src/HoltWinters_TripleExponentialSmoothing.ipynb

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    "## Demonstrating Eponential Smoothing Forecasting, - specifically the Holt-Winters Method.\n",
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    "Following this: https://grisha.org/blog/2016/01/29/triple-exponential-smoothing-forecasting/"
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('6da4a2dc-4603-4003-adf8-2a93b55c9ec6');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipdb\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "import chart_studio.plotly as ply\n",
    "import plotly.tools as plytool\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.offline as plyoff\n",
    "import plotly.subplots as plysub\n",
    "\n",
    "# plotly authentication - not needed for offline mode\n",
    "#plytool.set_credentials_file(username='ahowe42', api_key='')\n",
    "\n",
    "# if using jupyter lab, must have the plotly extension installed: https://github.com/jupyterlab/jupyter-renderers/tree/master/packages/plotly-extension\n",
    "# jupyter labextension install @jupyterlab/plotly-extension\n",
    "# to use plotly offline, need to initialize with a plot; might as well have fun with it\n",
    "plyoff.init_notebook_mode(connected=True)\n",
    "x1 = [1,4,7]; y1 = [7,5,7]\n",
    "x2 = [1,2,3,4,5,6,7]; y2 = [3,2,1,1,1,2,3]\n",
    "plyoff.iplot(go.Figure(data=[go.Scatter({'x':x1, 'y':y1, 'mode':'markers'}),\n",
    "                             go.Scatter({'x':x2, 'y':y2, 'mode':'lines'})],\n",
    "                                        layout=go.Layout(autosize=False,width=500,title=\"Initialization Makes Me Smile<br>(and it's fun to show off a little...)\",\n",
    "                                                         showlegend=False, xaxis={'showgrid':False, 'showticklabels':False},\n",
    "                                                         yaxis={'showgrid':False, 'showticklabels':False})))\n",
    "\n",
    "# if I want to show a df, I want to show *all* of it\n",
    "pd.options.display.max_rows = 999\n",
    "pd.options.display.max_columns = 999"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define model metrics and the function for single, double, and triple exponential smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean error\n",
    "def ME(actl, pred):\n",
    "    return np.mean(actl - pred)\n",
    "\n",
    "# mean absolute error\n",
    "def MAE(actl, pred):\n",
    "    return np.mean(np.abs(actl - pred))\n",
    "\n",
    "# median error\n",
    "def MDE(actl, pred):\n",
    "    return np.quantile(actl - pred, 0.5)\n",
    "\n",
    "# median absolute error\n",
    "def MDAE(actl, pred):\n",
    "    return np.quantile(np.abs(actl - pred), 0.5)\n",
    "\n",
    "# mean absolute percentage error\n",
    "def MAPE(actl, pred):\n",
    "    return 100*np.mean(np.abs(actl - pred)/pred)\n",
    "\n",
    "# sum of squared errors\n",
    "def SSE(actl, pred):\n",
    "    return np.sum((actl - pred)**2)\n",
    "\n",
    "# mean squared error\n",
    "def MSE(actl, pred):\n",
    "    return np.mean((actl - pred)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exponential_smoothing(data, alpha):\n",
    "    '''\n",
    "    Apply exponential smoothing to the levels\n",
    "    '''\n",
    "    smoothed = np.zeros(len(data)+1, dtype=float)\n",
    "    smoothed[0] = data[0]\n",
    "    for i, obs in enumerate(data[1:]):\n",
    "        smoothed[i+1] = alpha*obs + (1-alpha)*smoothed[i]\n",
    "    # forecast 1 step ahead\n",
    "    smoothed[-1] = alpha*smoothed[-2] + (1-alpha)*smoothed[-3]\n",
    "    return smoothed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def double_exponential_smoothing(data, alpha, beta, m):\n",
    "    '''\n",
    "    Apply exponential smoothing to the levels & trends\n",
    "    '''\n",
    "    n = len(data)\n",
    "    # initialize\n",
    "    smoothed = np.zeros(n+m, dtype=float)\n",
    "    smoothed[0] = data[0]\n",
    "    levels = np.zeros(n+m, dtype=float)\n",
    "    trends = np.zeros(n+m, dtype=float)\n",
    "    # iterate over datapoints\n",
    "    for i in range(1, n+m):\n",
    "        if i == 1:                 # initializing\n",
    "            levels[i-1] = data[0]\n",
    "            trends[i-1] = data[1] - data[0]\n",
    "        if i >= n:                 # forecasting\n",
    "            value = smoothed[i-1]\n",
    "        else:                      # somewhere in the middle\n",
    "            value = data[i]\n",
    "        levels[i] = alpha*value + (1-alpha)*(levels[i-1]+trends[i-1])\n",
    "        trends[i] = beta*(levels[i]-levels[i-1]) + (1-beta)*trends[i-1]\n",
    "        smoothed[i] = levels[i-1]+trends[i]\n",
    "    return levels, trends, smoothed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' note the assumption that the data starts at the beginning of a \"season\"\n",
    "and proceeds in exact integer counts of seasons '''\n",
    "def trend_init(data, L):\n",
    "    # compute the avg of trend averages for the first season\n",
    "    return np.sum((data[(L):(2*L)] - data[:L])/L)/L\n",
    "\n",
    "def season_init(data, L):\n",
    "    # compute the average level per season\n",
    "    rngStt = range(0, n, L)\n",
    "    rngStp = range(L, n+1, L)\n",
    "    avgs = [np.mean(data[stt:stp]) for stt, stp in zip(rngStt, rngStp)]\n",
    "    return np.mean([data[stt:stp]-mn for stt, stp, mn in zip(rngStt, rngStp, avgs)], axis=0)\n",
    "\n",
    "def triple_exponential_smoothing(data, alpha, beta, gamma, L, m):\n",
    "    '''\n",
    "    Apply exponential smoothing to the levels, trends, and seasons\n",
    "    '''\n",
    "    n = len(data)\n",
    "    # initialize\n",
    "    smoothed = np.zeros(n+m, dtype=float)\n",
    "    smoothed[0] = data[0]\n",
    "    levels = np.zeros(n+m, dtype=float)\n",
    "    trends = np.zeros(n+m, dtype=float)\n",
    "    seasons = np.zeros(n+m, dtype=float)    \n",
    "    seasonLevs = season_init(data, L)\n",
    "    # iterate over datapoints\n",
    "    for i in range(1, n+m):\n",
    "        thisSeasonLev = seasonLevs[i%L]\n",
    "        if i == 1:                 # initializing\n",
    "            levels[i-1] = data[0]\n",
    "            trends[i-1] = trend_init(data, L)\n",
    "        if i >= n:                 # forecasting\n",
    "            levels[i] = levels[i-1]\n",
    "            trends[i] = trends[i-1]\n",
    "            seasons[i] = thisSeasonLev\n",
    "            smoothed[i] = levels[i]+(i-n+1)*trends[i] + seasons[i]\n",
    "        else:                      # somewhere in the middle\n",
    "            value = data[i]\n",
    "            levels[i] = alpha*(value - thisSeasonLev) + (1-alpha)*(levels[i-1]+trends[i-1])\n",
    "            trends[i] = beta*(levels[i]-levels[i-1]) + (1-beta)*trends[i-1]\n",
    "            seasons[i] = gamma*(value - levels[i]) + (1-gamma)*thisSeasonLev\n",
    "            smoothed[i] = levels[i] + trends[i] + seasons[i]\n",
    "            # update the season levels list\n",
    "            seasonLevs[i%L] = seasons[i]\n",
    "    return levels, trends, seasons, seasonLevs, smoothed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate the data for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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pgfbzpgTaz9ypM1ICDWaGEmgQoIHhRQpkRxiEwYZt+wxEMXcoJdBcnq6iUQJdETL/55RA85lSAu1lGtpslED7cxE9SkQ8fvYKT5+/sn9yP5yREuiHSQ/hlimBBl8LlECDAN0cvm7tKrRp0QiPHj58Z2SUqFExauxkFCteys2I5nanBJrL01U0SqArQub/nBJoPlNKoL1MKYHO4S1XQgm0Nx+UQHt5O3k2SqDB7FACDQLUMfzhgwcoU7wgzp09rYxO9lUKzF7wsyP2BlICdSTUwBBKoAF4OodSAnWC0zmMy0F1gtM5jE8CdYIzMIwSaACejqGUQB3QfHQIJdBgYimBBgHqHJ4rS2pcuXxJGf1l7Dg4dOy8zkjmDqMEmsvTVTRKoCtC5v+cEmg+09AiUgLt5U0JtJe3zEYJtJc5JdBe3k6ejRJoMDuUQIMAdQyXJ4HpUiTAixfPldGRIn2IbXt+5ZNAHSy9fQgl0P4MUgLtZU4JtJc3JdBe3pRA+3lTAu1n7tQZKYEGM0MJNAhQx/DdO7ehcrni+OyzWLh9+5YSYdX6bciUOZuOaOYO4ZNAc3m6ikYJdEXI/J9TAs1nGlpESqC9vCmB9vKmBNrPmxJoP3OnzkgJNJgZSqBBgDqGDx3UD6OGDUSTZq3x6+EDOLh/L2bOW4rCRb/VEc3cIZRAc3m6ikYJdEXI/J9TAs1nSgm0l2los1EC7c8Fl4Pay5wSaC9vJ89GCTSYHUqgQYA6hmdJlwx/3riOdVv2YNuWjRj0w/eoVbchBg0boyOauUMogebydBWNEuiKkPk/pwSaz5QSaC9TSqBzeMuVUALtzQcl0F7eTp6NEmgwO5RAgwDdHP7roQMoVawAEiRMjD2HTuD0yRMonD8bvvgyNg4fv+BmNPO7UwLNZxpaREqgvbxlNkqgvcy5HNRe3nwSaC9vSqD9vCmB9jN36oyUQIOZoQQaBOjm8J5d22Pa5PHo0LkH2nbspozOnDYpbv55A5t3HESKVKndjGhud0qguTxdRaMEuiJk/s8pgeYzDS0iJdBe3pRAe3lTAu3nTQm0n7lTZ6QEGswMJdAgQDeGv3r1CplSJ8adO7eVp4DyNFBal/YtMXvmVHTt2RctWndwI6L5XSmB5jMNLSIl0F7eMhsl0F7mlEB7eVMC7eVNCbSfNyXQfuZOnZESaDAzlECDAN0Yvm3LJtSoXBoZMmbGmo07A0ZuXL8WdWtURNbsObF8zWY3IprflRJoPlNKoL1MXc1GCXRFyNyfUwLN5ekqGiXQFSHzf849geYzDS0iJdBe3k6ejRJoMDuUQIMA3RjeunkjLFk4F336D0XDJs0DRj558hgpk8TGyxcvcPr3PxElalQ3oprblRJoLk9X0fgk0BUh839OCTSfaWgRKYH28qYE2stbZqME2sucEmgvbyfPRgkEMGLSYkydt+adPPXr1ADli+dV/m3Z2p3oOWSq8r9LFM6Bvh3qI1KkiMp/UwLteXk/e/YUab+Kj8eP/8Vvpy8jZszP3pm4ZpUy2Lp5I8ZOnI5yFarYc1HBzEIJtBc9JdBe3jIbJdBe5pRAe3lTAu3lTQm0nzcl0H7mTp2REvifBEqC2jWu9F6eDh49gxETFmPcoDb4NFoURRgD96UE2vPSXrV8KZo2rIW8+QtiwdLV7006fcoE9OjSThFAEUFPNUqgveQpgfbypgTaz5sSaC9zSqC9vCmB9vOmBNrP3KkzUgJdSKBIX8K4XwQ8FQwqhZRAe17a9WtVxvpfVmPEmAmoUr32e5NKdVCpEipLQWVJaJgwYey5sCCzUALtxU4JtJc3JdB+3pRAe5lTAu3lTQm0nzcl0H7mTp2REhjMclB1KejTp8/Ra9g05MiUOkACL165gW6DpmBAl4ZInCA2l4Pa8Mp+cP8+0qVMoMx06sJ1fPTRx8HOWiBXJpw/d0YpDiNFYjzRKIH2UqcE2subEmg/b0qgvcwpgfbypgTaz5sSaD9zp85ICQySGZG8Jp2GY0C3RkibPLEigZVKFUDW9CmUnkEl8J8nL52aW0QIHxYfRAiLFy9f49mL1469TlcXNmP6VLRs3hRlypbHnHkLQ+zeo1tnjB41Au06dEKfvv1dhbXk58L7DYDnXszbEjAWBQ0bNgwiRQyLx09fWTQDwwYlEPmDcMrnyavX8kpns5qAfI6HDQOv/gy3mpGZ8WURSeRI4fGvg3+3m3m/TogVKWI4vHz1Gi9f8TPFjnyEDxcG8gXq0+fe/3vz4w/D24HMZ+egBAaTWnUJaPGC2V0+CXz4+IVjXxwRw4eFfLg+f/naq9/sJYoVwa6d2zFn3iKUKlM2RN7SR/qmTp0Gew786pG8fBAhHN68eaMwZ7OegHxrL1Lyz1PnfhljPQV7Z5AnU0+ev8JrSqAt4OVzXL7s8IU/2GwBZnCSMAiDjz8Mh0eUQIMktQ+Xz/DnL98oIshmPYEI4cIifPgwePLM+yVQVl+x6SdACQxFAqU6KPcE6n9xmTFSDobPkDIhIkf+SFkKGj5CyG94OUw+dbI4ePTwIQ4fv4AvvoxtxiW4FYPLQd3CZbgzl4MaRuh2AFYHdRuZoQFcDmoIn9uDuRzUbWSGB/CICMMI3QrA5aBu4fLpzn4vgX8/eIS1m/ejRvnCSqKDLvdkdVDPvv4n/DQK/b7vphSDkaIwrlqTBjWxesUyDBo2BrXqNnTV3fSfUwJNRxpqQEqgvbxlNkqgvcwpgfbypgTay1tmowTay5wSaC9vJ8/m9xKoFn9Zs2lfQJ5mjO4SsAdQ/pHnBHruJVy0QA6cPHFMORZCjodw1RYvmIM2LRqjcNFvMXPeUlfdTf85JdB0pJRAe5G6nI0S6BKRqR0ogabidBmMEugSkekdKIGmIw01ICXQXt5Ons3vJdBocnhEhFGCIY+/cvkicmVJoxwMLwfEazn24d7du0iXIr6ybPT85VuIEDGidRcYTGRKoK24lc3t0aNEwK37z+yd2I9nowTam3xKoL28KYH28pbZKIH2MqcE2svbybNRAg1mhxJoEGAow4cM7IvRwwehUdMW6P3DEM0TlSyaD0d+PYTZC35GwcLfaB5nRkdKoBkUtcegBGpnZVZPSqBZJLXFoQRq42RWL0qgWSS1x6EEamdlRk9KoBkUfSMGJdBgHimBBgGGMFyWgNapXgF/3riONRt3IkPGzJonGjVsIIYO6oc69RtjwJBRmseZ0ZESaAZF7TEogdpZmdWTEmgWSW1xKIHaOJnVixJoFkntcSiB2lmZ0ZMSaAZF34hBCTSYR0qgQYAhDK9drTw2b1yHBAkTY8+hE25NcvzYbyhWMJdSHVSqhNrZKIF20gaXg9qLW5mNEmgvdEqgvbwpgfbyltkogfYypwTay9vJs1ECDWaHEmgQYAjDE8X+BM+fP0fbjt3QoXMPtydJmzweZH/gll2HkTxFSrfH6x1ACdRLTt84PgnUx83IKEqgEXruj6UEus/MyAhKoBF6+sZSAvVx0zuKEqiXnO+NowQazCkl0CDA/4Y/fPAAvbp3hFT3DNqiRI2KUWMno1jxUpon69CmGebPmYHu3/+AZi3baR5ntCMl0ChB98ZTAt3jZUZvSqAZFLXHoARqZ2VGT0qgGRTdi0EJdI+X0d6UQKMEfWc8JdBgLimBBgEGGb5o/mx079IOj//9V/lJzlx5MXLsJMSLn8CtidatXYUGtaso45esXO/WWCOdKYFG6Lk/lhLoPjOjIyiBRgm6N54S6B4vo70pgUYJuj+eEug+MyMjKIFG6PnWWEqgwXxSAg0CDGZ44rjR8ezpU+UnUhVUqoO62548eYzkCT9Xhm3Yug8pUqV2N4Su/pRAXdh0D6IE6kaneyAlUDc6XQMpgbqw6R5ECdSNTvdASqBudLoGUgJ1YfPJQZRAg2mlBBoEGGS4PMFr0aQunjx5gvxfF8bt239h47b9uiapWqEkdm7fghy58mDpyg26Yrg7iBLoLjFj/SmBxvjpGU0J1ENN/xhKoH52ekZSAvVQMzaGEmiMn7ujKYHuEvPd/pRAg7mlBBoEGIwEdmjzHf6+dw+7DhzDzT9vIHWa9IgaLZrbE02eMBa9e3RChAgRcfnP+26P1zOAEqiHmv4xlED97PSOpATqJadvHCVQHze9oyiBesnpH0cJ1M9Oz0hKoB5qvjmGEmgwr5RAgwCDDL929QpyZEqJKFGi4sylm7qC7929A8MH98fTZ09x5PBBJUbO3HkBhEHqNGnRvlMPXVKp5WIogVoomdeHEmgeS62RKIFaSZnTjxJoDketUSiBWkmZ148SaB5LLZEogVoo+UcfSqDBPFMCDQIMMnzlz0vwXaPaKFz0W8yct1R3cBHBerUq49HDhwExKlWtib79h1omgDIRJVB3ynQNpATqwmZoECXQED63B1MC3UZmaAAl0BA+XYMpgbqw6R5ECdSNzucGUgINppQSaBBgkOG9unXA1Enj0Lnb92jVrrOh4FJptG3LJkqMTz+NjhPn/zAUT8tgSqAWSub1oQSax1JrJEqgVlLm9KMEmsNRaxSnSqAco6RnW4TW+/ZkP0qgvfQpgfbydvJslECD2aEEGgQYZHiJInnx25HDWPTzWuTOW8BQ8Pq1KuPYb7/izz9vKHE2bNuH1GnSGYrpajAl0BUhc39OCTSXp5ZolEAtlMzrQwk0j6WWSE6VwOFD+qN9p+5absHr+lAC7U0ZJdBe3k6ejRJoMDuUQIMAAw1/+eIFksSPidevXuH81duIFOlD3cHlW9PJE8cid978qFCqKNKkTY8GjZuhcrVaumNqGUgJ1ELJvD6UQPNYao1ECdRKypx+lEBzOGqN4kQJPHniGCqW+Qanf/9T6214VT9KoL3pogTay9vJs1ECDWaHEmgQYKDhB/fvRdkShZAydRps2n7AlMCPH/+LZPE/Q+TIHyliaXWjBFpN+N34lEB7ectslEB7mVMC7eXtFAkU8Vu/dpVy83t274Tsc5cvMePFi6/8W8MmLXxmeSgl0N7XOCXQXt5Ono0SaDA7lECDAAMNnzhuNPr26opadRti0LAxpgXOki4Z/rxxHQePnkPsOHFNixtcIEqgpXjfC04JtJc3JdB+3pRAe5k7RQLlrmVfe6/uHd8pcBY3XnxMm73I8q0NdlKnBNpJG6AE2svbybNRAg1mhxJoEGCg4Y3qVsPa1Ssw8seJpi7brFmlDLZu3ohZ85ehUJFi5l1wMJEogZbipQTaizfY2fgk0N4kUALt5e0kCZQ7X7d2FRrUrhIAQZaE+lqBGEqgva9xSqC9vJ08GyXQYHYogQYBBhqeNnk83Lt7Fzv2/YYkSb8yLfAPvbtj/NiR6NqzL1q07mBa3OACUQItxUsJtBcvJdABvCmB9ibBaRL4ffeOmDZ5PF6/fq2AmDprIYoVL2UvFItnowRaDDhIeEqgvbydPBsl0GB2KIEGAf43/Pof15AtQ3JDh8SHdCVLFs1D62YNUa5CFYydON2cCw4hCiXQUryUQHvxUgIdwJsSaG8SnCaBUuV66+YNeP78OVKkSoM8efOjT/+h9kKxeDZKoMWAKYH2Avai2SiBBpNFCTQI8L/h6iHxBQsXxewFy80J+l8U2WBftEAOpEiZCpt3HjI1dtBglEBL8VIC7cVLCXQAb0qgvUlwmgTeuXMb6VMkUCDIfsD9R87YC8SG2SiBNkAONAWfBNrL28mzUQINZocSaBDgf8N79+iEyRPGomOXnmjToas5Qf+L8uL5cySOG135rys3HyJs2LCmxg8cjBJoGdpgA7MwjL28ZTbuCbSXOSXQXt5Ok8Dffj2EEkXzBUC4+tcjhAsXzl4oFs9GCbQYcJDwlEB7eTt5NkqgwexQAg0C/G94yW/y48jhg5i/ZBXyFShkTtBAUQrkyoTz585g846DSJEqtenx1YCUQMvQUgLtRRvibJRAexNBCbSXt9MkcNXypWja8P/n2+46cAyJEie1F4rFs1ECLQZMCbQXsBfNRgk0mCxKoIPgJzgAACAASURBVEGAANRD4uX/nrl0U9kXaHaTX6Lyy1T2BMreQKsaJdAqssHH5ZNAe3nLbJRAe5lTAu3l7TQJ/GnMcAzo2zMAwpyFK/B1oSL2QrF4Nl+XQNmSkjpNOospag/PJ4HaWfl6T0qgwQxTAg0CBPDroQMoVayA8oROntRZ0caMGIzBA/qgZZuO6NKjjxVTKDEpgZahDTYwJdBe3pRA+3lTAu1l7jQJ7N65LWZMnRgAQc7QlbN0fan5sgSKAMpWl1FjJzkmZZRAx6TC4xdCCTSYAkqgQYAAJo0fgz49u6B6rboYOnKc8YDBRFj/y2pIlbWChb/B7AU/WzIHJdAyrCEGpgTaz5xPAu1lTgm0l7fTJLBW1bLYsmkDcubOh727d6BJs9bo1XegvVAsns2XJVCO+Fi0YA7kfEenNEqgUzLh+eugBBrMASXQIEAAjevXwJqVP2P46PGoWqOO8YDBRLhy+SJyZUmD2HHi4uDRc5bMQQm0DCsl0H60Ic5ICbQ3GZRAe3k7TQLz58yIC+fPolPXXhgysK9yRqCcFehLzdckUGS9Xq3KePTw4XtpatikuceP+KAE+tK7x9i9UAKN8QMl0CBAAJnTJsXNP29g+94jSJosufGAIURIGi8mnjx5jDMXbyJKVPP3HVICLUsdJdB+tJRAhzCnBNqbCKdJYJyYkRUAK9ZuQZniBZEydRps2n7AXigWz+ZrEii4Hj54gDYtGkFWIUn78MPISk0CkXhPN0qgpzPgnPkpgQZzQQk0BvDPG9eRJV0ySw6JD3plJYvmw5FfD2HZ6o3IniO3sQsPYTT3BFqClRJoL9ZQZ+OTQHuTQQm0l7eTJPD27VvIkDIhYsSIiYPHziFxnOj44INIuHj9nr1QLJ7NFyVQkPXq1gFTJ73d4hIxYkRcunHfYpLawlMCtXHyh16UQINZpgQaA7h6xTI0aVATBQoWxtxFK40FczG6Q5tmmD9nBgYMGYU69RtbMhcl0BKslEB7sVICHcSbEmhvMpwkgYcP7kfpb79G+gyZsHbTLqRLHh93797BkVOXECvW5/aCsXA2X5XATGmS4K+bb/cCytmOk6bP45NAk19HsWN8aHJE/wpHCTSYb0qgMYC9e3bG5PE/ol2n7mjfqbuxYC5GT5n4E2STtlRWkwprVjRKoBVUQ47JwjD28pbZ+CTQXuaUQHt5O0kCly9bhOaN66JE6XKYNG2uUkVbqmkvX7MZWbPntBeMhbP5ogReu3pFOZaqf98eAeQGjxiLmrXrW0hSW2g+CdTGyR96UQINZpkSaAyg7HE4dGAf5i1eifxfFzYWzMVo2axdsUwxZSmoLAm1olECraBKCbSXauizUQLtzQYl0F7eTpLAsaOHYWC/Xmjeqj269eqHVt81wNLF8zFm/FRUqFTNXjAWzuaLEii4hg7qh1HD/l/JtWOXnmjToauFJLWFpgRq4+QPvSiBBrNMCdQP0I5D4gNf3YP795EqaWxEjvwRzl+9rf/CQxlJCbQEa4hB+STQXt4yGyXQXuaUQHt5O0kCO7VtjrmzpwdsYVClwo6VM3ZS91UJlGOppDBM2fKVIU91M2bOitXrt9uJNti5KIEeT4FjLoASaDAVlED9ANVD4pN9lQLb9vyqP5AbI2WTvWy233/kDOLGi+/GSG1dKYHaOJnVixJoFkntcSiB2lmZ0ZMSaAZF7TGcJIHVKpbCjm2blbNt5YzbxQvmoE2LxspTQHka6CvNVyUwZ+ZUuHrlMtZt2YNiBXMp6Tp+9hqix4jh0dRRAj2K31GTUwINpoMSqB+g7AWUPYFyNqCcEWhHq1qhJHZu34KZ85aicNFvTZ+SEmg60lADUgLt5S2zUQLtZU4JtJe3kyQwT7Z0uHTxArbuPoyvkqfEgX17UK5kYWTJlkM5MsJXmi9K4OPH/yJZ/M+UFF2/8xgVS3+DvXt2YtTYSahUtaZHU0cJ9Ch+R01OCTSYDkqgfoBNG9ZSNk4PGfkTatSqpz+QGyPVQjRdevRByzYd3RiprSslUBsns3pRAs0iqT0OJVA7KzN6UgLNoKg9hpMkMP7nUfDq1SvlSAg5GuLWXzeRMXVifPZZLPx2+rL2m3J4T1+UQLWya8ZMWbB6ww5M+GkU+n3fDSXLlMfEqXM8mhFKoEfxO2pySqDBdFAC9QPMmv4r3Lj+BzbvPIQUKVPpD+TGyIXzZqFdq6YoU64Sxk2e6cZIbV0pgdo4mdWLEmgWSe1xKIHaWZnRkxJoBkXtMZwigTf/vIHMaZMiZszPcPTMlYAbkLMCnz17GiCG2u/MuT19UQLnzJyKzu1bolrNuhg2ahzOnzuDArkyKYfGn738l3JkhKcaJdBT5J03LyXQYE4ogfoA3rt7F2mTx1MOiT/1+w2EDRtWXyA3Rx397VcUL5wHyVOkxJZdh90c7bo7JdA1IzN7UALNpKktFiVQGyezelECzSKpLY5TJPDg/r0oW6IQ1CdJ6tUXzJMZZ8+cxqbtB5AydRptN+XwXr4ogd07t8WMqRPRp/9QNGzSXMlArsypceXKJSxZsQ45c+fzWFYogR5D77iJKYEGU0IJ1Adwzcqf0bh+DeTNXxALlq7WF0THqBfPnyNh7E8U6bz4xz1EiBhRR5SQh1ACTcXpMhgl0CUi0ztQAk1HGmpASqC9vJ0igXIUhBwJUapsBUyYMjsAQr2albBh3RpMnbXQEQePm5EdX5TACqWKYt/eXVj081rkzltAwdSza3tMmzweTZq1Rq++/z86wgyG7sSgBLpDy7f7UgIN5pcSqA9g315dMXHcaOXMHDk7x86mbra34ptUSqC2TC6aPxvFipdG1GjRtA0IoRcl0BA+XYMpgbqw6R5ECdSNTtdAp0jg6OGDMGRg34AzAtWbUfe1i0SITPhC80UJlKIwUhzm1IUbiPbJJ0qatm/dhOqVSiNpsuTYvveIx1JHCfQYesdNTAk0mBJKoD6A6iHxaulrfVH0jWpUtxrWrl6BHydMQ/mKVfUFCWEUJVAbTjk/qVjxUqhcrZa2AZRAQ5zMHEwJNJOm61iUQNeMzOzhFAns0KYZ5s+ZgUHDxqBW3YYBtzh9ygT06NIOtes1wsCho828dY/F8jUJ/PPGdWRJlwyxYn2OI6cuBXCVlUgpk8TGkyePcfj4BXzxZWyPMKcEegS7IyelBBpMCyXQfYB2HxIf9AqHD+mPEUP6o1nLduj+/Q/u30AoIyiBIcPZu3sHECaM0kHKZcueiPaduyv/HTVqNKROk87tXPBJoNvIDA+gBBpG6FYASqBbuAx3dooEVi5XHLt3bsOchSvwdaEiAfe1ZdMG1KpaFgUKFsbcRSsN368TAviaBKo5ylegEOYvWfUO4ga1q2Dd2lXoN3A46jf6ziP4KYEewe7ISSmBBtNCCXQf4G+/HkKJovk8tiRCngLK00A5fFeeRJrZKIEh0zx54hjkCeAf166+0ylnrryYNnuRrqWhlEAzX73aYlECtXEyqxcl0CyS2uI4RQJzZUmDK5cvYse+35Ak6VcBFy/nBsqWhkSJk2LXgWPabsrhvXxNAsf9OAL9+/QIdu/fgrkz0b71d4rYi+B7olECPUHdmXNSAgPl5enT5+g1bJryL3071EekSG+LhixbuxM9h0xV/neJwjne+Rkl0P0X9tRJ49CrWwdUqV4bI8ZMcD+AwRHqL9HYceLi4NFzBqO9O5wSGDrOhw8eIE/2tLh7547SMU++Ali4bK3uHFACdaPTPZASqBudroGUQF3YdA9yigTGiRlZuQc5aDxoC+1num/cgwN9TQJbN2uIJYvmBXsw/K1bfyFjqkQK7fNXbyNy5I9sJ08JtB25YyekBP6XGlUA12za947oHTx6BiMmLMa4QW3wabQoGDFpsTKiXeNKyv+lBLr/2i5XsjAO7NuDwcN/RM06DdwPYHDE69evkThudMj6fLM/hCmBrpOT8MtP8OLFc6Wj/AI8/fsNhI8QwfXAYHpQAnVhMzTo1G/7kD5LLrx4+dpQHA7WRsBMCZRlaLIXly1kAk6QwOt/XEO2DMnf21OmXrX8TPrsPXwK8RMk9Pp0+poEFs6fDadPnsC6LXuQNl2G9/JTrGAuHD/2G6bPWYyixUrYnj9KoO3IHTshJfC/1IjcJYz7hfJf+349GfC0T/338sXzKj8LKoWUQPdf2wm+iIqXL1969JwjOStQzgxctnojsufI7f5NhDCCEhg6SvkjtFmjOsphx7E+/wK3/rqJ9p17oF3HbrpyQAnUhc3QoG8L5sT0OYvwRex4huJwsDYCZkpg9owpsP/IGW0T+2kvJ0jg/n27Ub5kEWTJlgMr1m55LxNVypfArh1b3zl+wJvT5WsS6OpJrVqXoFadBhg0/EfbU0cJtB25YyekBALvPN2TpZ+qBErWZHlojkypoUrgxSs30G3QFAzo0hCJE8Tmk0A3X9p7du1ApbLFECFCRFy8fs+2Q+KDXmabFo2xeMEc9B88EnUbNHHzLkLuTgkMHeXePbtQsXRRJEiQCKN+mgx5Khwp0oc4cPQsYsSI6XYeKIFuIzM04NrVK8iRKSX6DRyK+o3eHoDMZi0BsyRQCjNVLFPMp86Xs4K8EyRwycK5aN28EcqWr4yfJs147zY7tm2GebNnYPCIsahZu74VGGyN6UsSeOb0KRTKm0XZxyn7OYNral0EqQ4qVULtbpRAu4k7dz6/l0CRvst/3AxY3hmcBFYqVQBZ06dQshhUAu88eObY7EaKGA4ffxgeT5+/wj9PXnrsOh88uI+W3zXCL2vfrZIlF5Qrd17MmrcY0aK9PUfHrjZu7Ch836ML6tRriGEjx5o2rfzB9vrNGzx59sq0mL4UaOigHzBk0A9o1LQ5Bgwajkb1a2H5ssWoXLU6fprwdj+uOy1cuLCIGjkc/n70wp1h7Osmge5dOmDShPffJ1LRdez4yUiTNr2bEdldKwH5HA8XLgz+1fEZ7sTPXq337al+YcKEwadRIuDew7dL1j3Rhg3uj8ED+6FNu07o3qvve5cweuRQ/NCnJ1q2bo9effp74hJNnVO+PH364hWev/D+JebLlixEk4Z1ULpsBUydMTdYTm/evEGKpHFx7+5dbNt1QFdlbCMJ+CBCWESMEA6PHnv/782Y0T4wgsLvx/q9BMpyz6nz1rz3QpACMN1a1sSAH+eE+iTwuYP3xYQNGwbhw4bB69dv8PL1G4+/2GfNmolGDf7/reWw4SPQspVnDrvdsnkzvi1WFLly5cbW7TtMYxMu7NsjEF45gLdpN2VioLy5c+HAgf34Zd0GFCxUCDeuX0eqlMnx5MkT7NqzF1mzZnNrNqEtTwNfvPL+Px7cunEPdN6xfTtKlyqh5Epardq1MWXqdA9ciX9NKZ8p8jo38hn+45jR6NC+XQA4T372Oj17wjpC+LDw5O/2po0bYfr0aRg/cRLq139/3/yypUtRrWplVKhYEfPmL3Q6UpfXFz6c/J0C5QtUb289e3THkMGD0KdvP3TpGvI2hyaNGmLGjOn4of8AdOzU2dbblqfdYcMCL195P++I4cPays7XJvN7CQya0MBPAqU6KPcEmveSlyMCihbIERBw6qyFHitSIHvRMqZOrBQmkeIwWposhYsXP0GoXbkcNGQ88q1nuhTxleWfUgwmQsS31XdHDx+EIQP7IlWatNi4bb+WVAT04XJQt3AZ6iyVXVMm+TIghhxgLQdZs1lLwIzloE767LWWlvHoTlgOKst2ZfnuvMUrkf/rwu/d1LGjR/BtodzKE/j1W/cav2kPR/Cl5aC1q5XH5o3rXC67XrNqORrXqx7ivk8rU8LloFbS9a7YlMAg+QoqgawOat4L+vvuHTF7xhQ8e/YMlavVgiyJGDV2knkTuBkpbfJ4ynKMA7+dRZy4rotcyD7C9p26hyqClMCQkyB7MIXhtyVKY8rMBe90zJk5Fa5euYyRP05UXhtaGyVQKynj/caPHYkfencPCPRxlCg4e+kv44EZIVQCZkigfPYunDcbjx49VOaqVLWmRz97nZxyJ0ig+nm4++BxJEyU5D1cjx4+RIrEXyBKlKg4c+mmk3FqujZfkkApviRn4e45fFLZ+x5S++efR0ie8HPlxyfPX8cnn36qiZUZnSiBZlD0jRiUQBcSKD/mOYHmvNhXrViKpg1qIUrUqDhz8abyTWfO3PnMCa4jivpt64y5S1Dkm+IuI8gv3fadeqBR0xYh9qUEhoyxcf0aWLPyZwwdOQ7Va9V9p6NUDW1Qu4pSHEZ+eX78cRSX+ZAOlEBNmEzp1L51UyyYOwt16tTF0qVLIX/ErNm4AxkyZjElPoMET8AMCdy1cxuqVyyFV6/e7lWWioRSmZDtfQKelkDJUfzP337+Xf3rEcKFCxdsmlIni4P7f/+NE+f+wKfRo3t1Kn1FAh8//hfJ4n+meYWRWuX1xwnTUL5iVdtySAm0DbXjJ6IEGkwRj4jQDnDDujWoV7MScuctoJS29nSTA+vl4PouPfqgZZuOwV6OlHLeu3snHtz/G6dOHkfUaNGQOk16RI0aVTnaQApjBG6UwJCz+lWCWPj3339w5ORF5XiIoK1axVLYsW0zmjRrjV59B2p6eVACNWEypVO+HBnw+4Vz2LZ9BxYt+RnjfhyJGrXqYcjIn0yJzyDWSeC5s6fxde7MARNIziR3bM6TQLUCr6vKkSWK5MVvRw5jzcadyJDx/7n1xpz6igQe3L8XZUsUQqYs2bBq3TaXqZg8YSx69+iEchWqYOxE+/ZXUwJdpsZvOlACDaaaEqgd4Igh/SFS9V2LtujR2/MVzebPmYEObZqFWIZbvTP1TB/1v2Xv2qixk4Ot6EUJDP71oJanT5c+I37ZvDvYTiIYIhrSdu4/isRJkrl8cVECXSIypYMcbCwHHMeNFx8XL17CyXOXkDF1UnzwQSSlxLm3P4kwBZJFQcx4Erh29Qo0qlst4Apr12uEgUNHW3TF3h3W008C1c/KrNlzYvmazSHCbN64LpYvW4Rxk2eiTLlKXg3dVyRQtrt06dBK85djV65cQq7MqZUvl0///qdtOaQE2oba8RNRAg2miBKoHWCd6hWwacMvyrlHcv6Rp9uRwwdR8pv8SJ4iJbbsOhzi5cg3dfKNndpC27dGCQweY99eXTFx3Gi06dAVHbv0DJF1n15dMGncGHxdqAjmLFwBKUYivyBDapRAe95Fal7kifnwoYNw/98XaFSvFlb8vBidu32PVu3srW5nz107YxYzJFAtviSfdWfPnEbGzFmxev12Z9ygw67C0xK4aP5stG3ZRFkeKMsEQ2qDB/TBmBGD0bl7b7Rq28lhFN27HF+RwK4dW2PW9Mn4YdAI1GvYVBMEdYXF3EUrUKBgEU1jjHaiBBol6DvjKYEGc0kJ1A4wc9qkuPnnDc1PebRH1tdTlibKEkVp1+88DjaIulxDfhgpUiQ8ffoUuXLnw+IV64LtTwkMPhf5c2bEhfNnsWbDDmTIFPIeMtlnlidrWty+fQs/DBqOjz76ONRCMZRAfa99d0dlTJUIt279hc07DyFvjoyKBB48eBCyJO2zz2Lht9OX3Q3J/hoJmCGBLZvWx7IlC5QVGFLcR57gXrx+T+MV+Fc3T0uguvJEvliRL1hCaqosVqtZF8NGjfPqJPmKBJYvWQT79+3G0lUbkCNnHk05Ub8glZUvsgLGjkYJtIOyd8xBCTSYJ0qgNoB37txG+hQJ8OGHkXHh2h1tg2zopVZh27h9P1KlTvvOjL+sWYmGdd5u1m7VpiP++OOa8odUiVLlMGl68IfAUgLfT5q65CV6jBg4duYq5DDm0NrCebPQrlVTRIkaDTlz5cH0OYtD7E4JtP5NIvs0Zb+m+sT8s2gfKBL44uVrlCleEIcO7HO7qqv1V+07M5ghgd98nRMnjh/Fz6s3oVWzBpB9Z5u2H0DK1Gl8B5RJd+JpCWzdvBGWLJwbbAGtwLeoLhsV2RDp8ObmKxIoRWGkOMypCzcQ7ZNPNKVk985tqFzubWE6WRIa2soXTQE1dKIEaoDkJ10ogQYTTQnUBnDLpvWoVbUcsufIjWWrN2obZEMvKVQjBWtGj5uCipWrB8z466EDKF+6KF48fx5QTn3G1Ino3rktKlSqhjHjpwZ7dZTA97FI8R0pwlOxSg2M/mlyqFmVP2ykEM+sGVNw+9bb4wfkWA5pqdKke+9cSUqg9W8SOdZDjvdQl50FlkD1rKukyZJj+94j1l+MH85ghgQmjP2J8ll2/Ow1dG7fArJH0N3jWPwFvaclUH2atHDZGuTJ93WI2G9c/wNZ038FVwVkvCFvviCBciyEHA8hRc+k+JmrJlsdJk8cqxyVNXr4YLx+/QqlylTAV8lTKF+AVqlWyzIhpAS6yo7//JwSaDDXlEBtAMeMHILB/Xsrxyv0/mGItkE29Bo6qB9GDRuI5q3ao1uvfsqMF38/jxJF8yn70dS9afLv8k26fKOeKHFS7DpwjBKoMT81KpfGti2bMHHqHJQsU97lqMBLcNXOcq5Z3/5D3/ulSAl0idNQh2fPniJd8gTKcRD7fj2tnJEZWAIleI5MKZUnS1LxVyr/splLwKgEqtUm5RwyOY9M/Sxu0LgZ+g4YZu7F+kA0T0tgtgzJcf2Pa9h7+BTiJ0gYKtE4MSMrPw9pO4O3pMMXJFAOiJeD4gsULIy5i1ZqQn/yxDHUr1VZOVdQbaEVntMUVEMnSqAGSH7ShRJoMNGUQG0ApTKdfPsc9ImbttHW9Vr58xJ816g2ChYuitkLliuHx5comlc5uDxN2vT4ec0m5cwftSWJGwNPnz4J8WwmPgl8N1fquUnyr3KosRxurKXJ4dZTJr49eiBtugxYt2VPsMMogVpo6u+zesUyNGlQE1my5cCKtVuUQEElUH3SW7jot5g5b6n+yTgyWAJGJXDLpg2oVbWsskdJlg3KFzLyxUy2HLmU5aFs7xLwtAS6I3bqXutte35Fsq9SeG0qfUECx44ehoH9eqFp8zbo2WeA5lyICBYtkCOgv/plm+YAOjpSAnVA89EhlECDiaUEagMoyyTk2y6pwil7i5zSzp87gwK5MilPmKTUvSzFkXL4Ugp/9YYdStGLwK1CqaLYt3eX8seu/NEbtFEC3yWiHgKfM1deLFm5XnPaixTIjls3b0L2kkoLaa8EJVAzUl0d1eXSgavdBZVA+VIkfYqEyhmQuw+dQIIEiXTNxUHBEzAqgVKVV4pP1KxdH4NHjMWjhw+RIvEXjtuf7ZT8e1ICr1y+iFxZ0uDL2HFw6Nh5l0hki4VstQjp95HLAA7p4AsSqB7ZIVseZOuD1iYrX4YN6ot//vlH2S8/YsyEUIuhaY0bWj9KoBkUfSMGJdBgHimBrgHe//tvpE4Wx7F/dKjfvGbPmQf79+5SNnRL+fTgzqkb9MP3+HHU0BCPOqAEvvt66NyuBebMmqYc/i6HwGtpytLCBXMQPlw4DBnYF4WKFEP1WvXe2w8osSiBWojq6yPLoVMm+VIZfOzsVcSIEVP530ElUP5NKk6OHzsSPH9OH+vQRhmVwI5tm2He7BnK0k9ZAipN9pLJnjKt53Gaf1fOjehJCVSLvWj90kxdMeHOkQROJO8LElgoX1acOXUSwRWZC4255LBhkxZKka2/bv6Jug2aoP/gkZamiRJoKV6vCk4JNJguSqBrgDu3b0HVCiWROWt2rPxlq+sBNvSQP3BPnny7r++7hrUDipCEjxAB/QYMU84xDK5Kl/pkSzbsy8b9oI0S+C6RtMnjKUts9SxXWrPyZzSuXwNFvimOGXOXBPuqoARa92aZM3MqOrdv+d4el+AkUI5+EbGQowfkCYbsP2Mzh4BRCVQruMo+JdmvJK1ujYrYuH4txk6cjnIVqphzoT4SxZMSOH/ODHRo00wpUiZbJ1w1WTIvEuHt+zu9XQKl6FLiuNGVdF384x4iRIzoKnXv/bzVdw2wdPF8RQBFBK1slEAr6XpXbEqgwXxRAl0DHPfjCPTv00M5PFW+sXRKk2UYw4f8oCyPUpscYTFgyMgQl2OoR118/HEUnL38tnpl4EYJ/D8Nda+DFBORfQ7uNnWpbmjnJ1EC3aWqvb9apTDo8qbgJFCiqsuhuvbsixatO2ifiD1DJWBUAlMljY0H9+8rci7LDKWNHDoAwwb/4Pb+JX9IlSclUD0Avm3HbujQuYdL3GoxEm/fj+vtEihPAOVJ4FfJU2Lr7sMu8xZcB/VopFJlK2DClNm6YmgdRAnUSsr3+1ECDeaYEugaYLNGdbDi58XKWvcq1Wu7HmBjD3X5jTrlkhXrkDN3vlCvQD1bUA7OTpEyFSUwBFqjhw9SlnMa+ZbaVZEESqA1bxa1/Lw82Tt+7io++ujjgIlCksCjv/2K4oXzaC6Rbs2V+15UIxKofmkl+Tt35VYAnE0bfkGd6hWUaq5S1ZXt/wQ8KYEtm9ZXzqLV+rvywvmzkOIwUhRGVlt4a/N2CZScSe5Kl6uI8ZNn6UqDFKOTvy1ixvwMR89c0RVD6yBKoFZSvt+PEmgwx5RA1wBzZ02Ly5d+d3utvOvIxnssmj8bbVs2gezB2Ltnp6azs9QnHkNG/oQatepRAkNIQ8lv8uPI4YOYs3CFctSGnpY3e3rlyI7NOw4iRarU74WgBOqh6nqMWulOjvSQoz0Ct5AkUPqUK1kYB/btQefu36NcharKkRJsxggYkUD1S670GTJh7aZdARciy3czp02KKFGj4szFm8Yu0MdGe1ICy5YohIP792Lx8l+QK09+l2TlCJfEcaIjXLhwuPrXI5f9ndrB2yVQVjrJiqdOXXuhdfsuujFnTJUIt279heC+YNYdNJiBlEAzaXp3LEqgwfxRAkMH+OTJYySNFxOy1+7S9b8RNmxYg8TNHS4HYRcrXkr5/7LfT/7/qLGTQp1k2uTx6Nm1ParWqIPho8dTAoOhJfsA06WIj0iRPsTp32/o2iMhYeUMpfW/rFaWx8gymaCNVeGxHwAAIABJREFUEmju+0GN9nXuzDh39jSmzlr4XkGe0CTwlzUr0bBOVUSPERM9e/e3vMqdNXfvrKhGJHDW9Mno2rF1sHvMpEKoLIXfc/gkK7oGSrknJTBTmiRKcRAtZwSqlywyL1IfeLmvs17Brq/G2yVQjmCRo1imz1mMosVKuL7hEHq0bt4ISxbOVbbNyPYZqxol0Cqy3heXEmgwZ5TA0AHK07WKpb9BhoyZsWbjToO0zR8uBWICF4AJ+t/BzaguewtuCQ73BL4lJr/I5BdaaEVdtGRTzl2Sp1LtO3VHu07dKYFaoBnsc/rkCRTOn01ZAnrqwnXlC5zALTQJfP36NXJnTaOcs5ktey7lnE02YwSMSKB8WSVfWnXp0Qct23R850JqVimDrZs3YtK0uShRupyxi/Sh0Z6SwFevXiH+51EUkvJUT57uaWnqsUVyBI+saPHG5u0SqIq4O/IeXJ7UlUklSpXFpOnzLEslJdAytF4XmBJoMGWUwNABTh7/I3r37IxadRpg0PAfDdJ2xvCXL14geaIvlEPjgx6ATgl8m6OmDWth1fKlyrlkcj6Z3qbKZEh7LfgkUC/ZkMepxz1Uq1kXw0aNe69jSBIoT9HbtGj0TqElGSxnbsrTdVd7bc2/E9+IaEQCpSqzVGcO7omuWoRE5FAkke0tAU9J4KWLF5AnWzrl/bL/yBnN6WjXqimkqIjWfYSaA9vY0ZslUIouSfGlyJE/wvmrb8+11dvkLGU5U1mOqTp14YbeMC7HUQJdIvKbDpRAg6mmBIYOUC17HNz+OYPoPTpcrZw4f8kq5CtQKOBaKIFvUahLzY6cvKgUCtHb1KeuqdOkw4Zt+94LQwnUSzbkcVnSJcOfN64rBUOkcEjQFtqTQDnjUZ5MXL9+TRkmTyamzV4U7HEr5l+5b0Y0IoFqLoM7D3Dt6hVoVLeasl9X9u2yvSXgKQncsW0zqlUspbxn5Kme1jZmxGCI0MteNNmT5o3NmyVw395dymeeWUdg5cqcGleuXLJ0XyAl0BvfJdZcMyXQIFdKYOgA1b1Fv2zejXTpMxqk7Zzh6tMSKeMt5bzVRgkE5s+Zjg5tmiNVmrTYuG2/oaT9++8/+CpBLGVP4eUb9ymBhmi6HqzmTirU/Xb6MsKECeOWBEpn+Sb7+h/X8ObNGxQoWARzF1EwXJMPuYdeCVTfO7Ks8NKN++8tLxRhz5EpJYvDBEHvKQmcM2saOrdrgUpVa7rclx74kpcvW6Qcz1KmXCWMmzzTyEvNY2O9WQKnT5mAHl3aKSteZOWL0aY+2e03cDjqN/rOaLhgx1MCLcHqlUEpgQbTRgkMGaAUhZE/4KVJUZige4sMovfo8JC+RacEAulSJMDdO7fRqm0ndO7e23Ce1KcZew6dQIKEid+JxyeBhvG+EyBNsrj4++97aPRdS/TuNzjY4KE9CZSzIWVJqLRTJ44jVqzPsX3vb3wSaCBNeiXwt18PoUTRfEieIiW27Ar+7DL1ib03FxUxgDbYoZ6SwEE/fI8fRw1V9j7LHmitTc1zxkxZsHrDDq3DHNXPmyVQxF0E3qxiLksWzUPrZg3xbYnSmDJzgSV5ogRagtUrg1ICDaaNEhgywMMH96P0t18jpKV8BtF7dLhUv0ybPB6iRImq7AtUm79LoPp0QXisWLsFWbLlMJyn6pVKY/vWTZg5bynkUOTAjRJoGG9AgDWrfkbjejWU/5bjBORYgeCaq+Wg0aJ9gm6d2uDnpQsxdORPyJu/II+KMJAmvRK4eMEcSPVjKfoixV+Ca1XKl8CuHVsNVzU0cHuOG+opCVTP05X9s/I0UGv7+949pPkqLj6NHh0nzv2hdZij+nmzBMrfOPK3jlmFeeSICDkqwsp9gZRAR738PXoxlECD+CmBIQOcMXUiundui+q16mLoyPcLTBhE7/HhspRKpCfwfht/lcDvu3fEyRPHlSeAcrSAHAWSPWcepchB3/5DDT0JkthTJv6Enn0GoGnzNpRAE1/5Ug23V/eOkIIE58+ehhwuHj58eGTNngup06RF+0493stdaBKoXppadCS4qpQmXr5fhNIrgQP69sRPY4Yry9Vl2XpwTe0TUvVdvwAc5CY9JYFlihfEoQP7sGz1RmTPkdst9CkSfYFHjx6+V6jMrSAe7OzNEpg84ef4559HSiEXETczWq4saXDl8sUQz8c1Ogcl0ChB3xlPCTSYS0pgyAA7tGmG+XNmYODQ0ahd7+0SMV9q6qHxo3+ajIpV3j5B8VcJlHsfPqQ/RgzpH5Dib74tiVFjJxsSQAmm7pUJrlolnwQaf0eJCHbp0Aorfl4cEKxhk+bo039osMG1SOC82TPQsW0zn6oKbJy0vgh6JVA9Y1P2icl+seCaVPCVSr5Gj3LRd2fOHOUpCTRy3t+3hXLj2NEjSvEsWXnjbc1bJVCt5hk7TlwcPHrONOyd2jbH3NnTTVtiGvTCKIGmpcrrA1ECDaaQEhgywCIFsiv7glat24ZMWbIZJO284fJ0Sp5SBT7+wp8lUGQiZZIvAxIVXFl6PVlUq6/Ja0heS4EbJVAP0XfHyFEnxb7OhfPnzwb8YN+vp0NcwqlFAuVYAjmeQJaCLli62vhF+nEEvRIoxw3IsQOhiYF6LMEXX8bG4eMX/Jjy/2/dExL47NlTJI4TXSneI2cEutsa16+BNSt/9tozH71VAtWiPGZX2F22ZAFaNq2PYsVLKce7mN0ogWYT9d54lECDuaMEBg9QztJLFOdT5Ye+VhRGvWN1Q37gKpj+LIFy0G3n9q3w/Pkz5MiVB/HiJ3Sryl1Ib8W7d+8gXfL4yuHl567cogQa/MwKPFwOd69TvQK2bFqPiBEjKvsu+/XuhspVa6FR0xbBzqRFAi9f+h25s6ZFwkRJsPvgcROv2P9C6ZHAF8+fI0m8GJADyKWqrlTXDakljRcTUsTrzMWbSqVQf2+ekMAL588if86MiJ8gIeTAcXebuqy3W69+aN6qvbvDPd7fWyWwUZ1qWLtmBb5r0RY9ev9/FYxRoFbvC6QEGs2Q74ynBBrMJSUweIDq+W4pUqZSzrvxxSaimyR+TLx+9Uo5JDZSpA/9ejmoFAJp0aSeUgX296t3IN9mVq5Wy5TUS5VZKXkf9NxBPgk0hletbPfBB5Gwcfs+JEn6lRJQhD6k3GmRQJGP+J9H0f1kw9hd+dZoPRJ45vQpFMqbBQkSJMKewydDBVKx9DfYu2enclagPNHw9+YJCdy6eSNqVimDXHnyY/HyX9xOgbpkvkatepAzeb2teasEqkt4R4+bgoqVq5uKXcuTfL0TUgL1kvO9cZRAgzmlBAYPUO+ZRwbTYftwdTO//OKWX+D+/CRw88Z1qF2tPLJmz4nlazabmotyJQvjwL497x1gTgnUj3ncjyPQv08P5SnRomVrkS1HLk3BtEigBDKyx0nThfhJJz0SuHrFMjRpUBOFihTDrPnLQiXVu2dnTB7/o3Kcixzr4u/NExI4a/pkdO3YGlWq18aIMRPcToFUeJVKr3nyfY2Fy9a4Pd7TA7xJAvfu3oG9u3cqyGQfvDSpefDZZ7GQM3de5MydzxSc6hd0vX8YEuKqDL0TUQL1kvO9cZRAgzmlBAYPUH6hyS82Kw88NZg6U4b3+74bJvw0Cl179kWL1h38WgKHDuqHUcMGmr40RhLVpX1LzJ459b0iQ5RAfS9jVRJk9KTp81CiVFnNgbRKoPqE6efVmzQLpuaL8KOOeiRw9PBBGDKwr6b3ovoEv2SZ8pg4dY4fkQ3+Vj0hgepyzk5de6F1+y5u50AtUKLlya/bwW0Y4E0SqMpf4CJo8m9my5oU6pJjQ6TA2rTZi0zNAiXQVJxeHYwSaDB9lMDgAZYokhe/HTkMX/8DUDbjy6b8osVKKGdt+fOTQPXMMXelQstbUJ5UyBOLug2aoP/gkQFDKIFa6L3bRwrtSNEW2TcmLIWpO02rBLb6rgGWLp6PMeOnokKlau5Mwb6BCOiRQPXMueGjx6NqjTqh8pQjXb7OnVkpBCQFgfy9eUICpUKrVGo1sqxQll/LMmwpLCMFZrypeZsEClvZ8yx7n6XJuY5yvqOZTd0XKHvhz17+C2HChDEtPCXQNJReH4gSaDCFlMD3AUqxCSkKI3vmLly7gw8/jGyQsnOHq4fGR48RA8fPXvNbCZScJ4v/GaTSpHAQHmY2dc9M7rwFlCWhaqMEaqMslVsXzp+t7PkqWTS/cqaY3mIGWiVQnkTJE6mOXXqiTYeu2i6Uvd4joEcC1crMK3/ZisxZs4dKVd67sueWxWHeYvKEBKpfmuo5I1BNrioluw4cQ6LESb3qneRtEihfcEvOwoULj7z5v8avhw/g9O9/ms5cigVJ0aD1W/ciTdr0psWnBJqG0usDUQINppAS+D7AUyePo0j+7EiaLDm27z1ikLDzh2dN/xVuXP9DKcCQNuVXeP3mDf558tL5F27iFZ45dRKF8mWF2eclqZeoLneK9fkXSnEYSqB7yZNCLxN+Go0HD+7j5p83IEv/JkyZrevbZa0SKGeEylmh8iRKnkix6SOgRwITxv5EedIr1XTlSYKrpu5tlv1ksq/Mn5snJFCqH0sVZDlrTj5D9bQalUtj25ZNGDBkFOrUb6wnhMfGeJMEvnnzBrmzymHulwK2gcjnqzxJN2s/oJoIdVtN736D0ei7lqblhxJoGkqvD0QJNJhCSuD7ABfOm4V2rZqiXIUqGDtxukHCzh8uBRhkj5Xca51aNf1SAtVCQKXKVlDkwooW3B+2fBIYMul1a1fh1IljSgcpY3765Anlf8eJGw8NGjdHtRp1EDVaNLdTpVUCd+/chsrliiPo01u3J/TzAe5K4JUrl5Arc2rEivU5jpy6pIlejy7tMH3KBKXMvTwh9udmtwQaOSNQnvBPnjgWslBQ9pBdOH8OiZMkVX73SvumeCmvODzemyRwzsyp6Ny+pfI5uufgCaUatlVNlgjLUuEi3xTHjLlLTJuGEmgaSq8PRAk0mEJK4PsAe3Ztj2mTx8Psb68Mpsqy4ZMnjEXvHp3QoHEzjBo12i8lUKRf5N/szfGBk/Ztodw4dvQI1mzYgQyZsig/ogSG/rL+vntHTJn4bsn4FClTK/v0UqdJp+s9oVUCr165jJyZUyFBwsTYc+itgLK5T8BdCZQzH2tVLeeWfMuTjLYtm6Bs+cr4adIM9y/Sh0bYLYHnz51BgVyZdL9PpFplvVqV8ejhw4AsyHmP7Tv1ML2qpFVp9hYJfHD/PnJnS4O/791TzlQtXPRbq5AocdUzcqNEiYozl26aNhcl0DSUXh+IEmgwhZTA9wGq5fyXrtqAHDnzGCTs/OG/HjqAUsUKIEPGzNi5e59fSqC6d2H1+u3ImDmrJUlTC43IBnzZiE8J1IZZ/ZJCen/xZWxs3XVY1xNAdTatEij948R8ux/4+p3H2i6Wvd4j4K4EThw3Gn17dX2viFJoaOUpceH82fxmCX9oLOyWwC2bNqBW1bKGjneQJ4Ipk3wZcFtTZy1EseKlvObd5C0SqC7PLFCwMOYuWmkL34J5MuPsmdOm7gukBNqSOq+YhBJoME2UwHcBBi4y4OtFYdQ7Vw+Nl/++decBwoUP71d7AqXISIpEXwQcEm/V8pgxIwZj8IA+7xQ04ZNA1x9gbVo0xuIF/y/9LxUgZf+K3uaOBKr7ZQ/8dlZZPsXmPgF3JbB96++wYO5Mtyq/+lMxL1cZsFsCJ4wdhX69u6FazboYNmqcq8sL9uey9LtB7bdLQKVZuSJD1wW6GOQNEihflEjBpbBhw2Ln/qPKk1s7WvfObTFj6kR8328QGn/XypQptUqgrBCoXK2WKXNaFSR2jA+tCu0XcSmBBtNMCXwXoLq0JWGiJNh98LhBut4zXJ4EyhPBjZu3I1uOnH4lgeo32ZmyZMOqddssS9ra1SvQqG41ZQmOLMWRRgl0jbtmlbLYunkDPvn0U7Rp/7ZKZ6OmLVwPDKGHOxJYvmQR7N+3G/6yKkA31FAGuiuBpb/9GocP7leq6Mp+TK3NjAqVWudycj+7JbBG5TLYtmUjOnf7Hq3addaFRpZ9S1GSlk3r4/Hjf71uWa83SKD6/pACLbLVxa62ZtVyNK5X/Z3fe0bn1iKB165eQaWyxRx/bAwl0NirgRJojB8oge8CXLZkgfKLyMoCIQZTZslwOcNOzrLrP3AIWrRq41cSqB4SL2Ih30Bb1YLbO0MJDJ22LBO7fPkiZD+l7AHcsG0f5N/0FIRRZ3JHAls3b4QlC+cqZ2ipS3iten34alx3JVCOe/j333+UKrpSTVdr69K+JWbPnIo+/YeiYZPmWof5XD87JPDkiWN4+PCBwq5JvRrK3q/W7Tojb4GCyr/lzJXXLa7qe1rdiiFFRKSYiLc0p0ugfIbJZ1mMGDGVKuAffxzFNrSyDzFV0thKlV+zzgsMSQJF/P7446pyb+vXroJsJZDP7rj/rRxJnTqdod8dVkCjBBqjSgk0xo8SGISf7EWRPSn+VmVOqoNKldCy5Spg5pz5fiWB1SqWwo5tm5WqoCL/Vrage8woga5pq0vFvvm2JKbNXuR6gIse7kjg8CH9MWJIf54VaIC6OxKoHjAthUHOXHSvkMTc2dPRqW1zSw6+NnD7tg+1SwLbtGiEUyfeXS0jeRs1drLu/XzdOrXBzGmT0G/gcNRv9J3t7PRO6GQJlC9UcmZKpYj60JHjUL1WXb23qXuc7NeV5ajrtuxB2nQZdMdRB4YkgfJlQq/uHd/ZPqCOadepO9p36m54brMDUAKNEaUEGuNHCQzCr2KZYpBqZe4uRTKYBo8P//PGdWRJlwxffhkbZy5c9isJTJ7wc/zzzyNDZ1xpTaBU0ZMngpt3HESKVKm5HFQDOKkOKsvFpHpt3wHDNIwIvYs7EqhWneRZgfqxuyOBe3ZtR6Wy3yoHxMtB8e6048d+Q7GCuZT3lby//LXZIYEq2+oVS2H7ts3Kf8r5gMLdyFP62TOmoEuHVqhRqx6GjHy3KrCT8+lkCezTqwsmjRuDdOkz4pfNuz2CUa3y3LPPADRt3sbwNbhaDhq4mJhMtmTFOtPPQDR8E/8FoAQaI0kJNMaPEhiEX+I40SHnHsm30PKtpj+1tMnj4d7duzhw6CjiJEzmF7d+5vQpFMqbBdFjxMDxs9csv+eGdarilzUrMX7yLJQuV1GzBMoXE2Yf5Gv5zZo0gbpU2aw/INyRQFVKZHnbkpXrTboj/wrjjgTKUyB5GqRHuqXAVaI4n0IOw758476l5585OYN2SuDXuTPj3NnTCuvIkSPj9O9/GkJzYN8eyJLQjJmyYPWGHYZi2TnYiRIovzM+i/U5CubJglevXr1zNJGdbGQudTWHWfvuXUng9MkT0KNru4DblG0Eeo8UspoVJdAYYUqgMX6UwED89u7ZiYqlv1EqD0oFQn9rsnlbNnE3bNwUfQaM8IvbV5eQFS9ZBpNnzLf8ngf3740xI4dAXZqidTmoVHXbuG2/5dfnxAnU16VZy3XdkUD1rMC48eJj/5EzTsTj+GtyRwLVQ9/1Cn/RAjkg+9XGjJuKCpWrOZ6NFRdolwQ+efIYsn9TKrPOX7IKY0YMQcOmLXQvBRUWUhQmWfzPEDnyRzh/9bYVeCyJ6UQJrF+rsrI/7uTxYyhfsSp+nDDNknvXElTdFxguXHjsOngM8eMn1DIsxD6uJFDdW5o1e06kz5BJiSN7hZ3YKIHGskIJNMaPEhiIX5vmjbB44VxlQ7psTPe3pp7PlShxEuw64B+VUdVy9HbtAV26eD7kvEC18JAWCZQ/auWPW287O8us949a1U4qt8o3yUabOxIo36DH//xtEYWrfz1CuHDhjE7vd+PdkcAq5Utg146tmDV/GQoVKeY2K/X9nDtvfiz6+Re3x/vCALskcNigHzBy2ADlXFU5X1WafFYZfeKiHsuy59AJ244xMJp3p0lg4HMXP/wwMoSlO0WWjPIIbnyhvFlx5vRJNP6uJb43WJ3UlQRWr1QK27duRteefdGidQdIwRgjxwpZwUONSQk0RpcSaIyf30ug/NJSvjG79raiVOAmhShkk7uRPQ4G02PL8KDr59VJZTls+049DJXjt+UGDEyiLmdavmYz5FtDq9uJ40fxzdc5kSpNWuXJXkgSKL/E5XUpT6eDNhkrr0ujf2xZfa9mxc+QMiFu376FI6cuIVaszw2HdUcCZbLsGVMonw9Gzyc0fOFeGsAdCcycNilu/nkDew+fQvwE2p4WhFYMQj7Dps9e5FdLqe2SwFyZU+PKlUumF9SqXa08Nm9cpxSBkt/B3tCcIIFOfB/INUkBofW/rH4vjbK6QnKs5/eYKwmUc3/l/N8tuw4jeYqUjn4JUQKNpYcSaIyf30ug4JMPqoZ1q2H3zrdnxMlSlP6DRzj+kFGDqX9neFAZlk3+8jRUzwe0mddlZazA35ZevH4PH3wQycrplNhPnz5BkrgxlLlkTldPAtXqlOqFSel7py5rsQpe0IqqRudxVwJlibjIuJOLCxhlYuV4rRIof7TJH2/qe8Pda5J9R62+q49///1XGSr7OOWPTF//Ei8oJzskcMO6NahXsxLixI2HA7+ddTdVofYf9MP3+HHUUHTq2gut23cxNbZVwZwggeq9STErOaD98ePHyj854ctsuaZundviyX/XJEdVTJ+7BJl1ruwITQLVbT3esoSfEmjsXUkJBDBi0mJMnbcmgOSM0V2QNX2KgP9etnYneg6Zqvx3icI50LdDfUSKFFH5b6efE3j/9nXE+iIu7v/7wtgrJZTR8pQhf84MkHXrapMN7v72x4PsO1NLfocJE0bZ55E3/9tzn3yxbduyCTUql1b2DKzdtMu2W8yWITmu/3ENuw8eR9KkyRA9SgTcuv8s2PnPnjmlbOxX26DhY1CrTkPbrtXTE126eAF5sqVTngrJ0yEzmrsS2KZFY6Xk+IgxE1Clem0zLsGvYmiVwCOHD6LkN/kDnpLrgaQ+AZCxcuannP3pb80OCSxfsgj279uNXn0Hokmz1qYiVs/qLVG6HCZNm2tqbKuCOUkC5R7VAndOeR8E/sJVzYEsra9ZpwE6df0en3z6aUBqtCzdDE0C+33fDRN+GoU69RtjwJBRVqXctLiUQGMo/V4C/37wCNMXrkOz2mUUsbt45Qa6DZqCAV0aInGC2Dh49AxGTFiMcYPa4NNoURRhlNaucSXl/zpdAsuWKIK16zZZJoGyEb1cicKQZXryh+aUmQuU5QuNmrTwqyeB8iGdLWNyTJo8DcuXL8PC+fOUJ6JSpj1l6jTG3qUOHT1y6AAMG/yDch6VnEtlV6tVtSy2bNqg7Hv6pljxUCVQloQGXkqT7Kvk2LbniF2X6vF5pMKdHNuSK09+LF5uzh4vdyVQfZ049ZwpjyfJxQVolUARbRHuchWqYOzE6W7fljwJlFL0T548wd07t/FV8hTYuvtXt+N4+wCrJVBd0i6Hfx8+fsH0KtpqxeZkX6XAtj3ekT8nSaC8D9q1bIIHD+6jWInSuHrlkseLismTwMkT5eD2yZg7axp2bt+Ci79fUN5q0T75RNl2IkcASZPtKbJ/r1jxUiG+FUOTwIJ5MuPsmdOYs3AFvi5UxPFvZ0qgsRT5vQQGxSdS2KzLKLRrWkl5GijSlzDuFyhfPK/SNagUOlkCXzz7BwnjxMKCRUuQt2BxY6+UYEZL0YcalcsoH0gJEibCll2HECnSh0pP+SAN7UPI9IvxcEBZDho1ajSkTpEUr9+8QdOmjTF/9gx89lks5YDXL76M7eErNH/66pVKY/vWTcofnPKHp11NPfKg+/c/oFWbDqFKYJqv4uHve3cxeMRYdG7XAhEjfoDj567i44/fFivx9bZw3iy0a9UUFavUwOifJptyu+5KoConFStXx+hxU0y5Bn8KolUC+/fpgXE/jkDHLj3RpkNXtxHJFwap06RHo3rVleIyUuypRq36freiw2oJbNm0PuRpnVVL06XaaIIv3h7PdPGPe4gQ8e2qJSc3J0mgvA/krMUL589h/da9ePTwgfK+8OTKpqB/T8l/p0mbHn16dsba1SuU1CZNllzZhjNq2CDEjZ8Ao8ZOCjHlIUmg7CeWfcVSDOf07ze84rVDCTT2zqYEBuEnktdtwGRMHNIesT+PiV7DpiFHptQBEhj0SaHTJFDdSPzw4UP88+gBjh87igQJEiJ23PiIKoVKOvcwbZ+ausxL1o7/smm3clacv7eokSMoEvjo8Qs0qltNOdNOPpxXb9iOKFF869xEdenYnsMnkSBBIttSrx5LUblaLfw4bnKIErhm5c9oXL+Gwn/73iPKHhzZiyPy2Kzl/89Asu3CPTDRiCH9IfsiZW+Q7BEyo7krgeoek+w5cmPZ6o1mXIJfxdAqgXVrVMTG9WuVJYCyFFBvU4+ZkL2zIir+1qyUwDt3biNzmiTKsRC7Dh637HNTfZojh5vLIedOb06SwBfPnyNx3OheI9EH9+9FgzpVcPfOnYA0fxwlCtKme5v3ylVrvLcqKyQJnD5lAuT9700V3imBxt7dlMD/+IncNek0HDf+ugt1T+DTp88VCaxUqkDAHsGgEvjq9RtjGbBg9P3791G/Xj2sXPn2GyJp6dKnx/Rp05E+QwZTZhw0aCB6dO+O6NGjY/eevUiWzD8OR3cFT/6AeCP/77+XRd68ebB3zx4UKlQY6zdscDXca35++tQppE2bBjFjxsTNv27Zet17du9Gvnx5kSNnTuzatRvhwoZBcO/D/PnzYfeuXZg0eTLq12+AHdu3o2DBrxE3blxcvvJ+NVtbb8KmyRo1bIjp06dh4qRJaNDAnL2QYcOGwWs3PveuXbuGRAkTIH78+Lh46bJNd+4704QJA4RBGOXLpdBayhTJcf78eRw/fgIpU6XSDWDChPFo0bw5GjdugnHjx+uO480DQ/pMMXpP8jtTfneWL18Bixa/3VpiRatZowYWLJiPadMVmqwwAAAgAElEQVSno3btOlZMYWpM5ffmG/nN6fl27OhRZMqUESlTpsTxEyc9f0Ear6BqlcpYsuT/R3NFixZNyX+ZMmXfixAGgNQtCPqZUqJ4caxfvw4TJk5Ew4aNNM7s2W7yXmXTT4ASGIRd4OWgaZMndvkk8K+/n+qnb+HIBw8eIHmiLwJm2LR9P9KkTWdoRokpHyzLlixEs8Z1lVir129DlqzZDcX1pcEffxheEcB/n75Ubuv+33/j2yJ5ceni76hYuRrGevDAWTM5z587E21bNlWW/M6Ys8jM0C5jSQGi5Im/VJZ0Xr5+B9E+Co+7D5+/M+7okV/xTaHcytPpU+f/CPhZ4fw5lP2rk6bNQemyFVzO5e0dKpcrgR3bt2DhstXIX6CQKbcTI0pEPHzyEi9evtYc74vob5eJ37z3RPMYdnxLIPIH4SHHKz56/PYzJaRmFuM9u3agfOlvkCNXHiz3wye3IiQxokXE7RCKTel9Xcrh8BnTJFV+J6xatxVZs+XQG8rluB9HDUP/vj3RtFkr9P5hsMv+nu7wyccR8OT5azx7/srTlxLw9438fpDfE97UalYth00b1imXXODrwliwdFWwlx8pYjh8EDEsHvzz/4KBUt8hcdyYSv9jZy6bcpyQHew+/9T6quR23Ien5qAEBkM+8D5Ab90TKBuJZZO/LAsNGzYs6jVsir4Dhul+ncmet5NS/CVhIsiBxK9evsTkGfPxbYnSumP64kB1Oeg/T/7/B5uckfZt4dy4d/euslenyXetsHD+bK+uvNehTTPMnzMj4DBZu3OZLnl83L17B0dPX0KqZPHfqw7avHFdLF+2SOEte6TUtmThXLRu3uidA5rtvnY755PKoFIhVApESKEIM5q7y0FlzpyZU+HqlcvKocsJEiY24zL8JoaW5aBnTp1EoXxZkThJMuzcf9QQm1u3/kLGVImUL1COn/0fe1cdJ8WxBgsnIRAIBAju7u7u7u5wuLu7u7u7u7u7uzsET4CE4Hm/6nt92dOd2ZndW+nvn/fIdff0VM/uTnV/X9UjQ2O5Ymd7pYMunDcLPbq0M6TeqhVPpgUzPZjq1MvX+PeY0zqOo9o5Uzqo0dpaR2Hm9zpSQbRs+UrYtGGtqOe7//Q/xXbL9gGlg0rbEtYashbSVUKlgxpbKY8ngUzv3H3oDJrW8VZSkmmhQ3t6iRRQV1UHpUJUg4YNULxQXty4ccOw3C8JJcnMkUMHhIlov0HD0bRFW2NPnxv2DogE8jYvXTyPCqUKC5+7WnUa4ML5s9i5/7jLIiBrToLL+61y2WI4fuwwVq/fhvJlivkigbLuhuCeuXwH0aL96oPz169fRU0O22zavh+ZbPRZcpWFo0AE7/nO49c+ok1G524LCaxaoSSOHj4gXkjd2TbFKLYB9ddCAqWsOz3N6O1nNJLFj46///4Ll28+RpRfvOujPCXsQQKZ6pg7SxphDj9l5nxUqFTNrnDSJiBHppRCmOz8NedPwXYmElinenns27MLcxaucClxO4rFMFMre848oI3S70+fYOqsBShf0VvJ3hoJpHDa4oVz/W2c2vVBNWFwRQKNgejxJFDW/W3Z/d8LuTv5BLb0qo9ly5YJVT6q8+kJfqlcvXxRdGH6H5WjWNDOQnOeLFKYQ4VvBAIjgVQcW7poPtatWSE68KWgU9de4v9HjPSzS50KSlNqnjDfevjSNHKh51nq3qkNFi2Yg6Ejx6FLx7a+SODQgX0wZeKYQBUxJ4wZjpHDBqJshcqYPnuRnsu6VFsS3fQp4ouXeL7MmxW2kEAqlFKpdNS4qahV1zuVXIU2BAIjgczO2LHVO91r5fLF4It/+oyZUaRoCfGdUr1mXZsVDcuVLIgzp04IIR8K+nhS2IME7tm1HfVqVsJvsWLj+NlrCB06tN0hTRrvVzDF7+rtp8JGwJnDmUgg1TH5ruPKWQszpk7AwL49UKxEacxb7L/2NKCTQHnfm3ccEJkyrhKKBBpbKY8ngcbgc36fwLkzJqFTp07watEG/Qfpqw1gesGYkYMxe8YUXzBxt5l+NcEpmWx03ezVPzASyOtRpZFqjZaRM1dejJs8U/j6uErQEqRG5TJCojq40kbmzJyKvj07o7FXC0yfNtmHBPKklT9mrLvZfeBkgB6Nf7x5I9p8+vQRpy7cRKzYcVwFel3zvHjhHEoWzi02bagSaFbYQgIl8W7bsRu69exn1lQ8YpygTgKZ8cHv6Pfv3vlgkSpNWvH9nDqN7TXgXTq0FJtWnkja7UECa1Ypi4P796DPgKFo3qq9Q57bCqULg8qR9AelT6gzh7OQQH6OUiSKKSwSbj/6T23TmbELaG68j0xpEotNgCOnLiFBwsS+mvklgdeuXEaR/Nlc5uTY8mYUCTT2dCoSaAw/pzeLP3fyMAoVKgSSjdUbd9h0ty2b1seGtd67SUrmPWgIgyKBfolgtF+j44ILpOr4vWNp/l2voReGjZpg0zNltBNfqPhilSdfAezbu8eHBC5eMAfdOrWxao7etUMr0GqiResOwg/NHYP2JE3q1xApTUxtMitsIYGrVy5Fu5ZNUKlKDUxyE3Eks/C0No61dFDu+HPnn0Ebmuv3nlkb0urfZ06biAF9utu0eWh1cCdvYDYJlPWa9NA9f/We6ebwgcFJr7tF82cLLQBpJO6s0DsLCTxx/AgqlSkqygRYLuDK0adHJ8ydNQ2NvFpg0LAxQZLASeNHYfjgfiK7a9ykGS5124oEGlsuRQKN4ef0JDDE1w+IHDmyoZ0tKeoQOUoUkQ567c7vBlFz3+7WSGCjutVEXeVff70XIDA1yJVOATlnWTNB83GakAdHPH3yGFnTJ0P0GDHw5MlTQQIt625YF8UT68Di9q0byJ8zo1AYPX/tnvh8uFvwlKh/764BvgQYuVdbSKB8ucqSLQc2bN1r5PIe1zcoEvj1yxekThpHfJ+kTJ0W165cErXGRk4BCfDe3TtQt0ZFFChUBEtWbvQozM0kgSypoDE8PUtZQjF4+FiHYTl/zgz06tYBNWrXx5gJ+qw+mFrMdGNuIDkinIUESsyYss5TcFcO1p+yDlVsPly7J37rZPg9CZTp3zPnLUXpsv4tJZwZB0UCja2OIoHG8HN6Ehg5QhgkTJgQ9+/fF6pxVI/TE/wxaN6kLs6fPY3O3XoLXxm+YDjqx0HPXJ2hbVAkkOm17Vt7IWu2nBg8oJf4X/7YuFptpTSJP3zyIhImShJssCeIFRk09v3jz7f48DUMZN0NjeuPnL4sfJCCilpVy+HAvt0YMmIcGjRuFmz3Ya8LkwCSCJqdgmYLCZSkPUbM33D28h173bJbjhsUCZw+ZTwoChPzt1g4duYK9u7eKV7eZb2xrYBQyZWbf7HjxMXJ8zdsHcYl+5lJAls0qYctm9bj+/dv4juJ302OCgpnUUArQ8bM2LLrkK7L8nuDz9H4yTN19bO1sbOQQFlr7gqnp1qwpkIslWL9/gZYkkCmjqZM/JtQkb9x/7nLbYgqEqjlSQi8jSKBxvBzCRJYsWJlrF+/NlClKGsQ0BLi8MF9WLZ6E/IVKAySGVUPGDBq1kggcdu/dzdqVysnVBJnzl3iUljeunkdBXJlMi3tzNqzF9TfSxXJI1RWDx0+jEQpMgnrEj6nTH1hCoy1oAIcTzW1kkZr4znb370a1MTWzRswY85ilClfybTp2UICefF4MSLi27dvePj8PULR+E6FJgQCI4G0SMmZKZVQ8bTcwTfr+1luslBmnnLznhJGSSBFwPD/DajaVcuL2uOs2XOiW6/+iBTpZ8OntFrXgX6qqZLECtIqwHIskr53796K/9SvZxc8evTAl9IsS0rsFc5CAuWJmCvUUWpZCyoyU5mZgkTczCHRY1iSwDWrlqFti8bIk68gVqzdomVYp2qjSKCx5VAk0Bh+LkECBw4agn59e9tc/yTlwrlLZJlSYBA6t+xuLR2UN02rjewZU4g0UKaDulLQf7JDm2YoUqwkFixdE6xTp98fff9mz56DhMnTo3DeLOL5PHf1Ln78MYKmuZHQktguXLYWhYuW0NTHVRqVLpoX58+dwZadB5EhUxbTpm0rCcxFifz7dxHcJ8imAeGggQIjgZ3atcDyJQvs9vJWolAuYW2zfe9RpE2XwUF3G/yXMUoCSaaY9s/vecugYM/chSsdmv6vR+mSWT+N6lXD1cuXfM07Ttx4ggwaTTEOamWdhQTKLBd3skYpWiC7WFNuOJcuV9EfCZSeuv0Hj3QplXL5PCkSaOw7U5FAY/i5BAnctHkbypUtZZNxrCxqT54iJfYePmMQLffvroUEEoXY0bxr0O4+eYNw4cK7DDDSS4gKj1R6DM6YPGE0hg3qi85duuDps5dCzVCv0At9kXhPrmKqrAfvDCkT4OXLFzh35S6ix4ipp2uQbW0lgX4zCkybkJsPFBAJ5Ak4T8IZB4+fR+IkyUxHoU3zRqKejUI+FPTxlDBKAokTT2Pz5cyAly+eC9jM8m/Uuwayfnv2guUoWbqcpu61qpTFgf17RNv4CRJi+56jds9WcQYS+OTxI+GvFz16DJy7ek8TVq7QaNXyxWjfuikyZ82Ojdv2+SOBTAXl83rszFXEi5/AFW7J1xwVCTS2ZIoEGsPPJUjgoyfPEC/Ob8IriJ5BeoIv1pQLr1mnAUaPd+1CaT33bWtbrSSwYO7MuHnjmiDWJNiuElIkaOW6rcidt0CwTnvn9i1oWKcqUqZMhWvXroq56LV8YKpWlnRJ8eb1a4ydMB3Va9cL1nsy8+Jyo+HJqw9mDgtbSWDn9i2xbLFn2g4YWYCASGDZEgVw9vRJ3ZseeubhqbYeZpBA4pwodhR8+vQJYcOGQ8HCRX2lVupZByNthwzojamTxop6/g5demoaSv42sbEZarNMj82ZO1+Q13YGErh75zbUr1XZLcWQ6BdL31jWhrJGVKaD7ti1HxXLFEGSpMlx4Ng5Tc+HszVSJNDYiigSaAw/lyCBHz5+ReJE8YUBqt7dHqb+MQXQE/2ibHk0tJJApgvt2LYZs+YvQ6ky5W25lMP7PHnyCNnSJxd1BcFlEm9503du30S+HP+lqbHujfVvemPE0AGYOHYEEiRKjNHjplh9YdE7fnC0v3f3NvJkSyfqHY+euWLqFGwlgZJUtG7XGT36DDR1Tu48mF8SKO02eLpLQ2t7KdtS0bJpo9qmW4w4+1qZQQJJfJo2qoM3r18Ji4blSxdgzYaddj9R84utfFao+Mi6UWvBE6EMqRKKOkbGjxEiYP2WPYZSQVn6cOLcdacngRPHjcSIIf3RtGVb9Bs43BpULvV36VFcrmIVTJu10IcEdu7cVWwSuPI9KxJo7FFUJNAYfi5DAitWrACenOiVAKaMPuX0Xe3EyuCy2txdKwmUO7S9+g1GyzYdbb6eIzsO7t8L0yaPQ+KkyXDw2HlHXjrQa8nTLjbYvOMAMmbOqnteL148R8ZU3qp9tes1wsixk3WP4WwdKJDD9Eue1vLU1sywlQRKAYKKlatj8ox5Zk7JrceyJIH//PMBubOmxfNnv9s9TZOZCjwVcuVTAlseDDNIIOvrWG/88eM/wreRJ2pmCfbouaerVy6haP7sQhWc6uDW4szpkyhXooDwMgwdOjT+ePMGazbtRI6c3qnHeoP1kcUK5BA+pUEpijvDSaCsjaMiatUadfTeqlO3p4hUuuTxxBxPX7yFRAniIXy4UMiUIR2uX7uK1Ru2u+zmpyKBxh49RQKN4ecyJLBvv/7gblDbDl2FSpmW4I8W88UptkFRGBXWEdBKAmWarS0eTtZnYV6LwMQCeAXWuYyfPMvhu9t8saDVhl8BA86JP94Dh4zSPCe5Q+oXMYoh8GXAWhqTeUibO9KKpQvRsW1zgYfZMu+2ksCTx4+K1CPL2hRz79o9R7MkgTR0prEz1SZ5QmPvsFdKsb3nbWR8M0ig9MVMnyETtu4+bGQ6hvvKNbzz+LXwjAsq5PNF66KvX7+JLCC9dgnSComZLn6DCqPjJs/0J47jDCSQwmIkRO4qhMTfA/4uMBODYoFvXj9DkoTxTUn5NfyQGhhAkUAD4AFQJNAYfi5DAlev2yjy3QsVKYZFy9drumspoU9bCNpDqLCOgFYSKD2csufIjbWbd1kfOJhbUEKaJzkyxk2aEaz+hnzR6NurC1j0zvjhxx8xefo8m/wrmbpVv1YVIbXP4KbH6AlTUbZ85WBG3fbLjx05RGz6dOzay7BnnN9Z2EoCmY5OtcJff42O89fu235zHtZTksBLV28J7z7G7gMnkTJ1GrsjITNBWC/EE0FPCDNIoEx9btW2E3r2HRSssMkaPy3kJkemlODGH0//Xr98KdKB8xcsgqWrNuq+B3oN0qtURlDfRc5AAt19w+Palcsokj+b2CC9dvO+UNZu17Y1ZIqo7gV2kg6KBBpbCEUCjeHnMiTw+u0H4gWMaR7X7z7TdNejhg/C+NHD0K5Td3Tt0VdTH09vpJUEvnj+DBlTJ0K0aL/iwvUHTg9bw7rVsNNiZ5fWFrS4CM6QdhVyDtfu/K75BNDvvFMkigma5loGd60HDB3lqx6G5PPt2z+D/d6t4S53fe1Ry2srCeScpVegq6niWsPbnn8nCXzy+CE6tG8nUvobNG6GISPG2fOSPmM3rlcd27du0l1GYPbkSEwc9X1jBgmsUr4EuLnkDNYzzZvUxab1azB24nRUrxW48NWpE8dQoXRh4SlHga2/3r9H6qSxxVLaYiJ++dIFFC+Y0+dRCColNLhJoEybpcou1XbdNapVLIUjh/Zj1JgJ2LhhLQ4dPIAJU2ahSvXaLnvLigQaWzpFAo3h5zIk8M+/v0AqRJ25dBsxf4tl9c5paE5j80XL16FQkeJW26sGgFYSSKySJ4iBv/56L35gndl/kdLZ3CH+/v07ZsxZgvlzpqN4qbLB7ilEcR36lzX3aoi69RqgWs06Np1O8iV3zMjBIrX16OGDYuPjX/wLmi0zKletKVKoY8eJK9KjGNVq1nXqx13aMSxfs1nYX5gZRkgghXwo6ONJJ0tGsd+/ayv27duF2TNnIGrUaDh88pLNmx165yJFk4LbEoYS95269nIIETRKAimqkij2LwLqmw9eIEKEn/TCbmp7rYInvbp1wPw5M0SNOmvVxffc/0kDfQKZ/q8nvBrUxNbNG0SXUKFCoVLVmoGmpgc3CVy3ZgVaN2sIW8XF9OASnG2lonacOHHw+PFjMZUrt54gcpQowTktQ9dWJNAQfCod1Bh8cCkSKD2DtH6hS+NUWdhuFCtP6K+HBHKXlLulrBlh7YizRpP6NbBty0Zhps6dbYYW2W973w/nkDdfAfwSMQxe/PnJ5jlxnNRp0vu8WLPmMFLEnzFtyjgsXjAH3759EzLvjZu2xO+/P8E/Hz4Ei9y7HjypDEqFUHuQLSMksEblMjh0YK9ISWdqugrrCHRq2xRrV6/E58+fHa7SLNUlg1vMhyf1rPV1xOaLURLI7xOeBFKKn5L8wR2szeOGWZ58BbFi7ZYAp8PvuLTJ44qNr10HTiBV6rSi3fQp4zGoX0+wRpBZBXpCbkTJPkFZ8AQ3CbTFSkMPFs7S9t9//0XuLGnw4IG3D6I71GcrEmjs6VIngcbwcykSKHd1tdQJ3bh+DYXyZNasKmYQRrfprocEtvSqjw3rVgmlRL5kOWMwdYS7wQxaDdBywJkidKiQPiTQHvOiUADX6cZ1b5uFECFCgD+kUjCmWo3aDnkx1Xtvsr7l9qNXplsIGCGB3Tq2xuKFczF89ETUbdBE7215THueTs+ePpkPHM6dPYWP//wjsgXSps8IRz5zFy+cQ8nCucWJO2vKHBn9enXBlcuX8O7tn+DGDNNB48SNj0iRIqFTt96GbAuCug+jJFCKTTlDPSDv8+GD+6KWNKha3L27d6BujYpIljwl9h054wPPrZvXhcppjJi/4ezlO7qWX2a6kAyfP3cmyLTD4CaBdWtUwN7dO62qmOoCwMkac3NizIghePbsd7FByKA5fOw48ZAzd17Ta8cddfuKBBpDWpFAY/i5FAlkagZTNIoWL4X5S1YHeefLlyxAp3Yt7KIuaBByp+6uhwSOHjEY40YN1WXk6+ibl8IQ7Tv3QJfufRx9eavXszcJlBMYOrCPsMdgSiyDL+SDho12SgJI43vu6jN18OKNh1Yx1NvACAmcPGE0hg3qC2d5QdZ7745sTyJIFVxZq8q0wsHDxzj0mfvw4W8kjferUJWkuqSjg0Rw9owpPpdNlSatSNtOnSad3aZilARWKVccx44ewuIVG4RJvDNEkrjRQHuRSzce4ZeoUf1NSQp/UcSGn03LkGIxlieE1u6JmSPMIEmTNr14Xvv27IwmzVphwJBRAXYNbhKYNX0yPH3y2Ck3Oq1hrefvJILtWnqBnr8y+g8eGeylHXruwW9bRQKNoKfUQY2hB9dKB5U7glp29Tq3b4lli+dj2KgJqNfQyzBOnjKAHhK4dvVytGneCJWq1BCeX84WM6dNxIA+3YVQwOGTF63KiwfH/B1FAnlvrEuSaqR+d8yD494Du+aF82dRqkgeu53eGCGB69euBP24ylaojOmzvesrVQSOQIM6VbFru3cKH2uymMrv6KDZ9+NHDxFcYlAJY0fB50+fxG07QpXYCAmkL2DiON4kyxnqAeWzUrZEAZw9fVJ4htI71DJYw5g2WTyhjkxBmFix4/j6e+/uHTFv9nToqQulquiWjevQb9BwpM+YGZXKFEW2HLmwbvPuAB/f4CSB0grrhx9+BDMn3D0sN1a4mbJz/3GXvmVFAo0tnzoJNIafS50E8lalCiIVKalMGVgwBYSpIDv2HRO7eSq0IaCHBJ47cwpliucX9YDB7SXl9+5oLpsrc2ohXDNjzmJRMO+M4UgSyM9OpkxZcWC/tz+bETVSe2K5ZdN6NG1YS9hlUJHP7DBCAk+fPI7ypQo5Tb2U2diYPV7iOL/g48ePyJQ5M86eORMsz1xwCoTxNLRpw9r49u2rgJYiRxQ7smcYIYEyfT5j5qzYvOOAPaepa+wuHVqC3rT9B42AV4s2vvrKjZnASBrTJJkumSlLNmzavt/qdUkmUyWJjW9fvwormAgRIoAnkUGRrOAkgdKuyVlqOK0CbLABN3Xy5s2PjJkyYNDAAdi1/4RDBJcMTjvQ7ooEGkNWkUBj+LkcCZRqX0FJV79//w4UhWEK0K2HLxEyZEiDKHlOdz0kkASLdRNMLaRCqDNFu1ZewkcoqN1bZ5ivo0gg02gY6TJkErvm3D0fO2kmqtes4www+JrDrGmT0L9PNyFkQ6Nns8MICXz+7HdkSpPYbqmqZt9rcI7HZ25w/16inmrh4qWIGSeRqI+T9aiOmhu93uj51nfgMDRr2c5RlxXXobl1jy7txeeNUa1GHZFSSK8ze4UREihtlZwt3XnOzKkiJZMWEbSKsAz6B+/euS3QOt0vnz8jaYLo+Prli9iEoM1UUME1o0VNrjz5sWr9NtE0b/b0uHvnVqBCVcFJAqmISmXUmnUaYPR4feI39noG7TUurVaEL279BggfLhQOHzsdLN8pZt6fIoHG0FQk0Bh+LkcCqfRFxS/Wd7HOK6CgLQR3f3PkzCNMY1VoR0APCeSoaZLFwR9v3ogdUxbuO0OcP3sapYvlE7Leew6dQtJkKZxhWgHOwVEk0PLi0nerbYeuwjrC2cLeL+1GSCCxUl6B2p4YChBxk4gnK49/f4EQYYLHaoBCPhT0CY6XZL9ec63bdUaPPgO1AWhjKyMksGKZIjh5/CiWrNyIAoWK2DgD87sdPXwAVSuU9Jd1QjVQ1g+HCBkSF689wM+RIwd4cUkUJ06bIyxzggqpCkpCxWeGIUXQWPbA8ge/EZwkUIpVcXOBdYueED+EDSVI4B/vP7v87SoSaGwJFQk0hp/LkUCqUfILOahUsbEjh4AKZ862m2lwqRzSXS8JZGocU+RYK8FTN2cIqgFSFbBpy7boN3C4M0wp0DkEBwmUXktUVjt25qrT4cNUUKaEzpy7BKXLVTR9fkZJoPQK3H/0rFNvMJgOnM4BJQFKkTIVTp+9gLd/f9E5gjnNSWpIbrJmz4n1W7xToR0VC+fNQo8u7cTJH2u3SKxIsOwZtpJAy3pAe6jyGrlnkr1USWL5S8mkBU63Tm1QrERpzFu8KtBLyHYVKlXDlJnzA2338uULZEiZQPzd0lpqxtQJGNi3hzhJ5omyM5FA+RvMU0ueXnpCKBLoCaus7R4VCdSGU6Ctnr7+x+AI9uv+Y/jQiBwhDD58/AqaxTNo1MyXMBZ/swg8oJA1IKwnIllUoR0BvSRQpl2OmTANNWrX134hO7VcsmgeunZoJdL1aAnhzCb2hCA4SCDTotKljC88tVgjw1oZZwpJ4rfsPIgMmbKYPjWjJLBW1XI4sG+38Jyk96SKgBGQL85NvJpj/MRJwUYCmanAjAWqk1LsxJEhvx9JHogHUxGv331m1ynYSgLpf0kfTGf1XsuYKiFevHguRL4SJkoiMKRgy4njR4RIE8WaAotnvz9F5rRJBP40F2eWSEDBtGFmIpQuWwEz5y31aSJrJZnKvHrDdn9dg/MkkOq3VMG9evtpoCehdn3ggmFwRQKDAXQnvaQigQYXxtVIIG9XykXzxzSg/H5pEh+YnLRByNy6u14SOHHsCNC/MbhPXbnLvn7dSgwf3E+Qm/GTZwp7EGeP4CCBxESmEDXyaoFBw8Y4FUzpkscDhX3OXb2H6NFjmD43oyRQYjd4+Fg0bNLc6vz4YunVvLXVdu7WQKbgLVi8HJUrVw42EkhcmTJI6xG9aesrly0yZGkha8konFW9Uilhl3H64i2hWGyvsJUESh/eNu27oHvvAfaans3j1qxSFgf37xHkjCTt96dPQGuEH3+MgEs3HyJcuPBBjl04X1Zcv3oFazfvQvYcuQNsW7poXlHDOnvBcpQsXc6nDdeNwlqBicMEFwlkjRwtMB+3mScAACAASURBVILyULQZcCfuqEigEy+Og6emSKBBwF2RBMq6haWrNiJ/Qd91CzdvXEPB3JkRJ248nDh33SA6ntddLwncvGEtmjWug1JlymPW/GXBBhhf1saNHiaMhTNmyoLNO72FUJw9gosESkU5KuyevXI30J1xR+P39etXxI8ZCaFDh8b9398Kc3uzwygJnDR+lNhsCCw1zHK+fEkrVjCHEKTwpLCsB7z/+DmiRf0lWEmgPDHiKY5WYRoqe7K0wFYJeilQFjpMGNx5+Aq1qpUHT5Rok0G7DHuFrSRQphUG9Ltqr7nqGXdA3+6YOXUiOnbtJYzBp04aiyEDemv2Aqa/J30+A9uwfHD/LnJlSSNOjK/efgKum2XQsJ6/L0dOXUKChIl9/S24SOCuHVvRoHYVh6jO6lkre7dVJNDeCLvO+IoEGlwrVySB0icmIGNYqexlLfffIGxu210vCbx65RKK5s+OFKlSY8/BUw7F5crli3j37q24JgWDLpw7I/7/qPFTkTBRYuTMldeh87HlYsFFAjnXbBmS48njR1i2ehPyFShsy/RN73Pv7m3kyZZOvGTxZcseYZQEyrpkvyljcq48lb5y5aL4546tm4QyJUUbUqf1NghPnTqdXdUh7YGZ3jGl12PyFClx4vQFhA4VIlhJYPdObbBowRyrvrEk7Y8fPxS3O3v6ZJAIkrRJNU893yl7d+9A3RoVfRSKqZQ6bfI4HxKjF1Ot7W0hgTRiZ4YNw9nqAeV90+OUXqdyw7FYgRzgbwAtN2i9YS1kbWhgv1XjRw8D1VFr122IkeOm+BuOm53c9AzIcii4SODEcSMxYkh/TRtS1vBxpb8rEuhKq2XfuSoSaBBfVySB8seA3m/8QrYM1oOxLozS8pSYV6EPAb0kUL48MBXn7pM3+i5msDVfttu39sKObb59t3gKzBc3Gsk6ewQnCZQ74wHJrgcXbrIuKU++glix1ttk3OwwSgJpWk3z6nTpM2LbniMBTo/CVDxFsgymrg8cMspQeqHZWNhrPCo4c2OG38Fjxo4PdhI4e8YUcPPQWvozv1P69uoC/sb4Xbt5i1ZqPkVkX2m30LxVe/QZMBQb161GC696KFq8FOYvWW0v6GELCZSfu+AQz9EKxKWL51GiUC4kSpwU8xavRP6cGXWnQaZM/JsQ6Dl7+Q5ixPzN16Vl6u7qjTsC3EDkKSK/MwNSeA0uEihVS8dNmuER3ytywRQJ1Pqpcf92igQaXGNXJIE3rl9DoTyZAzwtkHn/9hKVMAi303fXSwJ5Q