docs/MunichExample.ipynb

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   "source": [
    "# Munich chainladder model\n",
    "\n",
    "Now we will explore the properties and methods underlying the Munich Chainladder class.\n",
    "\n",
    "As usual, we we import the chainladder package as well as the popular pandas package.  For plotting purposes, we will also be using Jupyter's `%matplotlib inline` magic function.  \n",
    "\n",
    "### Load package and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jboga\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import chainladder as cl\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Munich chainladder method is useful when both a paid and incurred triangle are available.  It is an improvement on the standard chainladder in that it considers the ratio of Paid/Incurred **(P/I)** triangle to adjust both the paid and incurred ultimates.  A nice property of the Munich chainladder method is that the **(P/I)** ratio approaches 1.0 as the development period approaches ultimate.  This makes the Paid ultimate and Incurred ultimate converge towards the same value, a property not found in the basic chainladder method. \n",
    "\n",
    "The chainladder package comes loaded with `MCLincurred` and `MCLpaid` datasets to illustrate the Munich Chainladder method.  We will load these and verify that the **(P/I)** ratios approach 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th>dev</th>\n",
       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "dev            1         2         3         4         5         6         7\n",
       "origin                                                                      \n",
       "1.0     0.588957  0.857414  0.923149  0.944030  0.954002  0.963336  0.980221\n",
       "2.0     0.469631  0.763323  0.876723  0.900000  0.910686  0.956805       NaN\n",
       "3.0     0.486226  0.863114  0.905066  0.960000  0.967700       NaN       NaN\n",
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "MCL_inc = cl.load_dataset('MCLincurred')\n",
