopy-scaled). Root and stem respiration are set to depend on the nitrogen concentrations of the root and stem relative to the leaf nitrogen.\n",
    "\n",
    "Since we have not introduced stem and root biomass yet, we will assume here that leaf, root and (respiring) stem biomass ($L$, $R$ and $S$ respectively) we will assume these terms equal for the moment, since we only require their relative amounts:\n",
    "$$\n",
    "R_{pm}=0.012\\cdot R_{dc}\\left[\\beta+\\frac{N_r + N_s}{N_l}\\right]\n",
    "$$\n",
    "where:\n",
    "\n",
    "$N_x$ is the Nitrogen concentration of biomass component $x$ and the factor 0.012 converts units (see Clark et al., 2011).\n",
    "\n",
    "$$\n",
    "N_l = n_m L\n",
    "$$\n",
    "$$\n",
    "N_r = n_m R \\mu_{rl}\n",
    "$$\n",
    "$$\n",
    "N_s = n_m S \\mu_{sl}\n",
    "$$\n",
    "\n",
    "where $\\mu_{xl}$ is the relative Nitrogen concentartion of biomass component $x$ to leaf Nitrogen (assumed 1.0 here). $\\beta=1$ for unstressed conditions. So:\n",
    "$$\n",
    "R_{pm}=0.012\\cdot R_{dc}\\left[\\beta+\\frac{R + S}{L}\\right]\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:40.503215Z",
     "start_time": "2018-02-19T19:44:39.712595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'NPP $[mg\\\\, C\\\\, m^{-2} s^{-1}]$')"
      ]
     },
     "execution_count": 10,
     "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": [
    "# NPP calculation\n",
    "p.rg = 0.25\n",
    "# scale Rd (respiration in the light) up to canopy here\n",
    "p.Rpm = 0.036 * p.Rd * p.fapar / p.kbar\n",
    "# Gpp from above, introducing beta\n",
    "p.PiG = 0.012 * (p.Al - p.beta * p.Rd) * p.fapar / p.kbar\n",
    "# Grow respiration is a fraction of (GPP - maint resp)\n",
    "p.Rpg = p.rg * (p.PiG - p.Rpm)\n",
    "# ensure Rpg is non negative\n",
    "p.Rpg[p.Rpg < 0] = 0.\n",
    "# total respiration\n",
    "p.Rp = p.Rpm + p.Rpg\n",
    "# NPP: calculated as the difference\n",
    "p.Pi = p.PiG - p.Rp\n",
    "\n",
    "plt.plot(jd, p.Pi * 1e6, '-', lw=0.5)\n",
    "plt.xlabel('DoY')\n",
    "plt.ylabel('NPP $[mg\\, C\\, m^{-2} s^{-1}]$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have calcualted NPP and GPP, we can integrate them over the year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-19T19:44:40.527918Z",
     "start_time": "2018-02-19T19:44:40.505403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean NPP = 1.5890 g C m-2 day-1\n",
      "mean GPP = 2.5232 g C m-2 day-1\n",
      "NPP = 581559.2 g C/m2/yr\n",
      "Global NPP (rough est.) = 86.618 GT C yr-1\n"
     ]
    }
   ],
   "source": [
    "print('mean NPP = {:.4f} {:s}'.format(\n",
    "    np.mean(p.Pi) * 24 * 60 * 60 * 1e3, 'g C m-2 day-1'))\n",
    "print('mean GPP = {:.4f} {:s}'.format(\n",
    "    np.mean(p.PiG) * 24 * 60 * 60 * 1e3, 'g C m-2 day-1'))\n",
    "\n",
    "# n seconds in year\n",
    "nsec = 366 * 24 * 60 * 60.\n",
    "integral = np.mean(p.Pi) * nsec * 1e3  # g C m-2 yr-1\n",
    "print(\"NPP = {:.8g} {:s}\".format(integral * 1000, 'g C/m2/yr'))\n",
    "\n",
    "# The total land surface area of the Earth is around 0.292 * 510072000 km^2\n",
    "# http://chartsbin.com/view/wwu\n",
    "# so if this were the mean, we would have\n",
    "global_npp = 0.292 * 510072000 * integral * 1e-9\n",
    "print(\"Global NPP (rough est.) = {:.6G} {:s}\".format(global_npp, 'GT C yr-1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which is certainly an over-estimate by a factor of about 2 because we have assumed high LAI grasslands everywhere on the land surface, but is at least the right order of magnitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have shown here how to introduce LAI (or leaf C) into the scaling up to canopy GPP, and also how respiration terms in a model such as Jules can be calculated, which allows us to estimate canopy NPP.\n",
    "\n",
    "The next parts of a model of this sort include partitioning of the NEP among biomass pools and applying phenological controls.\n",
    "\n",
    "You could assume a simple, fixed proportion of partitioning of assimilates (e.g. $1/3$ to leaf, root and (respiring) stem biomass pools (i.e. each day, if NPP is positive, you add $1/3$ of the NPP (integrated over 24 hours = 24*60*60 seconds) to the leaf carbon pool (`p.Lcarbon`)). This then increases the LAI.\n",
    "\n",
    "Since the canopy scaling model here is very simple, it turns out to be just a scalar to $A_l$, so you can first calculate $A_l$ over each day of the year, then, starting at the begining of the time series, start to accumulate carbon (and produce LAI). This gives you a dynamic LAI model (albeit at this moment one that is not controlled by phenology) that you can then use for each daily sample to scale from leaf to canopy GPP and NPP. The only other term that you need to include is a leaf biomass loss (a leaf shedding term, and usually shedding terms for the other pools of carbon). In JULES, this is achieved by defining a leaf turnover rate $\\gamma_{lm}$ which is temperature controlled but set to 0.25 (per year) for C3 grasses if the temperature is above a threshold (see p.710 of Clark et al.). We could (very simply) assume then a rate of leaf biomass loss of $0.25L/366$ per day (although in JULES, the rate is based on the maximum seasonal leaf biomass, but we use the actual leaf biomass L here).\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "This final exercise then, is to build a dynamic vegetation model, one that ‘grows’ leaf carbon by calculating NPP, allocating a proportion of this to the leaf C pool at the end of each day, then losing a proportion ($0.25L/366$) as litterfall. This should be quite feasible given the codes above, though you might not finish it in this session. If you do complete this, you will find it a very satisfying exercise ... to have created a model of growing plants that links the concepts we have discussed. Although this is a slightly simplified model, it is not greatly less sophisticated than the models currently used in DGVMs, and you can learn a lot by building and trying out a model of this sort.\n",
    "\n",
    "Once you have built the model, demonstrate its application for some given latitude and (ideally) multiple PFTs.\n",
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    "### Experiment 4"
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    "Whilst it is an interesting exercise to build and use models of the sort we have created here, there are many flaws with such models. Think carefully about what insights you have gained into both the strengths and weaknesses of such models. You should read the references below carefully to see what criticisms there are in those papers (e.g. complexities about leaf to canopy scaling). You should also think carefully about the role that ‘fixed’ parameters for each PFT have in such models, bearing in mind what has been learned from plant traits databases such as that of Kattge et al., (2011) that was covered in the lecture on Terrestrial Ecosystem Modelling.\n",
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