6zLePrksRDqoWCPo6Nhnaqg4iUjeYpUQgHQnj5cZt5fcJJAmTrtCLEKrZgxrbdDm2YB+oFpHcNaO6MkkOIUFKmI8ssvuHzzcaCXa9/KC6tWLPH5O9MKXWFjwhp+Wv5er2Yl7Nm1HRTnqlypYrCTQNaSsaaMJ948+bYW/Xt3w6zpk0Sz8D/8gHOX7+r+TpE1bEyT5+mVPOWO+VssnLl029oUbP67LSRQ1gO27dgN3Xr2s/na9uxIr8VEsX8Rl5BCOzSOp4G81mjdrCHWrVmBEWMmoU79xj7dJMEMSnCOYlAUhQroGQouElg4bxZcv3YV2/ceRdp0GbTC4PLtFAl0+SU07QYUCTQIpSuSQN4yfwz4o2ApDkM/qmTxo4sCcUefShlcBqfpbgsJrFaxlKh14ckNT3AcHTKtkbUcoUKHcqn6q+AkgVynIvmz4dqVy/6EEBy9hvJ68gRN1v3YYx5GSSDnFDvaj2JqVJvkc+c3pA0H/3vIkCHx/ft3cSrGDAVPCPoD/vXXe6HEGPu3X4OdBFJEJEu6pNBKwArlyYIb1/+zTzl+9hrixouva+kkBueu3EX0GDFFX/4+8Xfq4o2HQsHYHmELCaxQujBOnTimObXSHvPWMiZP/27fuuHTVC/5Wbt6Odo0b4QixUpiwdI1PuNI/+GWbTqiV7/BAU7lzz/+QOqksfFL1Kig6JxlBBcJlN9DfN+xJoyjBV9XaaNIoKuslP3nqUigQYxdlQQyHYtpWZaF/q6Q0mJwueze3RYS2L1zWyyaP9tqvY09Jn//3l3kzppGDL1px34snDdb2IK4ijVIcJNAKa4QUGq1PdbL2pid2rXA8iULMHbidHEaaI8wgwRKr8A9h06DPniWQXXBymWLgb5rhYoUFwbOPEEIHToMTl244UMI7HFvzjCmrAdMlSYtdu0/gQjhQwc7CSQukpTduP88SOsYyu2nSPQbvn396gPniLGTUKfefydH1nCW2Sp+T5aqlCuOY0cPYfGKDShYuKi1YWz6u14SaFkPeOfxa4QP/4NN13VEJ1mXx2sxLfTQiQu6LiuJnF+VT9pH0EZi94GTSJna+/ckoJAbjifP30DsOHF9mgQHCeTmHTfxEidJhoPHz+vCwdUbKxLo6ito3vwVCTSIpauSQEk8WGvBmgvGuFFDMXrEYOhNETEIoVt1t4UE+viBNWslBDAcGcMG9cHkCWOESixV7Ris+VDpoNpWQcqshwodWijiBXSqpW0kc1rJU2V7itWYQQKlFykNqmlULeP+vTuiXpB2BFSh5Ok4Pcmo4Eclv8pVa2LitDnmgOWko0yZOAZDB/bxqb9zFhJYqkgekKBu3nEAGTNnDRQ9uTHCl2tK71NJd9rshShXoYpmxKVfqd/NFXrQUSiIKZdMvbRH6CWB+/fuBp/nbDlyYd3m3faYkmljss6W2QKMzt16o0OXnrrHLleyIM6cOoFFy9eJTRrpAZgseUrsO+ItLhZYNKpbTdSgyxRf2S44SOCaVcvQtkVjf56GugFxwQ6KBLrgotlpyooEGgTWVUmg/JG1VAGtW6MC9u7eadU41iBkbt3dFhIoU9/4g8ofVkfF58+fkDVdMrx69RJLVm5EgUK+7UIcNQ8j1wnuk0DOnadWfNF1BnGB3FnTgkSKO9t8CbdHmEEC5SaUpQAViV/JIrnx+NFDoZa7aft+4WHGoAprrqxp8PXLF7h7vbIkyPJF2VlIYLuWTbB65VKrz3muzKnx4ME90e7lyxeC0Nat3xjDx3jXCGoJeaLdb9BwNG3R1qcLr8952NNSRy8J5P2RuLfv3ANduvfRcnsOb0PiPGbkYOGzaBm2iIBJb9sGjZthyIhx6Ny+JZYtni+8EemRGFTIjWa/tZPBQQKl2iztMpg+70mhSKAnrXbQ96pIoMFnwVVJoCzkTpI0OQ4cOydQSJUkljAKt7cZr0HInbq7LSTwzu2bYHpcwkRJcPiktzS+I0KqxFKGfu/hoHdwHTEfW67hDCRQbqhYnqbaci9m9JE1LvZMSzODBFLqny9hTVu2Rb+Bw0XqJ73oeNLEF9NNOw74M7qnlDsl3SngwFomd42k8X4FUyqv3n6KnyNHdpp0UKnuGFTdl9zQkubbMq1Tay2hXFN61VJ4iRsBmbJk81lqKcbE+kLWGdoj9JLAMsXz49yZU1i1fhty5clvjymZMibtIOrXqixM4hm067C079B6EWlrJNdAKoZqETaTth9+NzyDgwTKTW+/xvZacXDldooEuvLqmTt3RQIN4umqJJA76gljRxGCC/Q14k47i8YDKto2CJFHdbeFBH779g0JY0UWON17+qfDjMelSMDo8VNRs04Dl1wnZyCB3DhJlzI+/v3+XRjH00A+OEKqbtr7M2wGCZRy//kLFsai5etFuidfEEl6Nm7bB25O+Q3WXtGM+sXzZxg1bipq1XXNZzaoZ4Npdky3Y10V66sYznISSM+/xvWqB2nRIGv2eCLGkzGG1noxiYtfk3hL03H+XlEchs+CpaiZmZ83PSSQIjWslWTK8q2HLxE2bDgzp2L6WCkSeQvs8ESwao06GD95pk3XSJs8rkjZbuTVEnNnTUX2HLmxdvMuq2Pxs5sxdSLxHXnh+gOf9sFBAuVzyY1XbsB6UigS6EmrHfS9KhJo8FlwVRLI2y5eMCcuX7ogLAEe3L+Ldq28PDI/3uAj4Ku7LSSQA8g0Pkf9IDF9kWmMfn+MzcTCEWM5AwnkffLlmC/JTI9imlRwhBQUCcp/z4x5mUEC5Vz5/BUvWUZ4kzL4XUSvtcBCGs2z35HTl4MUKDHjXh09hjxtoxiOrA92FhIoLRoCy1iQQhtUWaSFAy1AGD27tseCuTPRo89A4RFnLaQdBesOWX/oN6QS58p1W5E7bwFrw+n+ux4SuG/PLtSpXl6cqtEfz5mDJ4H0hZ27cKUwiafv44lz122acreOrbF44VyfviPGTkadeo00jZUhZQKRJmzpNehoEkgSTELsV+BG0w24QSNFAt1gEU26BUUCDQLpyiRQ5vLzZeP2zetYtGCOMOWVQjEGofHI7raSQJmaQtltym/bOyRpsVUcwN7z0zq+s5DAzRvWgsp7WbLlwIate7VO39R2WzauQ9NGte1aL8UJm0ECX79+hXTJ4/m6/5lzl6B0uYpWMZEkwB0FrKiCSj81+gNKhV5nIYFcGJlu/OTVB3/r1L51U2ESX7dBEwwfPdHn7/Q7pO+hVuEUH4Gy5q3Rf/BIf9fp06MT5s6aZrffKj0kcMiA3qAQjj0tWax+IDQ2IPGz9Nl89ND7JE6vdQf7WFq48N8ydVnLVJiSunvnNmExIX/rHE0C5SZo+gyZsHX3YS3Tdqs2igS61XIauhlFAg3BB7gyCeTuLHdpmRZy5fIFXL18SbzA8kVWhW0I2EoCpeodjXv5cmvPePjgPnJm9pblp1m33LG35zXtNbazkEB6bqZPkQBMZePuOuvaHB0Uf+Bz5BXIy7NZ8zFCAvkiOmbEYLx79w7Hjx7Cv//+K6aVIGFicYLKuVsLWZPEdvuPnkXSZCmsdXGZv8t6QPoDRo4SRczbmUigNNf2a+0hU5E5X7+iRKz3TBwnqriXa3d+t6o8bE2gTNYyW4qambnAekigrAe0tFoycy7ONhaJI08Q//zzD5w4dkRML2KkSEiTNgNy5s4LiqxYCyqQk+hbbkA6mgTKdx+WQbAcwtNCkUBPW/HA71eRQIPPgiuTwPNnT6N0sXyIHz+hUHNjeJppqsHl99fdVhIof5TqNfQSfoH2jL49O2POzKkifYdpPK4czkICiWHHts2xYulCTSp59sCcL2ezZ0wRpydayJStczBCAnlNmZbGTSdG2LBhMWLMJFSrWVfzlHp0aYeF82YhT76CwkbCHYK+rbTHkP6A8p6ciQRKn7npsxehbIXKPrCPGj4I40cPC7RekCeBPBGcOmsBylesGuRy0VCcfnR+veRkJ3v7u2klgawHZH2ip/1ukgh2aN1U+DXK0HMSKk8RaQ1DixiGo0mgVCdmFhRTrz0tFAn0tBVXJNBuK+7KJNByh5YAeWpqhJkPh60kUNbB2PullrUQmdIkFuqD9rQRMBPToMZyJhJ46MBe1KhcRpxMUdzE0V6LXg1qYuvmDf48uMxeC6MkkPORhJX/3xalxz/evEGurKmFpyVTJ5nmZktam9nYGBlPSu/7TXN1JhIoT3H8yupLoZDA6vTkJpc1n0eplBw9egycu+q9MRlQyLTUmw9emO7NqZUEbt6wDs0a1xaKoFQG9aRYuWwROrT5r/ZZywmvxOfpk8fImj4ZfosVWyiRBwcJLF+qEE6fPC42kPib62mhSKCnrbgigXZbcVcmgQRFSnHz/zds0hyDh4+1G1aeMLCtJJC7qzkypfT1w2gPvGZOm4gBfbqjUJFiQpXR1cOZSCBTGyl6QN/FYiXKYN7ilYbhJVnq1LW3JkJZolAu0PrF3j56ZpDA7BlTiJo3GsJTrGLNxp2+6pW0ADdv9nT07t4RsePERY5ceTBxqmubyFevVBqHD+4Tsv0Uy5HhTCRw/dqVaNW0gTgF5GkgY+mi+ejSoaUvRVO/6/fs96dCJZSpgyQMIUKECHCJJbngs0FyH1iULpoX58+dwZpNO5EjZx4tj4vmNlpJYInCuXHpwjmRAulpPnM0fWcw44C1oMRAz0k+RVm4IUmFUIo8OfokMH7MSPj69avP9TU/HG7SUJFAN1lIE25DpYMaBNHVSWDbFo2xZtUygcKk6XNRqUoNg4h4dndbSSBRixcjImgXce/pH3aRGqe8erYMyYVP1PI1m5E3fyGXXyxnIoEEU9Z2hgsXDnef/GEYX5IlrS9Y8jTm/LX7oE+bvcIoCeSGB1NCpfCJ339rnTc/K4XzZsWtm9dBvKfOWugzptYxnKUdX0hZD/jly2fcuP/c1+mWM5FArluxAjmQIlVq7Dl4SsAnrWYmTJmFKtVrBwqp3HAMSgFWqk726jcY9CMMLLp3aiOEzOyR+qyVBMrva6qCUh3Uk4L1xzLlnKfxR48c1PXZkwJI3IjkhqQjSeDJ40dRsUwRsSFBmxFPDEUCPXHVA75nRQINPguuSgIpZ3/18kWcPXMK+/bsFCg0btYKkX+OjOKlyurelTcIo9t0N0ICpegC/cHoE2Z2MFWQKYOWHmRmX8PR4zkLCSSRWbl8sfCwW7zA+0SKL0mRIv2MiJF+1lWjx9OQx48e4t27t+DLFlMdJWGiiFNAaY+Wqd0BKTeauS5GSaBZcyE2FIkhXowsWXOAvoOMJs1aazo9NWsuRseRL6Zp02XA9r1HfQ3nTCSQAkiJYntbP/A5o5IpX+itpW+yvVTSbNuxG7r17BcgZEULZBcCZfSco/dcYEFLka4dWgnSSfJpZgRFAvnMvX/3Fvfu3sHa1cvFZaUYiqs9c2Zipnes4YP7YdL4UejWqz/aduhqdxJIorpi2SKxdvv37sKZ0ycRNdqvaNCoqfh+rl6zrkt9X+jF2297RQKNIug+/RUJNLiWrkoC+aXYt1cXIektgztjA4eM0pXWYRA+t+tuhAQ2qV8D27ZsxMx5S4Vfo9lRqUxRnDh+RLw0BbVjb/Z17Tmes5BA3iM3VpjayDQnGUzrGz95lq4XDH42Oc6ObZt9fTY5jiSDfjGVHm6JEifFoRMX7Am5KRYRZkwwIIGKcOHDY8mK9SLN1JViwpjhGDlsIJq1bIe+A4f5mrozkUBOLFeWNMJX9ujpyyIdd+/unZo8APndw+8gbmrs3H/c3/KwTpmnoYw7j18jfPgfAl1Cpj0z/dnyRNKs9Q6KBPKzyVRIS1EU/m7OW7TS5Z45s/CyZRy5IcnfOf7eOeIk0K8gFedNESZ+r1paZ9hyP67WR5FAV1sx+83X40ngx4+f0Xf0XGzZ/d+P0vwJ3ZE1/X+y42u3HkKfkd67+6WL5MDAzo0QPnxY8W9XJYHykZI7r/y3HoUv+z2Srj2yERI4dGAfTJk4xnR1Sb64UP2VL03RY8TEuSt3XRtki9k7EwnktCwFE8KHD487j9/YjHXiOL/g48ePon+1GnUwbvLMQMeSojT2FhbiBJzlJFCCIUVC5L9pN9F/8AihVGkZ/Bw4WqxH6+LLesCAfEKdjQRKpU+S1YF9ewiyRuPvnyNHtnq7shbM0ihcdjp25CCqlC+BdOkzYtseb/uBoEKuuzXCaG0cv3/Xkg5q+cxZejrqvZantpc2RfHiJ8CxM1cdQgKJ9dkzJ1G2eAEf2LkZ4WkEkDevSKCnfvL837fHk8A/3r7HvBXb0bJeeUHsTl24jp5DZ2HGyE5IFD+W+PfY6aswdXh7RPk5IsbO9JY07tjUW+balUmgFCOhRDJz+kMgRIA7tOrjoh0BIyRw+ZIF6NSuBarXqoexE6drv6iVliQmO7dvxbYtG3zSb0wbPJgHcjYSSJGEo4cP4MnjRwIZW18yZO2VhJeE8srtJ4GejshnxxG+V85EAnn62rhedbGBRaGYj/98wD///CNgY63RwKGjkTBREvFvfg54QuiMKqL00WNK7/V7zxAxYiRfnypnI4GD+/fCtMnj8MMPP+Kffz4If8chI8Zp+iZo4VUPG9etxshxU1C7bkNffZgeyDRBreMVL5gTly9dMF0IyRoJnDxhNIYN6osffvgBv0SNJtRBxwexQaMJGA9slCpJLLz980/QEzNRvBj48OkbPn7+Zlckmjepi03r1+DX6DHE542iW/a007HrzRgYXJFAA+C5WVePJ4F+15OksGX38ejYvKo4DSTpSxAnJiqV8i789ksKXZkE8qUo0s+RfVLMxowcIk4cnPElyVU+d0ZIoEyXypo9JyieYFY0b1wXmzasEcO5ujm8X0ycjQRSzbNq9dooXigXfv45Mjp06WnTSwZrjyaMHYE/3rxGrNhxQFn1Fm06oHe/IQE+Fvzsjh05xCGn+c5EAolTrjz5xG4+T/pGDO2PGDF/w8SxIwVBYTRt2VbUbVEEiyTQ2V76jh87jMpliwV6AuZsJFBuOMgHkWmh8RMk0vR1tXrFErRr5QVLjzjZsUHtKti1Y6tmgTIqklKZlL6q9Fc1K6yRwCL5suHa1cto37kHmrVoK8oqFAnUj361iqVw5NB+YdNQrnRxh5DAVIlj4e3bPzFx2hxky54Ls2dMBr0CPS0UCfS0FQ/8fhUJ9IPN3QdP0XP4bAzt3gSxYkQTqaI5MqX2IYGWf+dJoSuTQPUxMB8BIyTwxYvnyJgqIX6JGhWXbnifJNkaPEmisAijdtVy+PTpk3jx4gtxnDjx3IboOxsJlOuVImFMvH//DsfPXrMJax9J/YiRhJJr6WLeNW6BGWjzBJL1vWMmTEON2vVtfWw09XMmEhjYhF+/foWRQ/pj6eL5oCouCTlf/kgWBwz1fulzls+BJPD8bPYbONzfLTkLCZTfKTevX0PPru3FPLNmy4FuvQcIASQtaXVvXr9GuhTxxIn2tTtPESasd1kFQ6rbHjl1CUzptRbSe9Ds0++gSCDT6vNkTYuQIUPizOU7wt5AhW0IDOrXE9OnjAeVYHv37G53Esja1bo1Kog149qFDh3atom7QS9FAt1gEU26BUUCLYCU9YGS9Ml/Vy1bwKdG0C8J/OufryYthfnDhAkdEuHChMSXr9/x6ct38y+gRvSHAPH+F8BnG/GOFTOqEBZ58uyVeLGyNR4+eIAa1Srh0qWLvoYoXaYcZsyaI16K3SFChgyB8GFD4sNH+6YR6cWqWpWK2LZ1M+YtWIwqVavr7Y4N69eiTq3qKFKkGNZt3IKWLbywaMF8lC1XAUuXe6ekW0bZ0sWxf99ebNy8HQULeStk2it+DBdKfJ98+84n3blj3969qF2zqiDkllG7Tj2MGDXGKT4HJYsXxuFDB7FqzXqUKFnaH6D8Hg8ZAsH+HU4SXaNaZTFXy8iTNx+Wr1yjGctCBfLg1MkT2LBpGwoVLiKGun/vHtKmTiZe0O89fKrpoTpz+hQK5MuFDBky4tDRk5r6aGlEC8Mfw4fG3wH8tvfo1hmTJ01AnXr1MW36bC3DqTaBILBm9Uo0qFdbfD8uW7YMX799x9dv9vtOKV+2JPbu2Y1+/Qehc9fuHr0uoUOFADdQ7Z1+6wiQf/rBc8m8GfgqEvh/FCXhixk9qk+9n19SyKZ+SeC7D1/MWAe7jBE2dEiEDxsKn79+d4sPu11AMnnQcGFCgabhxNyWKJA3J86dPYN9B48iU+Ystgzhq0+WDGlw69ZN8d9atGqD4SPHGB7TmQbgrj1JyV8fnWszZsyo4RjYv6/NmPfu0Q2TJo5D95590KNXH7x8+QLp06TA33/9hQ2bt6HA/60Q5FpkSJsC9+7exbmL15AosfVTFCNryJOpfz5/w3cXIIE++KRJgXv3vAWRihcviZVrNxiBwLS+FP6JEdW7BvDxs1f+6gH53/k9zs0OZ3lhq1W9MrZs3iTmTAK4ZftuXXiMGjEUgwf29/XZWLVyOZo0rIeSpUpj+ap1msZj7WfMaN4bZW///qypj5ZGrI3/6YdQeO+HBDKzIkXShOIzePTEGaROk1bLcKpNIAjcvnULmTOkRuIkSXD9+g18/vqvIIL2iGvXriJHlgyijvParXuIEsXb5sRTI0yokAgdOgT++eRcm6e2rAezr1TYjoAigQACIoASUneuCbT9sVE9A0PASDoox2zpVR8b1q0SNQuVq9Y0DHSSuNFEbVSYMGGQLHlKtxP+cdZ00MMH94GKjxkyZsaWXYd0r2O5kgVx5tQJLF6xAQULFxX9Z06biAF9uov6K9pAhAoVSvx3bjok+O1n0HD8wbN3dk9zcoV0UL+AU5Xyp58i4venT4SxPAVYwoYNp3tdzO4wZEAvTJ00LkhFTGdJB5X3nj3jf8rZIUKEECnPeuLihXMoWTg34sdPiKNnroiuvbp1wPw5M4R/IH0EtYb0VqW3Ij0WzYjA0kFnTJ0g1FApBLNq/TYzLuXxY8jfp9d/vANChbPbRgcF11jLWrd+YwwfM8njcVfpoB7/CPgA4PEkMKDTPsvHw53VQdXHwHwEjJLAUcMHYfzoYUJ0oEv3PoYmSOXEKRPGCFnsNu074+aN66II3p2Ef5yVBFJ5jp5nIUOFwo17z4L0PPO7yF+/fEHieNHA/7VUi/z27RvyZk8vPNr6DRqOpi3aiq40qM+YOhGiRo2GizceGnpmtHR2NRLIzwHtB/oPHonC+bLixrWrQYrsaMHArDb5cqTHndu30LxVe/QZMDTAYZ2JBLIukGI89JNlUBSFQjta6gEtb07W/x04dg5JkiYX9jX0/qNICG1OtAZFZig2M2rcVNSq20BrtyDbBUQCWVeaLUNysYkwf8lqf/YjplzYAweR3rU7du1Blux57EIC//zjD/H9+OXLZxyhiFH8hB6ItO9bViTQ4x8BRQIlAkzvbNZ1DJ4+f+3rqWhcq7RPWqg7+wSqj4K5CBglgWtWLRMqhuUqVsG0WQsNTY5qiV07tRaS2DPmLEaZ8pWEgqKzeqXZcrPOSgJ5L0XyZ8O1K5exbvNuZMuRS/PtnTtzCmWK5xcvx3xJtgzpB8hTLZ6ikPjJ9lr91TRPJJCGrkYCLZ/582dPC5EdngIePnkRsePENQqHof7xYkQEyf3CZWtRuGgJpyeBAX1/2PKdIk9m6DVYv1FTH5P4Ww9f6towmT1jHn1a3QAAIABJREFUCqjIS3VQqoSaEQGRwM0b1qJZ4zqCQJBI8ARUhXEEuHZcwxEjR6FJ87Z2IYFSeKlQkeJYtFxbqrHxO3PuERQJdO71ceTsPP4k0CjYSh3UKILu1d8oCZQvqWa90EsisvfwGSRPkdK9wAZEcfsvEcPgxZ+fnO7eundui0XzZ4sTHp70aA35YhuYX2SjutWwY9tmoQJKNdAtG9ehaaPaKF22AmbOW6r1Mja3czUS6PdG27VsgtUrl6JUmfKYNX+ZzTjY2pHWPDxBowCUZaRKkxbjJ8/yd6rmTCeBtt6z337btmxEk/o1xKkf7TsqlimClKnTYPcBfQIvp04cQ4XShZEpSzZs2r7flOkFRAJlevbQkeMFaVVhDgJy07NmrdqYPH2uXUhguuTxQLVgvafM5tyhc46iSKBzrktwzEqRQIOoKxJoEEA3626UBPLFUNYv3bj/3DA6saP9KMZ48srbM83dwplJIC0baN2gl5zJutCADLW5fg8f3EfOzKnEUrLe8OTxI6JW0KtFG/QfNMLuS+zqJJBWLLkypxa1sivXbUXuvAXsjpnfC9y+dQNF8+fA58/emxfFS5bB3EUrA5yHO5LADx/+9jn9o5fmuFFDbarXYtp14jhRES5ceNx98saUdfRLAs+fO4PSRfOKDIqzl+/ghx+8v1NVGEfg5o1rKJg7M1KkSIkjJ8+bTgKlp2WKVKmx5+Ap4xN2kxEUCXSThTThNhQJNAiiIoEGAXSz7kZJIOGQO5fnr93Hr79Gtxkh1o7lypIGiRInFUIi7hjOTALv3rklaviix4iJc1e8lSm1BIU3Hj96iD2HTiNFSm+y5zdGDhuICWOGI2PmrELwZ8XShaLmzRFG6K5OAonltMnjMLh/L3E6zlNyR0fjetXBWkWmFVLYp2qNOoEajrsjCSTeNSqXAdObf4oYEX+9f49xk2agWs26upeiQK5MuHXzOvYdOSM+C0bDLwls1bQB1q9diTbtu6B77wFGh1f9/SAgxWEeP/8DIUKZK9YkhYPGT54pPmMqvBFQJFA9CRIBRQINPguKBBoE0M26m0ECy5cqhNMnj2PNpp3IkTOPzQjt2rEVDWpXCfKUwebBnaSjM5NAQpQ6aWxQmCAwk3e/MPKUKmOqhIgQ4SfcfPAiUJR5ApI7a1rQVJ6nIJ8+fcTsBctRsnQ5u6+MO5BAgsQNEm6UOIo8y4Xp0aUdFs6bJf65ct02Yf/AE+MT564HuHbuSgIpMNO/d1efe5YiMXof4NbNGmLdmhWYMHU2qlSrpbe7v/aWJPDpk8fImj6ZaMNTwBgxfzM8vhrANwIy1XbytFmoWLW2afDI+ml6T164/sC0cd1hIEUC3WEVzbkHRQIN4qhIoEEA3ay7GSSQL4RMJTSqeDdl4hgMHdgHLdt0RK9+g90Mae/bcXYSWLdGBezdvVOI/FDsx1ps3bwBXg1qilop1rAEFbQSYeqoDKaG0pLC3uEuJHDPru2oV7OSsI6QIjv2xm7yhNEYNqgvQoYMiYXL1vnYf1BchWbsASn3uisJlCflxDxChAi4+eClTfBL64YmzVoJ9WOjYUkCB/XrielTxqNCpWqYMnO+0aFV/wAQkPYgufPmw8p1203DqE718ti3Zxc6d+sNphyr+A8BRQLV0yARUCTQ4LOgSKBBAN2suxkkcOK4kRgxpD9atO6A3v2H2IyQJJO2plnZfGEHdnR2Eshap9EjBkPrC6p86WzXqTu69ugbKJJS8S6gBvY+2XIXEkjsZEpizToNMHr8VLs+uRvXrUYLr3riGnp8QN2NBNJmon1rL1y9fMkf3hTHmbtwpS4bm6OHD6BqhZLIniM31m7eZXgNJQm89+QNMqRMiL/+eo9dB04gVWplDm8Y3P8PwE0PiiNxs9NvRIwUSQgklShV1ubLseaWacJhwoQVqfiRo0SxeSx37KhIoDuuqm33pEigbbj59FIk0CCAbtbdDBK4ZdN6NG1YS6T2McXP1qDNAO0DHHVCZOs8jfRzdhIoU5JYu7d5xwGrtyp9syhlTknzoIIv03WrV8Tz57+LZnHixhPiIno926xOyk8DdyKBDx7cEyIxDDMNx/1ieuTQflSrWEr85269+qNth//SIK3h724kkPdLEjBm5GBhDyBD60aJX7ykmJa1FGprOMu/SxI4YvRE9Oza3jRyqfX6ntSOSrm9u3fC33//JW47Z668GDd5pq5NgIDwksrMtes2BAW2VPhGQJFA9URIBBQJNPgsKBJoEEA3624GCbx65RKK5s+OJEmT4cCx8zYjlCx+dPHjytoyviC5Yzg7CXz//h1SJIyJ0GHC4M7DV+J/A4vATOKDWjdpF8E2QSlMmrn27kQCiQtTppk6nTFTFmzeeRCPHj4w/BLKceU4169eETYGfBZsOXF0RxJIfLiJUaxADp9Hc+f+4zZvYMj6TgpgUQjLSJAE/v3nUxQuVATcJKDtChV+VdgHgWwZkuPJ40di8MZNW2Lg0NGGLvTowQPky5lBKO/aWmdqaAIu0FmRQBdYJAdNUZFAg0ArEmgQQDfrbgYJ5I9XwlhRhHLg45d/24TQ70+fIEu6pELIgIIG7hrOTgKJe6E8mXHj+jVs2LoXWbL999Lrd02kFH3CREmEkbm14GlKtozJMfD/dVBMrzp57oaQsrdnuBsJpFUEScSL589ECu6PEX4yRWWVBJ2+ciUK5cLLly9QrERpzFm4QtQD6gl3JYE0Cr9y6SIGDB2Ffj27IHXadDbX9NHIXRi6t2yLvgOH64HXX1uSwPIl8+PkyZP4LVZsIeqkd80MTcCDOnMjgJ+TUCFD4MGDB4Z/r/idWLVCCVy+dAH5CxbB0lUbPQhN7beqSKB2rNy9pSKBBldYkUCDALpZdzNIICFJnzIBXr18gbWbdiF7zty6UTq4fw9qVimrSWBE9+BO1MEVSGCXDi2xdNF89B04DM1atgsUvbmzpqFPj06oUr02JkzxVo8MKvgCFSnSzz6nVvw3Q6WDWkPO/9+laXX48D8gYeLEuk3L/Y7Il9GUiX9D7DhxxSkHTxnXbd6NMGHD6p6cu5JAWmRY1n0xNdAWiwgCKgV3kqdIhb2HT+vG2LLDtcuXUKRAdvGf7F1fa2iibtD52JGDSJ0mPfp0b4fVq1aiXaduaN6yvc0bWcuWLECX9i2F7criFRt8hJfcACpTb0GRQFPhdOnBFAk0uHyKBBoE0M26GyGBfIkfM2Iw3r17h7NnTuLTx4+InyARYsWOg0iRIold8oDUAwOCcM7MqejbszMaNmmOwcPHuhnK/92OK5BAaVhszTRe+pENHz0RdRs0cdo1c7eTQJKPlcuX4NLFc8KvjpEzd77/E+q0mk+nSPwoeMLP79s//wDTuhk0F8+VOx+69e5vE0F3VxJoxgNOgaRjRw754B06dGhkzZ5LDM30aK2+mZbfvffu3MKzZ7+L0z+e3EeJ8ouu714z7svTxpg1ZTT69+uL1u06o0efgbpun0RyzIghQIgQuHH9Ct68fo1w4cIhU5bsok6amRL2zo7QNWEnaKxIoBMsgpNMQZFAgwuhSKBBAN2suxESSCgCUs5jsTwFP/T8kEkvMmcnFEaX3xVIIJXq8ufMiJi/xcKZS7cDveWcmVPh4YP74hQqZeo0RqGxW393I4EEiqdSbVs09hGo4H/r2LUXOnXtpQtHEkGmtx07esinX7LkKTB5xnybCCAHUSQw6CXwq5RLdclOXXtrJoBydLO+e3U9MKqxQOD4oV2oXLE8ihQriQVL1+hGhUSwYd1qoEiQDJrDKwIYMJSKBOp+xNy2gyKBBpdWkUCDALpZd6MkUBJBS8GEa3d+10UAOUaV8iXAH8bVG7b7nGq4GdTidlyBBHKeTA0kQTh98ZaoM/Ibr169RPoU8fHjjxFw66FtfmmOWl93JIHEjjVqUq0ywk8/4eb9FzZB6lfwxOhnUJFA68tgKZBkRFWZ35n87pRhy3ev9dmqFn4ReP/mKVIkSyLSp1mDaUvwRL9Dm2aiK7NnTl24acswHtFHkUCPWGZNN6lIoCaYAm+kSKBBAN2suxkkkC+jmzasxfP/pySNmTBNd60MCQWJxbmr9xA9egw3Q/m/23EVElirajkc2Lcb02cvQtkKlf2tB0+iGterjlx58mPV+m1OvV7uSgKzZ0yB5MlT4fChffj06ZMuLz/LBStaIAeuXr6IRImT4PPnz6LuzYiJuSKBQX8cZP1l0qTJcevWDZF+e/vRK5s+Q9K4nKJcP/0UEQOHjtL93WvThT280y8RwyJqlJ/x4cPfYhOMm2F6o2aVMji4f69Qwg4ZKiTWbNxp8+m73mu7WntFAl1txew3X0UCDWKrSKBBAN2su1kkkOlMlM6mrHzXnv3QrmM3zUjRFoL2EGb5Zmm+cDA0dBUSKFPWWKNEsQm/IW0K2rTvgu69BwQDktov6Y4kkKd3Rw8fFCmEi+bPBn3GfokaVaTvhg0bTjM40hcyVOjQoF1BlMi/YNaMybrTSi0vqEhg0PDz9O7t27di04y+fqy/7D9kpE0EoGmj2tiycR0yZ86MxSs3Y8WyRbrTSjU/LKqhDwIkgXnz5MbpUyeEgFK2HN51nVqDGwFdO7YSm6eFihRD994DceXSBUXgAwFQkUCtT5b7t1Mk0OAaKxJoEEA3624GCZSQ1K5WDvv37hY1EqyV0Bonjx9FxTJFkDlrdmzctk9rN5ds5yokkOvI9cyUJRs2bd/vD+vKZYvh+LHDmL9kNYoW9zYVd9ZwRxJoiTWVBYsWyI5rVy6jfece6NK9j6aloM9jvpwZ8eD+XbTt2A3devbT1M9aI0UCrSHk/fdLF88LOw7W07Ku1pagXyQ3ZFq2aoVeA0bZMoTqYwMCJIHNmjXDwvlzMGzUBNRr6KV7lOGD+2HS+FGmfvZ0T8JFOigS6CIL5YBpKhJoEGRFAg0C6GbdzSSB8vSobYeu6Narv2akliyah64dWqFG7fpgKqk7h6uQwKBM479//46k8X7Fx4//4NKNR+IEypnD3UkgsZeejTwFPHLqkqgxshZ8AeWLKNsePH5epCWaEYoEakNRfo7os8qUQtp96A2mZDM1e+HChShcqore7qq9jQiQBE6YOAndu3QQBJBEUG/ITdPZC5aDdaEqAkdAkUD1dEgEFAk0+CwoEmgQQDfrbiYJlKdHufMWwMp1WzUj1b9PN8yaNgl9BgxF81btNfdzxYauQgKJbYFcmXDr5nVxOstTWhnyBIN2IEdPX3b6ZfAEEshFaNfKC6tXLBEnszyhDSpoNE/DeRrPz5y3FLQDMSsUCdSOpDxR5/clvzf1RtrkcYXFwM1btxAhin8BJ73jqfbaECAJ3LV7H8qVLoqs2XNi/ZY92jpatEqRKKZQBz1x7rqwhlChSKB6BqwjoEigdYyCbKFIoEEA3ay7mSRQnh5xR5s72/St0hJ1qpfHvj27sHDZWhQu+p/SnZa+rtbGlUhgx7bNsWLpQvQfNAJeLdr4QD1/zgxQkKJy1ZpCjMTZw1NIIMkAbTv++us9Vqzdgjz5Cga6NC296mPDulWiDduaGYoEakdzyIDemDpprMicYAaFnnj08AFyZEopTuJfvnyJZ28+6umu2hpAgCTw6fNXSBwvpk0Kyb8/fYIs6ZKC9iDX7z4zMBPP6KpOAj1jnbXcpSKBWlAKoo0igQYBdLPuZpJAQkN/OfrM6fGOo6DMk8ePxKkST5fcOVyJBMo03TLlK2HGnMU+y9KmeSOsXb0cQ0aMQ4PG3hLnzhyeQgK5BjxR58l6/PgJRYpn6DBh/C2NrMHl39iGbc0MRQK1o7lty0Y0qV/DJr85kniSeZ78bt+2WZFA7bAbbkkS+OHTN6RMGh88VadNBO0itMaObZuFP2fe/IWwfM1mrd08tp0igR679P5uXJFAg8+CIoEGAXSz7maTQHl6NGLMJNSp39gqWl8+f0aCWJGFouG9p39Ybe/qDVyJBF6/dhWF82YRPoH0C5TBNEKKiWzfexRp02Vw+iXxJBL47ds3FMqTRWzEUOiFgi+Wwb8XzptVpPky9Zop2GaHIoHaEeXpLVM6eZrH+lo90bdnZ8yZOVWo8w4Z2EeRQD3gGWwrSWCl8qWFlY7eLJbRIwZj3KihaNmmI3r1G2xwNu7fXZFA919jrXeoSKBWpAJpp0igQQDdrLvZJHDxwrno1rE1qtaog/GTZ1pF6+KFcyhZOLchhTyrF3GiBq5EAilckSpxLGH7QeuBmL/FEvVHfGnVm/IbnEvgSSSQOJ86cQwVShcWQi88XY8eI6YP/PNmT0fv7h3Ff+PfzBKDsVxfRQL1Pe0yE4KCPgkSJtbcuUyxfDh39rTw6axUrrgigZqRM95QksCe3btixtQJ6NFnIFq366x54Pq1KmP3zm2YNmshylVUgj7WgFMk0BpCnvN3RQINrrUigQYBdLPuZpPA61evoHC+rEicJJlINbMWa1YtQ9sWjcUPIX8Q3T1ciQRyLWpULgN6yc2cuwSly1XErh1b0aB2FeTImQdrNu10ieXyNBLIRWnWuA42b1gr1oxrx3j755/Cy5M1g5NnzEPFytXtsn6KBOqDtYVXPWxct1rU17LOVkvQ3iNxvGj4/u0b7jx6hXi/RVYkUAtwJrWRJHDRwgVo37qp+CzxM6U1MqVJLHwiD5+8iISJkmjt5rHtFAn02KX3d+OKBBp8FhQJNAigm3U3mwRa2gdcv/cMESNGChKxEUP6Y+K4kcKcumPXXm6Grv/bcTUSOGr4IIwfPQxNW7ZFv4HDhaUArQVate2Enn0HucR6eSIJtFT/pPBLqFChsG71CrDO01Y1Q62LrUigVqS8282aPhn9e3dF/UZNMXTkeE2dT588jvKlCokMir0HTyF6lHCKBGpCzpxGkgSeOn1GeD2mSJkKew6d1jT4q1cvkT5FfHEKf/vRK019PL2RIoGe/gT8d/+KBBp8FhQJNAigm3U3mwQSnmoVS+HIof1YtHwdChUpHiRiFEWgOML02YtQtkJlN0PX9Ung3t07ULdGRWERQauIKuVL4NiRg5izcAVKlCrrEuvliSSQCyN9AGPHjotw4cPj7h3vus4Dx84hSdLkdls7RQL1QXv29EmULVFA1NeyzlZLMAVxYN8eqFOvEUaNm6JIoBbQTGwjSeD7v/5Boji/iJEfPHunSRFbfqe6UjaFidDZNJQigTbB5padFAk0uKyKBBoE0M2624MEjhw2EBPGDEeHLj3RuVvvIBGTaqLcReVuqruHq50EMoUwVZJYCBcuPO4+eSNM4j98+NslTOLls+SpJJD3nytzajx4cM/nY9WkWSsMGDLKrh8zRQL1wxs72o+iE0+GtNRpynRf1l1Xr1lXkUD9kBvqIUngx8/ffBSxtf6GTRw7AiOGDhC2O7TfUWEdAUUCrWPkKS0UCTS40ooEGgTQzbrbgwTu2bUd9WpWQr4ChbFs9aYgEZMvP09efXAzZAO+HVcjgbwLSdRHjpuCrh1aIV78BDh25qrLrJcnksArly+ifWsvXL18yd86FS9ZBuMnz0Kkn3+2yxoqEqgfVp4E8kSQdbY8IbIWsqaMdddJkyZXJNAaYCb/3ZIENm1UG1s2rsOUmfNRoVI1q1fyalATWzdvwKTpc1GpSg2r7VUDQJFA9RRIBBQJNPgsKBJoEEA3624PEvjHmzdIkywOfvopIm7cfx4oYpSxJ8FgYTwL5D0hXJEEStuPZMlT4uaNa+LFhS8wrhKeSAK5Nu/evkXfXl2wavl/Ho/jJs1AtZp17bp0igTqh5fejvR4pF0AbQOCCgqKkARGjhIFV249QcgQIRQJ1A+5oR6WJJA106ydbtO+i7DrsBY5M6fCwwf3sf/oWSRNlsJac/V3KBKoHoL/EFAk0ODToEigQQDdrLs9SCAhyp01Le7fuyOK5QNL89yyaT2aNqwlzI7nL1ntZsgGfDuuSAIXL5iDbp3a+NzQoGFj0Mirhcusl6eSQC7QymWL0KFNM5+1unbnd7udAMqLKBKo/6NBdVCqhLLOlvW2QQVVX5kOWrBwUSxesUGRQP1wG+5hSQJ5qsfTvcJFSwi/wKDi/bt3SJEopkj5vfnghaYaQsOTdYMB1EmgGyyiSbegSKBBIBUJNAigm3W3Fwls17IJVq9citHjp6JmnQYBoiZrI1xJadLo8rsiCbx25TKK5M/mc+tbdx9G+gyZjELhsP6eTAIb1a2GuPHio26DJoJkeDVrrU4CHfbkab/Qk8ePhH1H1KjRcPHGwyA7UhCGwjCst2bdtToJ1I6zWS0tSeCD+3eRK0saxI4TFyfP3wjyEocP7kP1SqWRJVsObNi616zpuP04igS6/RJrvkFFAjVDFXBDRQINAuhm3e1FAhfMnYmeXdujRu36GDNhWoCotWraAOvXroQjUtScZdlckQTS9iNxnKj4/PkTQoUOjbuPXiN0mDDOAqnVeXgyCdy+dZMvFVcqu+bMnc8qZkYaqJNA29BLmzwu3rx+jeNnrwniHliUK1kQZ06dwNJVG5G/YBFFAm2D21AvSxLIgRLEiowvnz/j1sOX+PHHCIGOPWXiGAwd2AcNGjfDkBHjDM3BkzorEuhJqx30vSoSaPBZUCTQIIBu1t1eJPDSxfPCP4k1D6x9CCiKFcgBClhs3nEAGTNndTNkA74dVyKBrCmjuMi7d+9w8fxZ/P33XwgXLhwyZcmOSJEi2VVcxMyHwZNJoJk4ah1LkUCtSPlu17hedZC0ByUwIk3i+b/Sh1WdBNqGt5FefklgqSJ5cOH8WazbvBvZcuQKdOjmTepi0/o1YmOUG6QqtCGgSKA2nDyhlSKBBldZkUCDALpZd3uRQMKUJG40/PPPB1y/+wwRI/k3jefp0seP/wjxGIrIeEK4EgnkepAIMqXw2NFDPsuTM1dezF200u61ZWY9D4oEmoWktnEUCdSGk99WUyeNxZABvRGUjcf5s6dRulg+JE+REnsPnxFDKBJoG95GevklgZ3atcDyJQswYswk1KnfONCh82RLh3t3b2P3gZNImTqNkSl4VF9FAj1quYO8WUUCDT4LigQaBNDNutuTBFYuWwzHjx3GkpUbUaBQEV/IUR2NKmkxYv6Gs5fvuBmqgd+Oq5FA3smjhw+QI1NKn5vauf84UqdJ5zJrpkigY5dKkUDb8OZ3Jb8zM2TMjC27/tt0sRxt9owp6Neri680e0UCbcPbSC+/JJDKrlR4rdfQC8NGTQhwaG6IcmOUqfT3nvyhRGF0LIAigTrAcvOmigQaXGBFAg0C6Gbd7UkCuavN3e2OXXuhU9devpDbu3sH6taoiNx5C2Dluq1uhqp7kcBZ0ydj9ozJYg1nzZiMEqXK+VtPZ15ARQIduzqKBNqGN7Miksb7FSFDhcKdh68CrLuluA+VROnZWbtuQ3EhRQJtw9tIL78k8NCBvahRuYxIBWVKaEDBetwq5UsIUS2Ka6nQjoAigdqxcveWigQaXGFFAg0C6Gbd7UkCt23ZiCb1a4hTQJ4GWgbV7ahy52kF8q54EkgSWL1mXZH+yfTQFcsWwat5a5f5JCgS6NilUiTQdryLF8yJy5cuYP2WPciaPae/gbJnTIHHjx76st5RJNB2vG3t6ZcEvnz5AhlSJhCiMBSHCShmTpuIAX26o069RhgxdrKtl/bIfooEeuSyB3jTigQafBYUCTQIoJt1tycJpNIdFe8iRowkRAwsQ9ZQUCGNRNBTwhVJoKuvjSKBjl1BRQJtx7tHl3ZYOG8W+gwYiuat2vsaSH6fhg//gyAaIUOGFH9XJNB2vG3t6ZcEcpxUSWLh7Z9/CpsI2kX4jTbNG2Ht6uVW6wZtnZM791Mk0J1XV9+9KRKoDy9/rRUJNAigm3W3JwkkVHLnet+RM0iW/L+6MilzzlRQpoR6SigS6PiVViTQsZgrEmg73qtXLEG7Vl4oXbYCZs5b6msgmVmRJ19BrFi7xedvigTajretPQMigdUqlsKRQ/uFYTyN4/1GgVyZcOvmdVHvybpPFdoRUCRQO1bu3lKRQIMrrEigQQDdrLu9SWBLr/rYsG6VP0nsZPGjC8uBc1fuInqMmG6GauC3o0ig45dakUDHYq5IoO14UzmSCpK/RI2KSzce+RpI1li369QdXXv0VSTQdpgN9wyIBFKwh8I9PfoMROt2nX1dg6Iw/M1jUBTGlXxWDYNlwgCKBJoAopsMoUigwYVUJNAggG7W3d4kUKrZ1arbAKPGTRXovXjxHBlTJUSECD/h5oMXboZo0LejSKDjl1uRQMdirkigMbxTJIyJ9+/f4dSFm4gVO47PYJXKFMWJ40f8nTSpk0BjeNvSOyASuGzxfHRu3xIVK1fH5BnzfA176sQxVChdWNhC0B5ChT4EFAnUh5c7t1Yk0ODqKhJoEEA3625vEnjuzCmUKZ4fKVKmEmIGjKOHD6BqhZLIlCUbNm3f72aIKhLobAuqSKBjV0SRQGN4UzWZ6snTZy9C2QqVxWDfv38XyqFUEJUm8fIqigQaw9uW3gGRQOnhmCJVauw5eMrXsHNnTUOfHp1QrWZdjJs0w5ZLenQfRQI9evl93bwigQafBUUCDQLoZt3tTQK/fvmCxPGigf8rX17mz5mBXt06oHqtehg7cbqbIapIoLMtqCKBjl0RRQKN4T1+9DCMGj4IXi3aoP+gEWKwixfOoWTh3EiYKAkOn7zo6wKKBBrD25beAZHADx/+9rb4CBkSD5698+UD2KFNM6xctgiDho1BI68WtlzSo/soEujRy69IoJnLr0igmWi6/lj2JoFESIrALFu9CfkKFBYEkESwd/8haNG6g+uDqOMOVDqoDrBMaqpIoElAahxGkUCNQAXS7OD+PahZpayvTAm5cValWi1MmDpbkUBjEBvuHRAJ5KC5sqTBg/t3xUkgTwRlFMmfDdeuXMaGrXuRJVsOw9f3tAEUCfS0FQ/8ftVJoAU2Y2euQoI4MVGpVF6TXkSHAAAVw0lEQVRfiK3degh9Rs4R/610kRwY2LkRwocPK/6tSKD6MFki4AgSOKBvd8ycOhFduvdB+849IFXUFixdgyLFSnrUgigS6PjlViTQsZgrEmgMb9YDsi6Q4iHSNF7aCwwbNQH1GnopEmgMYsO9AyOBjetVx/atm0RNIGsDGcyCSRg7ivj/rIH/4YcfDV/f0wZQJNDTVlyRwCBX3JLkDera2BcJPHXhOsZOX4Wpw9sjys8RQaLI6Ni0qiKB6nPkDwFHkMDNG9aiWeM6KFSkGBYtX4+MqRPhxfNnOHr6MuInSORRq6JIoOOXW5FAx2KuSKBxvAvlyYwb16+JmmnWTssTph37jiFN2vSKBBqH2NAIgZHAkcMGYsKY4UIdlCqhDFkrmDRZCuw/etbQdT21syKBnrry/u9bnQRaYBLQSaDf/+aXFKqTQPVhskTAESTw96dPkCVdUmEaf+bybR+p7CevPnjcYigS6PglVyTQsZgrEmgcb6pMUm2y/+CRqFy1JtImjwu/JvHyKqom0DjeekcIjARuWr8GzZvUFT6B9AtkLJo/G907t0WlKjUwafpcvZdS7QEoEqgeA4mAIoFBkMCPHz+j7+i5yJEptc/p4N0HT9Fz+GwM7d4EieLHUumg6rPkCwFHkEBekC8xb16/FvUs7Vo2Qao0abFr/wmPWw1FAh2/5IoEOhZzRQKN4y3tBspVrCLIQ4PaVZAzV16s3rjD3+CKBBrHW+8IgZFAmsHTFD52nLg4ef6GGLZrh1ZYsmge+g0ajqYt2uq9lGqvSKB6BiwQUCRQAwmsWrYAsqZPIVr6JYGv3n5y2gcqfNhQ+OmH0Pj4+Rv++uer087TnSbGF7bv//6Lfz59s+ttNapfC5s2rEWu3Hlx9MghVKxcDTPnLLTrNZ1x8FChQiLSj6Hwx/svzjg9t5xT5J/C4K9/vuHrt+9ueX/OdlP8Hg8VKgT+Vt/hNi/N9etXkTdHJsSNGw+Vq9bA+LEj0bZ9Z/TpP9jfmCFChECUiGHw5t1nm6+nOupDgJunH798w+cvvr9TaOURN2ZkfP78GQ+evsaPP0ZAkQK5cOH8WazfvBO58+TTdyHVWiAQLkxIhA0TCu8/uP7vZrSfw6lVNYCAIoEaSGBQJ4Gfvzrvi1DIkCEQOmQIfP/+L75+/9fAY6K6akUgVMgQouk3O+M9Yfw4dO3S2Wdaffv1R6/efbRO023aEW2eBn5RhMRhaxomVEh8/f4d/6qvFIdgzu8UPufqO9wY3NF+iYz3798jdeo0uHLlMtasXY8yZcv6J4EAwoQOCWf+bTeGhPP1Dh2K7ykQG6h+I3vWLDh//hz2HzyEnDlzIVyYUKLJi1dv8PPPPzvfzbjAjHjaHTIk8PWb63+Jhw0d0gUQd94pKhIYBAnkn1RNoPM+vM44M0elg546cQwVShf2gcDSCNkZcbHXnFQ6qL2QDXxclQ7qWMxVOqg5eNeoXAaHDuz1GezSjUf4JWpUf4OrdFBz8NYzSmDpoByD5Q6rVy4FlVwp6lO8YE4hgEYhNBW2IaBqAm3DzR17KRJohQQqdVB3fOztd0+OIoGWpvG8m90HTiJl6jT2uzEnHVmRQMcvjCKBjsVckUBz8JZKkxwtbrz4OH72WoADKxJoDt56RgmKBE6fMh6D+vVE/UZNkTZdBlDkp0z5SpgxZ7GeS6i2FggoEqgeB4mAIoEALC0iCEysGFExY2QnIfzCUD6B6gOjFQFHkUDOp0ShXLh08byY2r2nfyBsWM/LjVckUOuTaV47RQLNw1LLSIoEakHJeps9u7ajXs1KomGFStUwZeZ8RQKtw+aQFkGRwP17d6N2tXLIniO3MIxfMHcmevYdhFZtOzlkbu54EUUC3XFVbbsnRQJtw82nl7KIMAigm3V3BAns16sLrly+hBvXr+LN61cIHTo0smbPhUiRImHAkFFil9tTQpFAx6+0IoGOxVyRQGN4X7l8Ef16dsG3799w8vhRMVjCRIkR87fYKF6yDLyat/Z1AXUSaAxvW3oHRQKfP/sdmdIkFqIwzHY5c+oElq/ZjLz5C9lyKdVHqYOqZ8ACAUUCDT4OigQaBNDNujuCBBIyEsHZM6b4oEeLiLkLV3oUAeTNKxLo+A+QIoGOxVyRQON4kwg2qlsNjx899BmMnoF+CSD/qEigcbz1jhAUCeRYqZLEwtv/tXd3oZaVZRzA11VIUCZKiiXGGGSNaQWhFIUXFqJIaZlZWJmOphlNfo5jShROMqUZiY2NY2pf2lBQolgUhN8RBElmUIaWWl1EWjfSVaxd+7DOcp+z99nvcx72u85vbsLOep/17t+zePf6r7X23s89t1T2sT8807xiv/3Wuhvb/1/AnUCHwlhACCw8FoTAQsCBDc8KgS3bqw546ZLeV752U/OB088YmOb0lyMETjeK3kIIjBZdvZ4QGOO9e9cNzec+e+momM8ExphGVZkWAt930rubRx5+YLS77m8GRu1/o9URAjdax1d+vUJg4bEgBBYCDmx4Vgi89567mq0XbGmuv2F3c/OuG5qX77tvc8u3vj8wzekvRwicbhS9hRAYLSoEZoi+69ijm81HHDX6bdWrrrik+cGPf9psPuLIF+3ancCMbizfx7QQeMVln2lu3XPTaNDxJ5zU7Ln9zvxJDmiPQuCAmln4UoTAQkAhsBBwYMMzQ+Db3v7OUfhr/7VXuU87/Yyl/x4Y64ovRwjM77QQmGvuTmC597+ef7556MH7RgGi/feXPz/VPPzgfROfnhACy73XWmFaCPz2bXuayy761KjsJduubLZefPlad2H7joAQ6HAYCwiBhceCEFgIOLDhWSFwYGxzvxwhcG66uQcKgXPTzTVQCJyLbe5BQuDcdHMPXCkEtk+8/O63jzbPPvN0873v3Daqf/L7T2s2bXpt84YjjlwK9XPveIMOFAI3aOMnvGwhsPBYEAILAQc2XAjMbagQmOvd7k0IzDUXAnO9hcBc73Zvq90JvHbn1c11O69eNqmzz/3k6Juw/ZtPQAicz22Io4TAwq4KgYWAAxsuBOY2VAjM9RYC872FwFxzITDXe1oIbP++9YJzmr13/O/H4duf9diIn3+P7IoQGKlZdy0hsLB/QmAh4MCGC4G5DRUCc72FwHxvITDXXAjM9Z4lBB795sObQw45dPSZ94cfur95/Im/5k9yQHsUAgfUzMKXIgQWAgqBhYADGy4E5jZUCMz1FgLzvYXAXHMhMNd7Wghsf+PxoQfuW/pNx/ZL0Nqf+Bh/yU/+bOvfoxBYfw+jXoEQWCgpBBYCDmy4EJjbUCEw11sIzPcWAnPNhcBc72khMH82w9+jEDj8Hs/6CoXAWaVW2E4ILAQc2HAhMLehQmCutxCY7y0E5poLgbneQmC+txCYb76oexQCCzsjBBYCDmy4EJjbUCEw11sIzPcWAnPNhcBcbyEw31sIzDdf1D0KgYWdEQILAQc2XAjMbagQmOstBOZ7C4G55kJgrrcQmO8tBOabL+oehcDCzgiBhYADGy4E5jZUCMz1FgLzvYXAXHMhMNdbCMz3FgLzzRd1j0JgYWeEwELAgQ0XAnMbKgTmeguB+d5CYK65EJjrLQTmewuB+eaLukchsLAzQmAh4MCGC4G5DRUCc72FwHxvITDXXAjM9RYC872FwHzzRd2jEFjYGSGwEHBgw4XA3IYKgbneQmC+txCYay4E5noLgfneQmC++aLuUQgs7IwQWAg4sOFCYG5DhcBcbyEw31sIzDUXAnO9hcB8byEw33xR9ygEFnZGCCwEHNhwITC3oUJgrrcQmO8tBOaaC4G53kJgvrcQmG++qHsUAgs7IwQWAg5suBCY21AhMNdbCMz3FgJzzYXAXG8hMN9bCMw3X9Q9CoGFnRECCwEHNlwIzG2oEJjrLQTmewuBueZCYK63EJjvLQTmmy/qHoXAws4IgYWAAxsuBOY2VAjM9RYC872FwFxzITDXWwjM9xYC880XdY9CYGFnhMBCwIENFwJzGyoE5noLgfneQmCuuRCY6y0E5nsLgfnmi7pHIXBRO2NeBAgQIECAAAECBAgQWAcBIXAdUJUkQIAAAQIECBAgQIDAogoIgYvaGfMiQIAAAQIECBAgQIDAOggIgeuAqiQBAgQIECBAgAABAgQWVUAIXNTOFM7rn8//uzl/2/XNo48/Map061e3NW896vDCqob3Bf701LPNl75+R7Pj8i3Nfvu+bOnPL7zwn+aqL9/S3P2zR0b/3xcuPas55YR3AJxToO856Zj+4T33N1fu3DPaw4nHHdN8/uKPN/vs85I592hY13PSMWyNWb9jpF1Xzr302ua8j75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       "            Plotly.purge(gd);\n",
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       "\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the data\n",
    "data = np.asarray([30,21,29,31,40,48,53,47,37,39,31,29,17,9,20,24,27,35,41,38,27,31,27,26,21,13,\n",
    "                   21,18,33,35,40,36,22,24,21,20,17,14,17,19,26,29,40,31,20,24,18,26,17,9,17,21,\n",
    "                   28,32,46,33,23,28,22,27,18,8,17,21,31,34,44,38,31,30,26,32])\n",
    "n = len(data)\n",
    "print(n)\n",
    "# plot the data\n",
    "fig = go.Figure(data=[go.Scatter(x=list(range(n)), y=data, mode='markers+lines',line={'color':'black'},\n",
    "                                 marker={'color':'black', 'symbol':'star'})], layout=go.Layout({'title':'Data'}))\n",
    "plyoff.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimally fit the single, double, and triple exponential smoothing to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global optimization settings\n",
    "meth = 'Nelder-Mead'\n",
    "maxIter = 500\n",
    "initCnt = 25\n",
    "np.random.seed(42)\n",
    "\n",
    "def MYOBJFUN(actl, pred):\n",
    "    #res = ME(actl, pred)\n",
    "    #res = MAE(actl, pred)\n",
    "    #res = MDE(actl, pred)\n",
    "    #res = MDAE(actl, pred)\n",
    "    #res = MAPE(actl, pred)\n",
    "    #res = SSE(actl, pred)\n",
    "    res = MSE(actl, pred)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully., objective=0.0000(array([1.2547094])), 18 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.04578574])), 11 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.13459061])), 15 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.04765235])), 16 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.14673701])), 20 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.14655972])), 20 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.17038479])), 21 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.08272018])), 13 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.05195127])), 16 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.0975125])), 15 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.07348138])), 23 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.01840534])), 10 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.0405533])), 13 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.22094989])), 19 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.04549356])), 19 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.05457593])), 19 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.01921151])), 18 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.12822633])), 17 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.1014598])), 17 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.20860093])), 19 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.07074257])), 16 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.02527988])), 20 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.21240029])), 19 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.22731218])), 18 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.16297846])), 17 iterations; best=0.0000\n",
      "Optimal value for alpha is 1.0000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>ES_Sing</th>\n",
       "      <th>ESErr_Sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data  ES_Sing  ESErr_Sing\n",
       "0  30.0     30.0         0.0\n",
       "1  21.0     21.0         0.0\n",
       "2  29.0     29.0         0.0\n",
       "3  31.0     31.0         0.0\n",
       "4  40.0     40.0         0.0"
      ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective=0.0000\n"
     ]
    }
   ],
   "source": [
    "''' single exponential smoothing '''\n",
    "# optimize alpha\n",
    "minMe = lambda a: MYOBJFUN(data, exponential_smoothing(data, min(1, max(0, a)))[:-1])\n",
    "\n",
    "# list of initial alphas to try\n",
    "alphas = np.random.rand(initCnt)\n",
    "optimS = [None]*initCnt\n",
    "best = np.inf\n",
    "bestOptimRes = None\n",
    "for init in range(initCnt):\n",
    "    # run the optimization\n",
    "    optimRes = minimize(minMe, x0=alphas[init], method=meth, options={'maxiter':maxIter})\n",
    "    # is it better than the current best?\n",
    "    if optimRes.success & (optimRes.fun < best):\n",
    "        bestOptimRes = optimRes\n",
    "        best = optimRes.fun\n",
    "        # talk\n",
    "    print('%s, objective=%0.4f(%r), %d iterations; best=%0.4f'%\\\n",
    "          (optimRes.message, optimRes.fun, optimRes.x, optimRes.nit, best)) \n",
    "if bestOptimRes.success:\n",
    "    alpha = min(1, max(0, bestOptimRes.x))\n",
    "    print('Optimal value for alpha is %0.4f'%alpha)\n",
    "else:\n",
    "    alpha = 0.5\n",
    "    print(bestOptimRes)\n",
    "    \n",
    "# execute\n",
    "smoothed = exponential_smoothing(data, alpha)\n",
    "\n",
    "# store results & compute error\n",
    "single = pd.DataFrame(data, columns=['Data']).append(pd.Series(dtype=float), ignore_index=True)\n",
    "single['ES_Sing'] = smoothed\n",
    "single['ESErr_Sing'] = single['Data'] - single.ES_Sing\n",
    "display(single.head())\n",
    "print('Objective=%0.4f'%MYOBJFUN(data, smoothed[:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully., objective=0.0000(array([4.5238097 , 1.04146257])), 27 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.5672098, 1.1066119])), 24 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([2.07614321, 1.31439219])), 43 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([13.77062336,  1.02160342])), 33 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.02005195, 1.03805444])), 15 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([3.50231835, 1.06912294])), 26 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.29008493, 1.27870536])), 39 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([14.14594881,  1.28309677])), 32 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([48.74996431,  1.13447677])), 37 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([2.47576688, 1.09556931])), 25 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([4.25295311, 1.11881142])), 27 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.58498856, 1.02123772])), 24 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.93477988, 1.01768512])), 36 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.00282809, 1.06280818])), 17 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.05981114, 1.0033509 ])), 19 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([6.85310863, 1.20430172])), 29 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([26.52364464,  1.18598779])), 35 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.57226049, 1.09558411])), 34 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.01066915, 1.35608649])), 22 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.21637555, 1.04801741])), 34 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([8.8199508 , 1.19984454])), 31 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([14.93610088,  1.1992716 ])), 32 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.05473541, 1.08596921])), 18 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([216.10295834,   1.12457541])), 42 iterations; best=0.0000\n",