    "MCL_paid = cl.load_dataset('MCLpaid')\n",
    "MCL_paid/MCL_inc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Munich chainladder model\n",
    "\n",
    "The Munich chainladder model, unlike the Mack model, only allows for volume-weighted link-ratios.  As such, there is no alpha parameter.  Like the Mack model, we do have the option of specifying exponential tail fits to our data, but for the purposes of this tutorial we will allow the tails to default to 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MCL = cl.MunichChainladder(MCL_paid, MCL_inc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary shows that the Ult. Paid and Ult. Incurred are indeed very close, with paids slightly less than incurred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Latest Paid</th>\n",
       "      <th>Latest Incurred</th>\n",
       "      <th>Latest P/I Ratio</th>\n",
       "      <th>Ult. Paid</th>\n",
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       "        Latest Paid  Latest Incurred  Latest P/I Ratio    Ult. Paid  Ult. Incurred  P/I Ratio\n",
       "origin                                                                                       \n",
       "1.0          2131.0           2174.0          0.980221  2131.000000    2174.000000   0.980221\n",
       "2.0          2348.0           2454.0          0.956805  2384.842092    2443.222400   0.976105\n",
       "3.0          4494.0           4644.0          0.967700  4553.623621    4634.357895   0.982579\n",
       "4.0          5850.0           6142.0          0.952458  6069.509293    6182.347407   0.981748\n",
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "MCL.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison to the basic chainladder\n",
    "\n",
    "We can see that the basic chainladder does not guarantee that the paid and incurred ratios converge to 1.0, with the below data ranging from a low of 0.73 to a high of 1.11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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IP9uqyiK2bt0arq6u5eamAxRLR1U0T8CAAQNw5MgRlJaWIiIiQmkvXBcXlxrt\nLU1ERIaHcWH1YFyoHOPCf9W1uFBWNnHXrl0IDQ1FYWEhzM3Nhftv3LiBJ0+ewNTUVKuRTrqi7vPk\n5OSEu3fvoqysDAUFBUqTxM+TfQ/q16+vdj60Dz74AFOmTIGTk5PS8/Ju3bqp3E/yxy3ZiERNycci\n1RVzqjs2yydcZTGSjC6PY+pU5jtUkaq2XT7eUzfHl7e3t9LpA4qLi3Hp0iUA0sSZqnm6zMzMMHLk\nSGzatKncfbo+zpqamtbqqGuqXbzaQAYjJydH6PGl6sdDGxYWFmjYsCFefvlluLq6Yty4cSp7Kly6\ndAlisRidOnVS2tNMW/Ltr+xIBl2q6lB12UlNUVERHj9+jMTERDx48ACxsbG4du2aENyp+3Fu2rSp\n2oliLS0tkZubK4yUkG1P1iOpopP6V155BSYmJgrrV0S+9N7q1auxevVqjdZLTEzU+Dl0wdPTE+3a\ntcPdu3dx5coVeHt7w8HBAf369UPfvn3Rr18/2NraVuk51E12K1/XWv79jYuLE26rGlEi83z9/8py\ndnYWJliXV1hYiOzsbMTExMDX1xdnzpxBSEgI4uLisHv3bqUlAuWTYWlpaUhMTMSjR48QHx+P6Oho\nIagDyn+258yZg0WLFqG4uBjffPMN1q5di+7duwv7o2vXrmrna1JGvudeRSd28r2NNSUf8MgSb7qm\nbOJiGfnvv6oekLL3LCcnB4mJiUhISMC9e/cQExODyMhI4cS+okBAlcp8zjVVmbaXlJQIx89OnTqp\n3b6qOUxkk1Zrug1lCbHbt28L/8uPZquIfBlOeeo+B0RERBVhXFh9GBcqx7jwX3UlLpTn5eWFXbt2\nIT8/HxcuXFC4eC4rl/jaa69p1ZlQVzR9PzUdISaLGdu2bav2e9SkSRO15SHVna/Xr19f+F/buEg+\n5nw+IaUrVW27Lo5j6tREzKlt22Uxo42NjdrfAWdnZ9jY2JTbdykpKcjPzwdQ+bhV18dZW1tbtd8B\nMmxMiJHBkB1cAWhcFgOQDs2v6mS2sh76/fv3r9J2ZOR79mjT66O6VKZUgkxGRgZ27NiBU6dO4cGD\nB5BIJOUeY2xsXOHJQkWThcqSE/Lbz8jIEE4SKhpub2ZmBmtra60u9Fc2KZCdnV2p9SrL1NQUW7du\nxX/+8x+EhYUBkJ6QBAQEICAgAEZGRujWrRtGjx6NcePGlSt7oAl1+0c+cSS/f+RLdlS0f+RHW1UH\nc3NzmJvRbVSDAAAgAElEQVSbo2nTphgwYABWr14NHx8fJCUl4ZtvvhEm35Z34cIF7Nu3D1evXhXK\nTshTl9AaOXIkCgsLsXbtWmRnZ0MsFuPKlSu4cuUK1q9fDxsbGwwePBjvvPMO2rVrp9FrSE9PF/6v\njmBNvqdfdV2Q0fRYo+w4Eh8fj23btiE0NFRp+9SN5tNUZT7nmqhs2589eyY8V0X7XNV3SJvPjaoL\nJJUNVlX1GNXmN5yIiOh5jAurD+NC5RgX/qsuxoWurq5wcHBASkoKgoODhYSYRCJBcHAwAKgdTVWd\ndB1/yM79qxovViVuU0c+5qxsycGKVKXtujqOqVNdMWdV2i77jmryubG1tS0XH8p/xysbt+r6OMuY\n88XGhBgZJG1HVlSV7GRSF2UxAMUfOV1cxK2qyrYhKioKc+bMUfjhsrCwQOvWrdGmTRu4uLigT58+\nWLt2rfAe6pL8MG9NTki0PaGQ74W1cuVKlT1ZnidfgkGX1LXfwcEBW7duRWxsLIKDg3H+/HnExMSg\nrKwMEokEN27cwI0bN3DgwAHs2LGjRnq/abN/tN03VbVw4UL4+fmhsLAQ586dQ1ZWltBbTSKR4D//\n+Q8CAgIU1nF0dESbNm3Qvn17uLq6wtLSUm0N7fHjx2PEiBE4d+4czp49i4sXLwonjpmZmfDz80NA\nQACWL1+uMB+CKpr2Cqws+XJ8ly9f1joo/PXXX2FjY4PevXujQ4cOOj22+fn54euvv1boJWdjY4M2\nbdqgXbt26NatG/r164dBgwZVW+mNyqpK27UJiFSVINRmG6ouish/9gICAjT+DVbV61QffveIiMgw\nMC7ULcaFyjEurJrajguNjIwwZMgQ+Pj44Pz58yguLoaZmRkiIyORnJwMMzMzDBw4UKfPWZWESVVU\nZlSRMtV1PJJVSikrK0NERITW6x8+fBgJCQno3bs3unfvrnQEUF09jlVFVduurKOBKsriTl3Erbo+\nzurDbyrVHibEyGDI9/IoLCyssee9d+8ekpKSYGVlhR49euhkm7ISH0DVeuHVpsLCQixYsED4wZ0+\nfTq8vb3Rrl07hbmIAMVenLpkY2MDIyMjSCQSPHv2TO1jS0pKkJubq/X2ZRo2bFjh0O/K0vSHWtko\nped16NABHTp0wMKFC5GZmYnLly/jr7/+wsmTJ5Gbm4uYmBj89NNPWLlyZVWbXSH53n8V7Z/qKtGn\nirW1NVq2bInY2FiUlZUhISFBKM+xe/duIRnWqlUrfPTRR+jXr1+5GuoXL16s8HksLS3x5ptv4s03\n30RZWRnu3LmD8PBwnDp1ClFRUSgtLcXKlSvh7u6utGyjvOouL9G7d2+Ym5ujsLBQo9cmLycnB1u2\nbIFYLIaFhQUuXboECwsLnbTr9u3bWL58OUpLS2FlZYUFCxZg8ODB5UpNlJSU6F0yrKptt7W1FUr6\nVPQdUjUaS76cU0XfM1WfK/nPXrNmzapcaoeIiKgqGBfqF8aFusO4sPriQi8vL/j4+CAvLw+hoaEY\nOHAgTp48CUCa4NZkfq7nqbvwr8k+qg7W1tZ49uxZtZUjrCpra2t0794d165dw/379/H06VOtSrXu\n3r0bUVFR+O233+Dj46NyvkFt6cNxrLJ00XZ7e3s8ePCgwu8noDzulI85Kxu31tRxll4MNdtdiqga\nWVlZCUOLa7K+emhoKABpTWlVPRm0JRKJhP9VTTap786ePYvk5GQAwIQJE/DFF1+gY8eO5X5wAeDp\n06fV0gZzc3NhUlT5esPKxMbGaj3CRr6U3c2bN9U+Ni0tDb/99hsCAwNx584drZ5H/nMlHxQ/T9X7\nWFxcjLt37+Kff/5RWG5jY4OhQ4fim2++waFDh4Qh47KJdqub/AmM/BxEylR0f3WQ77UnX7Zgz549\nAKT7Zfv27Rg+fLjSCYWfPHmicttpaWmIiIhQCLaNjY3xyiuvYPbs2fDz88O0adMASHtMatLDrG3b\ntsL/FX3GPvroIwwbNgwLFiyocLsyVlZWwkTSCQkJOH/+vMbr7t+/X+j56enpqbNkmGzbsu/u8uXL\nMWPGDKV116vrOFMVVW27sbGxMDl6dHS02qBb1TFQ/nNz69Ytte1V9bmSn5Puxo0bardx7do1/PHH\nHzh+/Hi1lUEhIqIXG+NC/cK4UBHjwvL0IS50c3ND06ZNAQCnT5+GRCLBqVOnAGhXLlF+H6lLyNdW\nbCI79793757azoK3b99Gv379MGnSJJw5c6ammgcAGDt2rPD/rl27NF4vKioKUVFRAKTHy549e+qs\nTfpwHKssXbRddozLyclRmLv8eRkZGcJzyXNwcBDmxKwo5lR1jK6p4yy9GJgQI4Py8ssvAwCSkpJq\n7DlldeJ1VRYDUGy/7DXVNbLJOAH1ExdfuXJF+NHV1fB9eW+88QYAaXLi6tWrKh8XGBio9ba7desm\n9BQ7cuSI2p6Eu3btwoYNG7B06VKtEgmA4twBqj7bt2/fVgiY5Xl5eeHNN9/EwoULVT6Hk5MT2rRp\nAwA1NorG3t5emDQ5JCRE5fsnkUhw+PDhGmmTjEgkEiaOtbS0VJg4PSEhAYC0J6O6UVtBQUHC//Kf\n7YCAAPTr1w/Tpk1TG1zIzz1RVFRUYZu7desmXPw5ceKEyjIcBQUFCA0Nxf3797XuGfj+++8LJ84r\nV67UqIfYo0ePsGnTJgDSXq1z5szR6jkrItsfgPpjjfxnqDqONZWhi7YPGTIEgLTGvuxCnDKHDh1S\nurxLly5CEi4oKEjl918kEiE8PFzpfe7u7sL/+/btU9kGAFi7di1+/PFHfPLJJzX6W01ERC8WxoX6\ng3GhIsaF5elDXGhkZCTMHRYSEoLIyEikpKTA3Nxc+OxoQpN9VFRUhL///rtqDa6kvn37ApAm69TF\noufPn0daWhquX78uJDJqyltvvSXE2T4+Prh+/XqF6xQWFuLrr78Wbs+ePVunJXP15ThWGbpouyzm\nBICDBw+q3Iaq72e9evXg6ekJQNqB8t69e0ofJ5FIcOTIEaX31dRxll4MTIiRQZGVNHvy5EmNDEEv\nKCjAlStXAOg28JHvwSA/b09dIl/2QBYcPu/+/fv4/PPPFZbp+qT77bffFua9+frrr5WWWAgPD6/w\nIq4y9evXx5QpUwBIh31//vnnStt/5coVbN++HYC0TvO4ceO0ep4OHToI/+/evbvc/bm5uWpLWbz+\n+usAgMePH6vsYRUfHy/0xJEFIzXhnXfeASCd6PTrr79WmsTZsGFDhT05dam0tBSrVq0STgKHDx+O\n+vXrC/fLPtsikUhpD8WysjL8/PPPCkGO/OfC3d1d+Exu3LhRZVLq6NGjwv+yY5s65ubmGD9+PAAg\nLi4Ov//+u9LHrVmzRpiUXZO5yeS1a9dOSGglJSVh+vTpiIuLU/n4f/75BzNmzBCeb+bMmejYsaNW\nz1kRTY41586dw+bNm4Xb+lI6URdtHz9+vJAIXb58udIeeYcOHcK5c+dUtmP69OkApPt01apV5Uaa\nFRcXY9myZSrft+7duwuloUJDQ7Flyxalj/vjjz+EgLZz585wdXVV2SYiIqKqYFyoPxgX/otxoWr6\nEBd6eXkBkJYJ//777wEAAwYM0Kpcqfw+CgwMLHfRXiKR4IcffkB6eroOWqy9CRMmCLHt2rVrlcYO\nDx48EM7nW7VqhVdffbVG22hmZobVq1fDyMgIYrEYc+bMwenTp1U+Pj09HXPnzhVi8+7du2PSpEk6\nbZO+HMcqQxdt79WrF1555RUAwI4dO5QmdGNjY7FhwwaV7Zg6dSpMTEwgkUiwePFipb/NGzduVPkd\nr6njLL0YOIcYGZTevXtj//79kEgkuHnzpkKv9epw+fJlFBcXo127dlrVNa6IbPivnZ2dwrDgusTT\n0xNr1qxBUVERzp49i/nz52P06NGwt7dHWloaQkNDcfjw4XI1inNycnQ6/4yTkxM+/PBD/PLLL4iP\nj8eYMWMwa9YsdOnSBQUFBTh79qxC2TJAu8k1582bh/Pnz+POnTs4c+YMxowZg+nTp6NDhw7Izs4W\ngirZD/XixYthZ2en1WsYOHAgrKyskJubi+DgYMyfPx/jx49Hw4YNERMTg507d+LRo0dwdnZWGHEi\n89577+HQoUPIy8vDt99+i6tXr2LYsGFwdHRETk4OoqKisGvXLhQXF8PExATz5s3Tqn1VMWrUKBw6\ndAjh4eE4evQokpKSMHXqVDg7OyM1NRUHDx4sV6qjKpOfisVipSdYEokE+fn5uHv3Lvz8/IQyIjY2\nNvj4448VHjts2DD4+PgAkPY8mz17Njp37gyJRIK7d+/C39+/XBkS+ZO9pk2bYvLkydi1axcSEhIw\natQovPPOO+jYsSMsLS3x5MkTBAQECGUS3d3d0b17d41e38KFCxESEoKEhARs2LAB0dHRGDt2LBwc\nHPD48WP4+voKJ6+9e/fGm2++qeE7p/gciYmJOHr0KO7evYvRo0dj0KBB8PT0FErRJCYm4ty5czh1\n6pTw3Ro8eDA+/fRTrZ+vIsOGDcPx48cBAOvWrUNKSgr69euHBg0a4PHjxzh58iTOnj2rkOSprZr9\nz9NF2x0dHbFo0SKsXLkSSUlJGDNmDGbPno0ePXogLy8Px48fR0BAACwtLVXWhJ82bRpOnDiBGzdu\nwNfXFw8ePMCUKVPQokULPHjwANu3b0dMTIzabXz77bcYN24c8vLysG7dOkRERGDs2LFo3rw5UlJS\ncOTIESGQNTMzw6pVq3TxFhIRESnFuFB/MC5kXKiJmo4LlXF1dYW9vT1EIpFQek+bcomA9HPm5uaG\nyMhIPH78GFOnTsV7772Hl156CY8fP8aBAwcQERGhch9VNzs7O3z++edYuXIlnj59itGjR+Pdd9+F\nq6srxGIxrl27hm3btiE/Px8mJiZYvny5zt9nTfTr1w///e9/sWrVKmRnZ2P+/Pno0aMHhg8fjk6d\nOqF+/fpIS0vD33//jYCAACFGat26NdavXy8kv3VFX45jlaGLthsbG2P16tWYOHEixGIx3nvvPbz9\n9tsYOHAgTExM8PfffwufG9m848/r0qULZsyYga1bt+Kff/7B6NGjMXv2bHTq1AnPnj2Dv78/Tp8+\nrTbmrInjLL0YmBAjg9K/f3/h4BseHl7tgY+sPJUuewGWlJTg8uXLACAM2a+LHBwcsHz5cnz55Zco\nLS3F6dOnlfbq6dGjB9zc3PDnn38CAO7evavzE4Z58+YhPT0dPj4+ePr0KVavXq1wv6mpKT777DP8\n+OOPAKQXazVlbm6O7du346OPPkJERATi4+Px1VdflXtcvXr18PHHHws9WrRhY2ODb7/9Fp999hnE\nYrHS93LatGl46aWXsGbNmnLrN2/eHBs2bMDChQuF4Ck4OLjc4ywtLbF8+XKd1trWxPr16zFnzhxE\nRkbi+vXr5Uoi2NvbY9SoUdi6dSsA7fbP82Qn/ZpwcnLChg0bFCaABaTzb0VGRuL27dtIS0tT+p6b\nmZlh0aJF+OOPP5CWlob4+HiF+xcvXoynT5/i9OnTSElJwdq1a5W24dVXX8Uvv/yi4auTzpmxc+dO\nzJs3D3fu3EFISIjS2v+9e/fGxo0bKxXcGBsbY926dejQoQM2bdqE/Px8lZ8pQPr9mjt3LubNm6e0\nTnlVDRkyBN7e3vD394dYLMa2bduwbdu2co/z9vbGs2fPEBISgsTERBQVFSmM/KsNumr7lClTIBaL\n8f333+PZs2dCj1YZCwsLrF69WmVC0sTEBFu2bMH8+fNx+fJlXLlyRejlLtOlSxe4u7sL5S+f17p1\na/j4+GD+/Pl48uQJ/vrrL6U9EG1sbPDTTz+pLddBRERUVYwL9QfjQkWMC1WrybhQGWNjYwwdOlQY\nfWdpaSmMqtPG6tWr8c477yA1NRUxMTH47LPPFO53c3PDJ598gqlTp+qi2VqbMmUKiouLsW7dOmRk\nZAifd3kWFhb45ptvhBKLtUHWQW/58uV4+vSp0s+EvOHDh+Orr75SGBGlK/p0HNOWrtreuXNnbNu2\nDR9++CGys7Oxc+dO7Ny5U2EbixYtgr+/Px48eKC0LYsXL0ZZWRm2b9+Ox48fK5S5BKSj2RYtWoQv\nvvhC6fo1cZylFwMTYmRQrKysMGjQIBw9ehRnz54tN+RX16qjTnxERITQu0XTC/f6auzYsWjbti22\nb9+OyMhIpKenw8TEBHZ2dujQoQNGjhyJoUOH4tGjR8KP7okTJ9CnTx+dt+XLL7/EwIEDsW/fPly/\nfh0ZGRmwsbFBnz59MHfuXJiamgongrLyY5pq0qQJfHx8cObMGQQFBSEqKgrp6ekwMjKCo6Mjevfu\njalTp1apV+fQoUPRvn17bN++HWFhYUhNTYW1tTW6du2KKVOmwMPDAzt27FC5fr9+/XDy5Ens3bsX\nYWFhePToEfLy8mBlZYUWLVrAw8MDkydPRrNmzSrdxsqysrLC7t274e/vjyNHjiAuLg75+flwdHTE\noEGDypVI0Hb/aMLExAQWFhawt7dH+/bt4enpieHDhysNsho2bIj9+/dj586dCA4Oxv3791FUVAQr\nKys4OTmhT58+mDRpEpycnHDz5k0cO3YM169fR3JysvD+mpmZ4bfffsPZs2dx+PBhREdHIy0tDRKJ\nBHZ2dujSpQtGjBhRqYsfzZs3h7+/Pw4fPozjx48jJiYGWVlZsLKyQufOnTFmzBiMGDGiSjXVZXOB\neXt7IygoCBcvXsTdu3fx7NkzlJSUwNraGm3atEHfvn0xbty4ap8E/ttvv4W7uzv8/Pxw+/Zt5OTk\noH79+nB0dES3bt0wfvx4uLq6Yv