      "Optimization terminated successfully., objective=0.0000(array([1.13654937, 1.06470387])), 19 iterations; best=0.0000\n",
      "Optimal values: alpha=1.0000, beta=1.0000\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Level_Doub</th>\n",
       "      <th>Trend_Doub</th>\n",
       "      <th>ES_Doub</th>\n",
       "      <th>ESErr_Doub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data  Level_Doub  Trend_Doub  ES_Doub  ESErr_Doub\n",
       "0  30.0        30.0        -9.0     30.0         0.0\n",
       "1  21.0        21.0        -9.0     21.0         0.0\n",
       "2  29.0        29.0         8.0     29.0         0.0\n",
       "3  31.0        31.0         2.0     31.0         0.0\n",
       "4  40.0        40.0         9.0     40.0         0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective=0.0000\n"
     ]
    }
   ],
   "source": [
    "''' double exponential smoothing'''\n",
    "# optimize alpha & beta\n",
    "minMe = lambda ab: MYOBJFUN(data, double_exponential_smoothing(data,\n",
    "                                    min(1, max(0, ab[0])), min(1, max(0, ab[1])), m=0)[-1])\n",
    "\n",
    "# list of initial alphas & betas to try\n",
    "alphabetas = np.random.rand(initCnt, 2)\n",
    "optimS = [None]*initCnt\n",
    "best = np.inf\n",
    "bestOptimRes = None\n",
    "for init in range(initCnt):\n",
    "    # run the optimization\n",
    "    optimRes = minimize(minMe, x0=alphabetas[init], method=meth, options={'maxiter':maxIter})\n",
    "    # is it better than the current best?\n",
    "    if optimRes.success & (optimRes.fun < best):\n",
    "        bestOptimRes = optimRes\n",
    "        best = optimRes.fun\n",
    "        # talk\n",
    "    print('%s, objective=%0.4f(%r), %d iterations; best=%0.4f'%\\\n",
    "          (optimRes.message, optimRes.fun, optimRes.x, optimRes.nit, best))        \n",
    "if bestOptimRes.success:\n",
    "    alpha = min(1, max(0, bestOptimRes.x[0]))\n",
    "    beta = min(1, max(0, bestOptimRes.x[1]))\n",
    "    print('Optimal values: alpha=%0.4f, beta=%0.4f'%(alpha, beta))\n",
    "else:\n",
    "    alpha, beta = (0.5, 0.5)\n",
    "    print(bestOptimRes)\n",
    "    \n",
    "# execute\n",
    "forecasts = 2\n",
    "l,t,s = double_exponential_smoothing(data, alpha, beta, forecasts)\n",
    "\n",
    "# store results & compute error\n",
    "double = pd.DataFrame(data, columns=['Data'])\n",
    "for i in range(forecasts):\n",
    "    double = double.append(pd.Series(dtype=float), ignore_index=True)\n",
    "double['Level_Doub'] = l\n",
    "double['Trend_Doub'] = t\n",
    "double['ES_Doub'] = s\n",
    "double['ESErr_Doub'] = double['Data'] - double.ES_Doub\n",
    "display(double.head())\n",
    "print('Objective=%0.4f'%MYOBJFUN(data, s[:-forecasts]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully., objective=0.1040(array([0.96738295, 0.07130334, 0.07603036])), 50 iterations; best=0.1040\n",
      "Optimization terminated successfully., objective=0.1039(array([ 9.69710478e-01,  7.13274188e-02, -6.10726137e-04])), 155 iterations; best=0.1039\n",
      "Optimization terminated successfully., objective=0.1115(array([1.38700476, 0.06408549, 0.07506364])), 43 iterations; best=0.1039\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.96971148,  0.07132893, -0.02641222])), 146 iterations; best=0.1039\n",
      "Optimization terminated successfully., objective=0.1042(array([0.93253567, 0.07125179, 0.58196267])), 66 iterations; best=0.1039\n",
      "Optimization terminated successfully., objective=0.0884(array([0.00784602, 1.03099146, 4.39492835])), 157 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.96971152,  0.07132919, -0.01201087])), 148 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1115(array([1.25654113, 0.06409513, 0.0289839 ])), 44 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.0884(array([0.00784605, 1.02211832, 5.40083532])), 126 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.9697115 ,  0.07132898, -0.00756962])), 160 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1100(array([0.74363226, 0.06700695, 1.05831284])), 92 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.9697115 ,  0.07132889, -0.00726172])), 124 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1115(array([ 1.07740974,  0.06407731, -4.66464077])), 62 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.96971165,  0.07132862, -0.01576156])), 131 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.6072(array([ 1.08556134, -0.61983264, -1.28443356])), 30 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.96971143,  0.07132889, -0.00689671])), 119 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.6072(array([ 0.17608308, -0.1367611 ,  1.01794023])), 28 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([0.96943706, 0.0713645 , 0.00916302])), 50 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1115(array([1.17149712, 0.06408986, 0.2586749 ])), 45 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.0884(array([0.0078461 , 1.00591061, 3.87210798])), 140 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.96971157,  0.07132862, -0.00456165])), 149 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=2.1220(array([ 0.63244649,  1.16190795, -0.22968099])), 49 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.96971197,  0.07132891, -0.00609625])), 179 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1100(array([0.74363377, 0.0670069 , 3.7452726 ])), 75 iterations; best=0.0884\n",
      "Optimization terminated successfully., objective=0.1039(array([ 0.96971095,  0.07132896, -0.00337613])), 104 iterations; best=0.0884\n",
      "Optimal values: alpha=0.0078, beta=1.0000, gamma=1.0000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Level_Trip</th>\n",
       "      <th>Trend_Trip</th>\n",
       "      <th>Seasn_Trip</th>\n",
       "      <th>ES_Trip</th>\n",
       "      <th>ESErr_Trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>-0.784722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.0</td>\n",
       "      <td>29.269274</td>\n",
       "      <td>-0.730726</td>\n",
       "      <td>-8.269274</td>\n",
       "      <td>20.269274</td>\n",
       "      <td>0.730726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>28.599160</td>\n",
       "      <td>-0.670113</td>\n",
       "      <td>0.400840</td>\n",
       "      <td>28.329887</td>\n",
       "      <td>0.670113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.0</td>\n",
       "      <td>27.993135</td>\n",
       "      <td>-0.606026</td>\n",
       "      <td>3.006865</td>\n",
       "      <td>30.393974</td>\n",
       "      <td>0.606026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>27.459372</td>\n",
       "      <td>-0.533763</td>\n",
       "      <td>12.540628</td>\n",
       "      <td>39.466237</td>\n",
       "      <td>0.533763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data  Level_Trip  Trend_Trip  Seasn_Trip    ES_Trip  ESErr_Trip\n",
       "0  30.0   30.000000   -0.784722    0.000000  30.000000    0.000000\n",
       "1  21.0   29.269274   -0.730726   -8.269274  20.269274    0.730726\n",
       "2  29.0   28.599160   -0.670113    0.400840  28.329887    0.670113\n",
       "3  31.0   27.993135   -0.606026    3.006865  30.393974    0.606026\n",
       "4  40.0   27.459372   -0.533763   12.540628  39.466237    0.533763"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective=0.0884\n"
     ]
    }
   ],
   "source": [
    "''' triple exponential smoothing'''\n",
    "L = 12 # season length\n",
    "# optimize alpha, beta, and gamma\n",
    "minMe = lambda abg: MYOBJFUN(data, triple_exponential_smoothing(data, \n",
    "                        min(1, max(0, abg[0])), min(1, max(0, abg[1])), min(1, max(0, abg[2])), L=L , m=0)[-1])\n",
    "\n",
    "# list of initial alphas, betas, and gammas to try\n",
    "alphabetagammas = np.random.rand(initCnt, 3)\n",
    "optimS = [None]*initCnt\n",
    "best = np.inf\n",
    "bestOptimRes = None\n",
    "for init in range(initCnt):\n",
    "    # run the optimization\n",
    "    optimRes = minimize(minMe, x0=alphabetagammas[init], method=meth, options={'maxiter':maxIter})\n",
    "    # is it better than the current best?\n",
    "    if optimRes.success & (optimRes.fun < best):\n",
    "        bestOptimRes = optimRes\n",
    "        best = optimRes.fun\n",
    "        # talk\n",
    "    print('%s, objective=%0.4f(%r), %d iterations; best=%0.4f'%\\\n",
    "          (optimRes.message, optimRes.fun, optimRes.x, optimRes.nit, best)) \n",
    "if bestOptimRes.success:\n",
    "    alpha = min(1, max(0, bestOptimRes.x[0]))\n",
    "    beta = min(1, max(0, bestOptimRes.x[1]))\n",
    "    gamma = min(1, max(0, bestOptimRes.x[2]))\n",
    "    print('Optimal values: alpha=%0.4f, beta=%0.4f, gamma=%0.4f'%(alpha, beta, gamma))\n",
    "else:\n",
    "    alpha, beta, gamma = (0.5, 0.5, 0.5)\n",
    "    print(bestOptimRes)\n",
    "\n",
    "# execute\n",
    "#alpha = 0.716; beta = 0.029; gamma = 0.993 # values which supposedly are optimal, but increase the MSE/SSE\n",
    "moreforecasts = 24\n",
    "l,t,s,sl,p = triple_exponential_smoothing(data, alpha, beta, gamma, L, moreforecasts)\n",
    "\n",
    "# store results & compute error\n",
    "triple = pd.DataFrame(data, columns=['Data'])\n",
    "for i in range(moreforecasts):\n",
    "    triple = triple.append(pd.Series(dtype=float), ignore_index=True)\n",
    "triple['Level_Trip'] = l\n",
    "triple['Trend_Trip'] = t\n",
    "triple['Seasn_Trip'] = s\n",
    "triple['ES_Trip'] = p\n",
    "triple['ESErr_Trip'] = triple['Data'] - triple.ES_Trip\n",
    "display(triple.head())\n",
    "print('Objective=%0.4f'%MYOBJFUN(data, p[:-moreforecasts]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate and compare the smoothing models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>ES_Sing</th>\n",
       "      <th>ESErr_Sing</th>\n",
       "      <th>Level_Doub</th>\n",
       "      <th>Trend_Doub</th>\n",
       "      <th>ES_Doub</th>\n",
       "      <th>ESErr_Doub</th>\n",
       "      <th>Level_Trip</th>\n",
       "      <th>Trend_Trip</th>\n",
       "      <th>Seasn_Trip</th>\n",
       "      <th>ES_Trip</th>\n",
       "      <th>ESErr_Trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>-0.784722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.269274</td>\n",
       "      <td>-0.730726</td>\n",
       "      <td>-8.269274</td>\n",
       "      <td>20.269274</td>\n",
       "      <td>0.730726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.599160</td>\n",
       "      <td>-0.670113</td>\n",
       "      <td>0.400840</td>\n",
       "      <td>28.329887</td>\n",
       "      <td>0.670113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.993135</td>\n",
       "      <td>-0.606026</td>\n",
       "      <td>3.006865</td>\n",
       "      <td>30.393974</td>\n",
       "      <td>0.606026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.459372</td>\n",
       "      <td>-0.533763</td>\n",
       "      <td>12.540628</td>\n",
       "      <td>39.466237</td>\n",
       "      <td>0.533763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.027646</td>\n",
       "      <td>-0.431726</td>\n",
       "      <td>20.972354</td>\n",
       "      <td>47.568274</td>\n",
       "      <td>0.431726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.673083</td>\n",
       "      <td>-0.354563</td>\n",
       "      <td>26.326917</td>\n",
       "      <td>52.645437</td>\n",
       "      <td>0.354563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.404398</td>\n",
       "      <td>-0.268685</td>\n",
       "      <td>20.595602</td>\n",
       "      <td>46.731315</td>\n",
       "      <td>0.268685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.226947</td>\n",
       "      <td>-0.177451</td>\n",
       "      <td>10.773053</td>\n",
       "      <td>36.822549</td>\n",
       "      <td>0.177451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.136177</td>\n",
       "      <td>-0.090770</td>\n",
       "      <td>12.863823</td>\n",
       "      <td>38.909230</td>\n",
       "      <td>0.090770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.109890</td>\n",
       "      <td>-0.026288</td>\n",
       "      <td>4.890110</td>\n",
       "      <td>30.973712</td>\n",
       "      <td>0.026288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.112478</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>2.887522</td>\n",
       "      <td>29.002588</td>\n",
       "      <td>-0.002588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.101849</td>\n",
       "      <td>-0.010629</td>\n",
       "      <td>-9.101849</td>\n",
       "      <td>16.989371</td>\n",
       "      <td>0.010629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.022003</td>\n",
       "      <td>-0.079846</td>\n",
       "      <td>-17.022003</td>\n",
       "      <td>8.920154</td>\n",
       "      <td>0.079846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.892390</td>\n",
       "      <td>-0.129613</td>\n",
       "      <td>-5.892390</td>\n",
       "      <td>19.870387</td>\n",
       "      <td>0.129613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.725355</td>\n",
       "      <td>-0.167036</td>\n",
       "      <td>-1.725355</td>\n",
       "      <td>23.832964</td>\n",
       "      <td>0.167036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.471236</td>\n",
       "      <td>-0.254118</td>\n",
       "      <td>1.528764</td>\n",
       "      <td>26.745882</td>\n",
       "      <td>0.254118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.129325</td>\n",
       "      <td>-0.341911</td>\n",
       "      <td>9.870675</td>\n",
       "      <td>34.658089</td>\n",
       "      <td>0.341911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.708057</td>\n",
       "      <td>-0.421268</td>\n",
       "      <td>16.291943</td>\n",
       "      <td>40.578732</td>\n",
       "      <td>0.421268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.232789</td>\n",
       "      <td>-0.475268</td>\n",
       "      <td>13.767211</td>\n",
       "      <td>37.524732</td>\n",
       "      <td>0.475268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.698436</td>\n",
       "      <td>-0.534353</td>\n",
       "      <td>3.301564</td>\n",
       "      <td>26.465647</td>\n",
       "      <td>0.534353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.124634</td>\n",
       "      <td>-0.573802</td>\n",
       "      <td>7.875366</td>\n",
       "      <td>30.426198</td>\n",
       "      <td>0.573802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.547373</td>\n",
       "      <td>-0.577261</td>\n",
       "      <td>4.452627</td>\n",
       "      <td>26.422739</td>\n",
       "      <td>0.577261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.979075</td>\n",
       "      <td>-0.568298</td>\n",
       "      <td>4.020925</td>\n",
       "      <td>25.431702</td>\n",
       "      <td>0.568298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.478967</td>\n",
       "      <td>-0.500108</td>\n",
       "      <td>-0.478967</td>\n",
       "      <td>20.499892</td>\n",
       "      <td>0.500108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.049811</td>\n",
       "      <td>-0.429155</td>\n",
       "      <td>-8.049811</td>\n",
       "      <td>12.570845</td>\n",
       "      <td>0.429155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.669864</td>\n",
       "      <td>-0.379947</td>\n",
       "      <td>0.330136</td>\n",
       "      <td>20.620053</td>\n",
       "      <td>0.379947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.285487</td>\n",
       "      <td>-0.384377</td>\n",
       "      <td>-2.285487</td>\n",
       "      <td>17.615623</td>\n",
       "      <td>0.384377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.991890</td>\n",
       "      <td>-0.293597</td>\n",
       "      <td>13.008110</td>\n",
       "      <td>32.706403</td>\n",
       "      <td>0.293597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.740904</td>\n",
       "      <td>-0.250985</td>\n",
       "      <td>15.259096</td>\n",
       "      <td>34.749015</td>\n",
       "      <td>0.250985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.523014</td>\n",
       "      <td>-0.217890</td>\n",
       "      <td>20.476986</td>\n",
       "      <td>39.782110</td>\n",
       "      <td>0.217890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.328095</td>\n",
       "      <td>-0.194919</td>\n",
       "      <td>16.671905</td>\n",
       "      <td>35.805081</td>\n",
       "      <td>0.194919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.129765</td>\n",
       "      <td>-0.198330</td>\n",
       "      <td>2.870235</td>\n",
       "      <td>21.801670</td>\n",
       "      <td>0.198330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.909412</td>\n",
       "      <td>-0.220352</td>\n",
       "      <td>5.090588</td>\n",
       "      <td>23.779648</td>\n",
       "      <td>0.220352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.672256</td>\n",
       "      <td>-0.237156</td>\n",
       "      <td>2.327744</td>\n",
       "      <td>20.762844</td>\n",
       "      <td>0.237156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.415830</td>\n",
       "      <td>-0.256426</td>\n",
       "      <td>1.584170</td>\n",
       "      <td>19.743574</td>\n",
       "      <td>0.256426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.154065</td>\n",
       "      <td>-0.261765</td>\n",
       "      <td>-1.154065</td>\n",
       "      <td>16.738235</td>\n",
       "      <td>0.261765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.924920</td>\n",
       "      <td>-0.229145</td>\n",
       "      <td>-3.924920</td>\n",
       "      <td>13.770855</td>\n",
       "      <td>0.229145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.687726</td>\n",
       "      <td>-0.237194</td>\n",
       "      <td>-0.687726</td>\n",
       "      <td>16.762806</td>\n",
       "      <td>0.237194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.480620</td>\n",
       "      <td>-0.207105</td>\n",
       "      <td>1.519380</td>\n",
       "      <td>18.792895</td>\n",
       "      <td>0.207105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.239921</td>\n",
       "      <td>-0.240699</td>\n",
       "      <td>8.760079</td>\n",
       "      <td>25.759301</td>\n",
       "      <td>0.240699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.973658</td>\n",
       "      <td>-0.266264</td>\n",
       "      <td>12.026342</td>\n",
       "      <td>28.733736</td>\n",
       "      <td>0.266264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.729485</td>\n",
       "      <td>-0.244172</td>\n",
       "      <td>23.270515</td>\n",
       "      <td>39.755828</td>\n",
       "      <td>0.244172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.468387</td>\n",
       "      <td>-0.261098</td>\n",
       "      <td>14.531613</td>\n",
       "      <td>30.738902</td>\n",
       "      <td>0.261098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.214527</td>\n",
       "      <td>-0.253860</td>\n",
       "      <td>3.785473</td>\n",
       "      <td>19.746140</td>\n",
       "      <td>0.253860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.983803</td>\n",
       "      <td>-0.230724</td>\n",
       "      <td>8.016197</td>\n",
       "      <td>23.769276</td>\n",
       "      <td>0.230724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.752445</td>\n",
       "      <td>-0.231358</td>\n",
       "      <td>2.247555</td>\n",
       "      <td>17.768642</td>\n",
       "      <td>0.231358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.590874</td>\n",
       "      <td>-0.161570</td>\n",
       "      <td>10.409126</td>\n",
       "      <td>25.838430</td>\n",
       "      <td>0.161570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.450683</td>\n",
       "      <td>-0.140192</td>\n",
       "      <td>1.549317</td>\n",
       "      <td>16.859808</td>\n",
       "      <td>0.140192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.291774</td>\n",
       "      <td>-0.158909</td>\n",
       "      <td>-6.291774</td>\n",
       "      <td>8.841091</td>\n",
       "      <td>0.158909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.152911</td>\n",
       "      <td>-0.138863</td>\n",
       "      <td>1.847089</td>\n",
       "      <td>16.861137</td>\n",
       "      <td>0.138863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.049092</td>\n",
       "      <td>-0.103818</td>\n",
       "      <td>5.950908</td>\n",
       "      <td>20.896182</td>\n",
       "      <td>0.103818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.978970</td>\n",
       "      <td>-0.070123</td>\n",
       "      <td>13.021030</td>\n",
       "      <td>27.929877</td>\n",
       "      <td>0.070123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.948586</td>\n",
       "      <td>-0.030384</td>\n",
       "      <td>17.051414</td>\n",
       "      <td>31.969616</td>\n",
       "      <td>0.030384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.979489</td>\n",
       "      <td>0.030903</td>\n",
       "      <td>31.020511</td>\n",
       "      <td>46.030903</td>\n",
       "      <td>-0.030903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.037524</td>\n",
       "      <td>0.058035</td>\n",
       "      <td>17.962476</td>\n",
       "      <td>33.058035</td>\n",
       "      <td>-0.058035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.127877</td>\n",
       "      <td>0.090352</td>\n",
       "      <td>7.872123</td>\n",
       "      <td>23.090352</td>\n",
       "      <td>-0.090352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.255620</td>\n",
       "      <td>0.127743</td>\n",
       "      <td>12.744380</td>\n",
       "      <td>28.127743</td>\n",
       "      <td>-0.127743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.417643</td>\n",
       "      <td>0.162023</td>\n",
       "      <td>6.582357</td>\n",
       "      <td>22.162023</td>\n",
       "      <td>-0.162023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.587600</td>\n",
       "      <td>0.169957</td>\n",
       "      <td>11.412400</td>\n",
       "      <td>27.169957</td>\n",
       "      <td>-0.169957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.762996</td>\n",
       "      <td>0.175395</td>\n",
       "      <td>2.237004</td>\n",
       "      <td>18.175395</td>\n",
       "      <td>-0.175395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.925472</td>\n",
       "      <td>0.162476</td>\n",
       "      <td>-7.925472</td>\n",
       "      <td>8.162476</td>\n",
       "      <td>-0.162476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.080611</td>\n",
       "      <td>0.155140</td>\n",
       "      <td>0.919389</td>\n",
       "      <td>17.155140</td>\n",
       "      <td>-0.155140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.226440</td>\n",
       "      <td>0.145829</td>\n",
       "      <td>4.773560</td>\n",
       "      <td>21.145829</td>\n",
       "      <td>-0.145829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.384876</td>\n",
       "      <td>0.158435</td>\n",
       "      <td>14.615124</td>\n",
       "      <td>31.158435</td>\n",
       "      <td>-0.158435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.546491</td>\n",
       "      <td>0.161615</td>\n",
       "      <td>17.453509</td>\n",
       "      <td>34.161615</td>\n",
       "      <td>-0.161615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.678851</td>\n",
       "      <td>0.132360</td>\n",
       "      <td>27.321149</td>\n",
       "      <td>44.132360</td>\n",
       "      <td>-0.132360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.836525</td>\n",
       "      <td>0.157674</td>\n",
       "      <td>21.163475</td>\n",
       "      <td>38.157674</td>\n",
       "      <td>-0.157674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.042324</td>\n",
       "      <td>0.205799</td>\n",
       "      <td>13.957676</td>\n",
       "      <td>31.205799</td>\n",
       "      <td>-0.205799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.248182</td>\n",
       "      <td>0.205858</td>\n",
       "      <td>12.751818</td>\n",
       "      <td>30.205858</td>\n",
       "      <td>-0.205858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.469446</td>\n",
       "      <td>0.221264</td>\n",
       "      <td>8.530554</td>\n",
       "      <td>26.221264</td>\n",
       "      <td>-0.221264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>14.286560</td>\n",
       "      <td>32.243993</td>\n",
       "      <td>-0.243993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>2.237004</td>\n",
       "      <td>20.194437</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>-7.925472</td>\n",
       "      <td>10.275955</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>0.919389</td>\n",
       "      <td>19.364808</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>4.773560</td>\n",
       "      <td>23.462973</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>14.615124</td>\n",
       "      <td>33.548531</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>17.453509</td>\n",
       "      <td>36.630909</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>27.321149</td>\n",
       "      <td>46.742542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>21.163475</td>\n",
       "      <td>40.828861</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>13.957676</td>\n",
       "      <td>33.867055</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>12.751818</td>\n",
       "      <td>32.905191</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>8.530554</td>\n",
       "      <td>28.927920</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>14.286560</td>\n",
       "      <td>34.927920</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>2.237004</td>\n",
       "      <td>23.122357</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>-7.925472</td>\n",
       "      <td>13.203875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>0.919389</td>\n",
       "      <td>22.292728</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>4.773560</td>\n",
       "      <td>26.390892</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>14.615124</td>\n",
       "      <td>36.476450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>17.453509</td>\n",
       "      <td>39.558829</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>27.321149</td>\n",
       "      <td>49.670462</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>21.163475</td>\n",
       "      <td>43.756781</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>13.957676</td>\n",
       "      <td>36.794975</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>12.751818</td>\n",
       "      <td>35.833111</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>8.530554</td>\n",
       "      <td>31.855840</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.713440</td>\n",
       "      <td>0.243993</td>\n",
       "      <td>14.286560</td>\n",
       "      <td>37.855840</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Data  ES_Sing  ESErr_Sing  Level_Doub  Trend_Doub  ES_Doub  ESErr_Doub  \\\n",
       "0   30.0     30.0         0.0        30.0        -9.0     30.0         0.0   \n",
       "1   21.0     21.0         0.0        21.0        -9.0     21.0         0.0   \n",
       "2   29.0     29.0         0.0        29.0         8.0     29.0         0.0   \n",
       "3   31.0     31.0         0.0        31.0         2.0     31.0         0.0   \n",
       "4   40.0     40.0         0.0        40.0         9.0     40.0         0.0   \n",
       "5   48.0     48.0         0.0        48.0         8.0     48.0         0.0   \n",
       "6   53.0     53.0         0.0        53.0         5.0     53.0         0.0   \n",
       "7   47.0     47.0         0.0        47.0        -6.0     47.0         0.0   \n",
       "8   37.0     37.0         0.0        37.0       -10.0     37.0         0.0   \n",
       "9   39.0     39.0         0.0        39.0         2.0     39.0         0.0   \n",
       "10  31.0     31.0         0.0        31.0        -8.0     31.0         0.0   \n",
       "11  29.0     29.0         0.0        29.0        -2.0     29.0         0.0   \n",
       "12  17.0     17.0         0.0        17.0       -12.0     17.0         0.0   \n",
       "13   9.0      9.0         0.0         9.0        -8.0      9.0         0.0   \n",
       "14  20.0     20.0         0.0        20.0        11.0     20.0         0.0   \n",
       "15  24.0     24.0         0.0        24.0         4.0     24.0         0.0   \n",
       "16  27.0     27.0         0.0        27.0         3.0     27.0         0.0   \n",
       "17  35.0     35.0         0.0        35.0         8.0     35.0         0.0   \n",
       "18  41.0     41.0         0.0        41.0         6.0     41.0         0.0   \n",
       "19  38.0     38.0         0.0        38.0        -3.0     38.0         0.0   \n",
       "20  27.0     27.0         0.0        27.0       -11.0     27.0         0.0   \n",
       "21  31.0     31.0         0.0        31.0         4.0     31.0         0.0   \n",
       "22  27.0     27.0         0.0        27.0        -4.0     27.0         0.0   \n",
       "23  26.0     26.0         0.0        26.0        -1.0     26.0         0.0   \n",
       "24  21.0     21.0         0.0        21.0        -5.0     21.0         0.0   \n",
       "25  13.0     13.0         0.0        13.0        -8.0     13.0         0.0   \n",
       "26  21.0     21.0         0.0        21.0         8.0     21.0         0.0   \n",
       "27  18.0     18.0         0.0        18.0        -3.0     18.0         0.0   \n",
       "28  33.0     33.0         0.0        33.0        15.0     33.0         0.0   \n",
       "29  35.0     35.0         0.0        35.0         2.0     35.0         0.0   \n",
       "30  40.0     40.0         0.0        40.0         5.0     40.0         0.0   \n",
       "31  36.0     36.0         0.0        36.0        -4.0     36.0         0.0   \n",
       "32  22.0     22.0         0.0        22.0       -14.0     22.0         0.0   \n",
       "33  24.0     24.0         0.0        24.0         2.0     24.0         0.0   \n",
       "34  21.0     21.0         0.0        21.0        -3.0     21.0         0.0   \n",
       "35  20.0     20.0         0.0        20.0        -1.0     20.0         0.0   \n",
       "36  17.0     17.0         0.0        17.0        -3.0     17.0         0.0   \n",
       "37  14.0     14.0         0.0        14.0        -3.0     14.0         0.0   \n",
       "38  17.0     17.0         0.0        17.0         3.0     17.0         0.0   \n",
       "39  19.0     19.0         0.0        19.0         2.0     19.0         0.0   \n",
       "40  26.0     26.0         0.0        26.0         7.0     26.0         0.0   \n",
       "41  29.0     29.0         0.0        29.0         3.0     29.0         0.0   \n",
       "42  40.0     40.0         0.0        40.0        11.0     40.0         0.0   \n",
       "43  31.0     31.0         0.0        31.0        -9.0     31.0         0.0   \n",
       "44  20.0     20.0         0.0        20.0       -11.0     20.0         0.0   \n",
       "45  24.0     24.0         0.0        24.0         4.0     24.0         0.0   \n",
       "46  18.0     18.0         0.0        18.0        -6.0     18.0         0.0   \n",
       "47  26.0     26.0         0.0        26.0         8.0     26.0         0.0   \n",
       "48  17.0     17.0         0.0        17.0        -9.0     17.0         0.0   \n",
       "49   9.0      9.0         0.0         9.0        -8.0      9.0         0.0   \n",
       "50  17.0     17.0         0.0        17.0         8.0     17.0         0.0   \n",
       "51  21.0     21.0         0.0        21.0         4.0     21.0         0.0   \n",
       "52  28.0     28.0         0.0        28.0         7.0     28.0         0.0   \n",
       "53  32.0     32.0         0.0        32.0         4.0     32.0         0.0   \n",
       "54  46.0     46.0         0.0        46.0        14.0     46.0         0.0   \n",
       "55  33.0     33.0         0.0        33.0       -13.0     33.0         0.0   \n",
       "56  23.0     23.0         0.0        23.0       -10.0     23.0         0.0   \n",
       "57  28.0     28.0         0.0        28.0         5.0     28.0         0.0   \n",
       "58  22.0     22.0         0.0        22.0        -6.0     22.0         0.0   \n",
       "59  27.0     27.0         0.0        27.0         5.0     27.0         0.0   \n",
       "60  18.0     18.0         0.0        18.0        -9.0     18.0         0.0   \n",
       "61   8.0      8.0         0.0         8.0       -10.0      8.0         0.0   \n",
       "62  17.0     17.0         0.0        17.0         9.0     17.0         0.0   \n",
       "63  21.0     21.0         0.0        21.0         4.0     21.0         0.0   \n",
       "64  31.0     31.0         0.0        31.0        10.0     31.0         0.0   \n",
       "65  34.0     34.0         0.0        34.0         3.0     34.0         0.0   \n",
       "66  44.0     44.0         0.0        44.0        10.0     44.0         0.0   \n",
       "67  38.0     38.0         0.0        38.0        -6.0     38.0         0.0   \n",
       "68  31.0     31.0         0.0        31.0        -7.0     31.0         0.0   \n",
       "69  30.0     30.0         0.0        30.0        -1.0     30.0         0.0   \n",
       "70  26.0     26.0         0.0        26.0        -4.0     26.0         0.0   \n",
       "71  32.0     32.0         0.0        32.0         6.0     32.0         0.0   \n",
       "72   NaN     32.0         NaN        32.0         0.0     32.0         NaN   \n",
       "73   NaN      NaN         NaN        32.0         0.0     32.0         NaN   \n",
       "74   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "75   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "76   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "77   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "78   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "79   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "80   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "81   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "82   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "83   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "84   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "85   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "86   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "87   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "88   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "89   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "90   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "91   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "92   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "93   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "94   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "95   NaN      NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "\n",
       "    Level_Trip  Trend_Trip  Seasn_Trip    ES_Trip  ESErr_Trip  \n",
       "0    30.000000   -0.784722    0.000000  30.000000    0.000000  \n",
       "1    29.269274   -0.730726   -8.269274  20.269274    0.730726  \n",
       "2    28.599160   -0.670113    0.400840  28.329887    0.670113  \n",
       "3    27.993135   -0.606026    3.006865  30.393974    0.606026  \n",
       "4    27.459372   -0.533763   12.540628  39.466237    0.533763  \n",
       "5    27.027646   -0.431726   20.972354  47.568274    0.431726  \n",
       "6    26.673083   -0.354563   26.326917  52.645437    0.354563  \n",
       "7    26.404398   -0.268685   20.595602  46.731315    0.268685  \n",
       "8    26.226947   -0.177451   10.773053  36.822549    0.177451  \n",
       "9    26.136177   -0.090770   12.863823  38.909230    0.090770  \n",
       "10   26.109890   -0.026288    4.890110  30.973712    0.026288  \n",
       "11   26.112478    0.002588    2.887522  29.002588   -0.002588  \n",
       "12   26.101849   -0.010629   -9.101849  16.989371    0.010629  \n",
       "13   26.022003   -0.079846  -17.022003   8.920154    0.079846  \n",
       "14   25.892390   -0.129613   -5.892390  19.870387    0.129613  \n",
       "15   25.725355   -0.167036   -1.725355  23.832964    0.167036  \n",
       "16   25.471236   -0.254118    1.528764  26.745882    0.254118  \n",
       "17   25.129325   -0.341911    9.870675  34.658089    0.341911  \n",
       "18   24.708057   -0.421268   16.291943  40.578732    0.421268  \n",
       "19   24.232789   -0.475268   13.767211  37.524732    0.475268  \n",
       "20   23.698436   -0.534353    3.301564  26.465647    0.534353  \n",
       "21   23.124634   -0.573802    7.875366  30.426198    0.573802  \n",
       "22   22.547373   -0.577261    4.452627  26.422739    0.577261  \n",
       "23   21.979075   -0.568298    4.020925  25.431702    0.568298  \n",
       "24   21.478967   -0.500108   -0.478967  20.499892    0.500108  \n",
       "25   21.049811   -0.429155   -8.049811  12.570845    0.429155  \n",
       "26   20.669864   -0.379947    0.330136  20.620053    0.379947  \n",
       "27   20.285487   -0.384377   -2.285487  17.615623    0.384377  \n",
       "28   19.991890   -0.293597   13.008110  32.706403    0.293597  \n",
       "29   19.740904   -0.250985   15.259096  34.749015    0.250985  \n",
       "30   19.523014   -0.217890   20.476986  39.782110    0.217890  \n",
       "31   19.328095   -0.194919   16.671905  35.805081    0.194919  \n",
       "32   19.129765   -0.198330    2.870235  21.801670    0.198330  \n",
       "33   18.909412   -0.220352    5.090588  23.779648    0.220352  \n",
       "34   18.672256   -0.237156    2.327744  20.762844    0.237156  \n",
       "35   18.415830   -0.256426    1.584170  19.743574    0.256426  \n",
       "36   18.154065   -0.261765   -1.154065  16.738235    0.261765  \n",
       "37   17.924920   -0.229145   -3.924920  13.770855    0.229145  \n",
       "38   17.687726   -0.237194   -0.687726  16.762806    0.237194  \n",
       "39   17.480620   -0.207105    1.519380  18.792895    0.207105  \n",
       "40   17.239921   -0.240699    8.760079  25.759301    0.240699  \n",
       "41   16.973658   -0.266264   12.026342  28.733736    0.266264  \n",
       "42   16.729485   -0.244172   23.270515  39.755828    0.244172  \n",
       "43   16.468387   -0.261098   14.531613  30.738902    0.261098  \n",
       "44   16.214527   -0.253860    3.785473  19.746140    0.253860  \n",
       "45   15.983803   -0.230724    8.016197  23.769276    0.230724  \n",
       "46   15.752445   -0.231358    2.247555  17.768642    0.231358  \n",
       "47   15.590874   -0.161570   10.409126  25.838430    0.161570  \n",
       "48   15.450683   -0.140192    1.549317  16.859808    0.140192  \n",
       "49   15.291774   -0.158909   -6.291774   8.841091    0.158909  \n",
       "50   15.152911   -0.138863    1.847089  16.861137    0.138863  \n",
       "51   15.049092   -0.103818    5.950908  20.896182    0.103818  \n",
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       "53   14.948586   -0.030384   17.051414  31.969616    0.030384  \n",
       "54   14.979489    0.030903   31.020511  46.030903   -0.030903  \n",
       "55   15.037524    0.058035   17.962476  33.058035   -0.058035  \n",
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       "62   16.080611    0.155140    0.919389  17.155140   -0.155140  \n",
       "63   16.226440    0.145829    4.773560  21.145829   -0.145829  \n",
       "64   16.384876    0.158435   14.615124  31.158435   -0.158435  \n",
       "65   16.546491    0.161615   17.453509  34.161615   -0.161615  \n",
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       "67   16.836525    0.157674   21.163475  38.157674   -0.157674  \n",
       "68   17.042324    0.205799   13.957676  31.205799   -0.205799  \n",
       "69   17.248182    0.205858   12.751818  30.205858   -0.205858  \n",
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       "73   17.713440    0.243993   -7.925472  10.275955         NaN  \n",
       "74   17.713440    0.243993    0.919389  19.364808         NaN  \n",
       "75   17.713440    0.243993    4.773560  23.462973         NaN  \n",
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       "79   17.713440    0.243993   21.163475  40.828861         NaN  \n",
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       "83   17.713440    0.243993   14.286560  34.927920         NaN  \n",
       "84   17.713440    0.243993    2.237004  23.122357         NaN  \n",
       "85   17.713440    0.243993   -7.925472  13.203875         NaN  \n",
       "86   17.713440    0.243993    0.919389  22.292728         NaN  \n",
       "87   17.713440    0.243993    4.773560  26.390892         NaN  \n",
       "88   17.713440    0.243993   14.615124  36.476450         NaN  \n",
       "89   17.713440    0.243993   17.453509  39.558829         NaN  \n",
       "90   17.713440    0.243993   27.321149  49.670462         NaN  \n",
       "91   17.713440    0.243993   21.163475  43.756781         NaN  \n",
       "92   17.713440    0.243993   13.957676  36.794975         NaN  \n",
       "93   17.713440    0.243993   12.751818  35.833111         NaN  \n",
       "94   17.713440    0.243993    8.530554  31.855840         NaN  \n",
       "95   17.713440    0.243993   14.286560  37.855840         NaN  "
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   "source": [
    "# join them all together\n",
    "results = single.join(double.drop(columns=['Data']), how='outer').join(triple.drop(columns='Data'), how='outer')\n",
    "display(results)"
   ]
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   "cell_type": "code",
   "execution_count": 12,
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   "outputs": [
    {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ME</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MDE</th>\n",
       "      <th>MDAE</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ES_Sing</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES_Doub</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES_Trip</th>\n",
       "      <td>0.170647</td>\n",
       "      <td>0.24752</td>\n",
       "      <td>0.202718</td>\n",
       "      <td>0.219121</td>\n",
       "      <td>1.016735</td>\n",
       "      <td>0.088417</td>\n",
       "      <td>6.366015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "               ME      MAE       MDE      MDAE      MAPE       MSE       SSE\n",
       "ES_Sing  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000  0.000000\n",
       "ES_Doub  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000  0.000000\n",
       "ES_Trip  0.170647  0.24752  0.202718  0.219121  1.016735  0.088417  6.366015"
      ]
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     "output_type": "display_data"
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   ],
   "source": [
    "# error metrics\n",
    "models = ['ES_Sing', 'ES_Doub', 'ES_Trip']\n",
    "metrics = ['ME', 'MAE', 'MDE', 'MDAE', 'MAPE', 'MSE', 'SSE']\n",
    "mets = np.zeros(shape=(len(models), len(metrics)), dtype=float)\n",
    "for i, mod in enumerate(models):\n",
    "    for j, met in enumerate(metrics):\n",
    "        mets[i, j] = globals()[met](**{'actl':results['Data'].values[:n], 'pred':results[mod].values[:n]})\n",
    "display(pd.DataFrame(data=mets, index=models, columns=metrics))"
   ]
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
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   ],
   "source": [
    "# plot the data & forecasts & error\n",
    "fig = plysub.make_subplots(rows=2, cols=1, print_grid=False, subplot_titles=('Predictions / Forecasts', 'Errors'))\n",
    "fig['layout'].update(title='Data, Predictions, Forecasts, and Absolute Errors', height=800)\n",
    "# forecasts + data\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['Data'], mode='markers+lines', name='Data',\n",
    "                            marker={'color':'black', 'symbol':'star'}, line={'color':'black'}), 1, 1)\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['ES_Sing'], mode='markers+lines', name='Single',\n",
    "                            marker={'color':'red', 'symbol':'triangle-up-open'}, line={'color':'red'},\n",
    "                            legendgroup='Single'), 1, 1)\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['ES_Doub'], mode='markers+lines', name='Double',\n",
    "                            marker={'color':'blue', 'symbol':'circle-open'}, line={'color':'blue'},\n",
    "                            legendgroup='Double'), 1, 1)\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['ES_Trip'], mode='markers+lines', name='Triple',\n",
    "                  marker={'color':'green', 'symbol':'square-open'}, line={'color':'green'},\n",
    "                            legendgroup='Triple'), 1, 1)\n",
    "# errors + data\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['Data'], mode='markers+lines', name='Data',\n",
    "                            marker={'color':'black', 'symbol':'star'}, line={'color':'black'},\n",
    "                            showlegend=True), 2, 1)\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['ESErr_Sing'].abs(), mode='markers+lines', legendgroup='Single',\n",
    "                            marker={'color':'red', 'symbol':'triangle-up-open'}, line={'color':'red'},\n",
    "                            name='Single Error'), 2, 1)\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['ESErr_Doub'].abs(), mode='markers+lines', legendgroup='Double',\n",
    "                            marker={'color':'blue', 'symbol':'circle-open'}, line={'color':'blue'},\n",
    "                            name='Double Error'), 2, 1)\n",
    "fig.append_trace(go.Scatter(x=results.index, y=results['ESErr_Trip'].abs(), mode='markers+lines', legendgroup='Triple',\n",
    "                            marker={'color':'green', 'symbol':'square-open'}, line={'color':'green'},\n",
    "                            name='Triple Error'), 2, 1)\n",
    "\n",
    "plyoff.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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