/+/QgJCUFxcTHOnj2rdY9PfW77jBkz0K9fP+F4+/TpU1hbW6Nv\n376YO3duhbX/ra2tsX37dgQHB+PAgQOIj49Hbm4unJycMGLECMyaNUvtMQaQBkknT57EwYMHcebM\nGcTFxSErKwtmZmZ4+eWX8cYbb+Dtt99GkyZNqvq2ERERqcW4UL8wLmRcqAl9iAu9vLyEhNgbb7wB\nc3NzrbfRunVrHDt2DDt27MDp06eRmJiIevXqoW3btnjrrbcwYcIEtWXna8LMmTPxxhtvwMfHBxcv\nXkRycjJKS0vRvHlzuLu7Y8aMGQrzaNeW119/HadOnUJwcDBCQkJw+/ZtpKamorCwUJjru2fPnhg7\ndqxQ0q+66NNxTFu6anuvXr0QHBwMHx8fnDt3DgkJCTAzM4OLiwtmzJgBDw8P+Pv7q2yHkZERli5d\niqFDh2Lnzp2IioqCSCSCnZ0d+vfvjw8++ECYx0yVmjjOkuEzkjw/UQRRHRcTEyMEDH5+fujatWst\nt0g7y5YtQ0BAAHr37q2yrjfpXlRUlDD/0hdffCHMqUP6YcuWLVi3bh0A6UStup6HioiIiIgMC+NC\nqgzGhfqNcSEREVVV5buHE+mpTp06wdPTEwAqNSFubcrKysKJEycAAB988EEtt8YwbNmyBZ9++ik2\nbNgAdfl/+UlBeVJdcxYvXoxly5bh0KFDah8n2z+mpqZo3bp1TTSNiIiIiOowxoUkj3GhfmNcSERE\nNYUJMTJIsqDh2LFjSEtLq+XWaO7AgQMoKCiAq6urXgyrNhTHjh0TytMp8+DBA2HeHnt7e/To0aMm\nm/dCy8zMREBAAFasWIHk5GSljzl16hTCwsIASCeE1XWteCIiIiIyTIwLSR7jQv3FuJCIiGqKyfLl\ny5fXdiOIdM3BwQFPnjxBdHQ0ysrKdFrLvbrk5eXhk08+gVgsxq+//lrtc+68KJo3bw5fX1+IxWKE\nhIQgPT0dpaWlyMnJwZ07dxAYGIjly5cjMzMTAPDDDz+w1nANsrCwwPHjxyEWi3HixAmIxWIUFhbi\n2bNniIqKwpYtW7B+/XqUlZWhYcOG2LhxIxo2bFjbzSYiIiKiOoBxIckwLtRvjAuJiKimcA4xMlg5\nOTkYOXIkMjIycPz4cbRo0aK2m6TWb7/9hg0bNuD999/Hp59+WtvNMSghISH49NNPkZ+fr/Ix5ubm\n+Oqrr+Dt7V2DLSMAWL9+PTZt2oSysjKVj2nRogV+/vlndOvWrQZbRkRERER1HeNCkmFcqN8YFxIR\nUU1gQowM2sWLF/Huu+9iyJAhWL9+fW03R6UnT55g2LBhcHZ2hr+/P4f+VwORSIS9e/ciLCwMDx8+\nRH5+Pho1aoRmzZrhjTfewNixY/U+ODZkMTEx2L9/P65evYqkpCSUlJTAzs4Ozs7O8PLywqhRo2Bl\nZVXbzSQiIiKiOohxIckwLtRvjAuJiKi6GVRCLDIysrabQEREREREOubm5lbbTSADxRiSiIiIiMjw\nqIoh69VwO6qdvgTLkZGRetMWqhruS8PBfWlYuD8NB/el4eC+NCz6sj+ZsKDqpg+fc0B/vnNUddyX\nhoP70rBwfxoO7kvDwX1pWPRlf6qLIY1rsB1ERERERERERERERERENY4JMSIiIiIiIiIiIiIiIjJo\nTIgRERERERERERERERGRQWNCjIiIiIiIiIiIiIiIiAwaE2JERERERERERERERERk0JgQIyIiIiIi\nIiIiIiIiIoPGhBgREREREREREREREREZNCbEiIiIiIiIiIiIiIiIyKAxIUZEREREREREREREREQG\njQkxIiIiIiIiIiIiIiIiMmhMiBEREREREREREREREZFBY0KMiIiIiIiIiIiIiIiIDBoTYkRERERE\nRERERERERGTQmBAjIiIiIiIiIiIiIiIig8aEGBERERERERERERERERk0JsSIiIiIiIiIiIiIiIjI\noDEhRkRERERERERERERERAaNCTEiIiIiIiIiIiIiIiIyaEyIERERERERERERERERkUFjQoyIiIiI\niIjqjJs3b2LatGnllp87dw7e3t6YOHEifH19a6FlRERERESkz+rVdgOIiIiIiEh/REdHIzQ0FCKR\nCBEREfDw8ICLi0ttN4sIALBlyxYcOXIEFhYWCsvFYjHWrFmDgwcPwsLCApMnT4anpyfs7OxqqaVE\nREREpApjDqottT5CTFXvPgAoKCjApEmTcO/evRpuFRERERHRiyc6Ohr+/v5ITU2FRCJBamoq/P39\nER0dXdtNIwIAODs7Y8OGDeWW37t3D87OzmjUqBHMzMzg5uaGK1eu1EILiYiIiEgdxhxUm2p1hJiq\n3n0AcOvWLXz99ddISUmphZYREREREb14QkNDlS4PCwtjj03SC0OHDsXjx4/LLc/NzUXDhg2F2w0a\nNEBubq5G24yMjNRZ+6pKn9pCVcN9aTi4Lw0L96fh4L6su86cOaN0+bFjx1BUVFTDrSFd0/fvZq0m\nxGS9+5YsWVLuvuLiYmzcuFHpfUREREREpHsikUir5UT6wsrKCnl5ecLtvLw8hQSZOm5ubtXVLK1E\nRkbqTVuoargvDQf3pWHh/jQc3Jd129GjR5UuLyws5H6t4/Tlu6kuKVerJROHDh2KevWU5+Tc3Nzg\n6OhYwy0iIiIiInpx2dvba7WcSF+0adMGjx49QmZmJoqLi3H16lX06NGjtptFRERERER6pFZHiFUH\nfRqSp09toarhvjQc3JeGhfvTcHBfGg7uy7qtRYsWSE1NLbe8efPm3Lekl4KCgpCfn4+JEydi6dKl\nmDVrFiQSCby9veHg4FDbzSMiIiKi51hbWyM7O7vc8kaNGtVCa+hFY3AJMX0Ykgfoz/BAqjruS8PB\nfWlYuD8NB/el4eC+rPvc3NzQunVrhIWFITU1FU2bNoW7u3utzh/GRBw976WXXoKvry8AYOTIkcJy\nT09PeHp61laziIiIiEgDgwcPhr+/f7nlgwYNqoXW0ItGrxJi8r37iIiIiIio5rm4uMDFxYUJTiIi\nIiIi0jkXFxckJibi2rVrKCkpQb169eDq6lqrnfDoxVHrCTFVvftkfHx8arpJRERERERERERERESk\nY9HR0YiIiBBul5SUICIiAk5OTkyKUbUzru0GEBERERERERERERGR4QsNDVW6PCwsrIZbQi8iJsSI\niIiIiIiIiIiIiKjaiUQirZYT6RITYkREREREREREREREVO3s7e21Wk6kS7U+hxgRERERERERERER\nERm+Vq1aITU1tdzyli1b1kJrSBeio6MRGhoKkUiEiIgIeHh46O18cEyIEREREVGV1KWTXyIiIiIi\nIqo9Dx8+VLr80aNHNdsQ0ono6Gj4+/sLt1NTU4Xb+nhdgAkxIiIiIqq0unbyS0REREREdRM74hkG\nziFWR4jFwLNnQHr6v39paeX+b3zrFj7Mzka9khIcfustPGzdGgBw5swZvfx+MiFGRERERJUWGhqq\ndHlYWJhenvwSEREREVHdw454hsPe3l5pyUTOIVaNCguVJrPUJbqQlaXRph2NjFBgYYFcKysUm5kJ\ny7M0XL+mMSFGRERERJXG3n1ERERERFTd2BHPcHh4eCgkN2Xc3d1roTV1jEQC5OZqn9zKz9ds+2Zm\ngJ0d4OwM2NpK/+zs1P6/6pdfcOufLggN9YBoqz3s7UXw8AhFly7/VO97UUlMiBERERFRpbF3HxER\nERERVTdVHe6UxSKk31xcXJCYmIhr166hpKQE9erVg6ur64uX2CwrAzIztU9uicWabb9BA2nSqmPH\n8sksVYmuBg0AIyOtXsatf7rA33+ccDs11UHhtr5hQoyIiIiIKo29+4iIiIiIqLoZGxujtLS03HIT\nE5NaaA1VRXR0NCIiIoTbJSUliIiIgJOTU91Nisnm29IkoSX7PyNDmhTThI2NNGnVsqX6UVvyt+vX\nr97X/P9CQz2ULg8L089rAkyIEREREVGlyQKWsLAwpKamomnTpnB3d6+7gQwRERERGZTo6GiEhoZC\nJBIhIiICHh4ePFetg5Qlw9QtJ/2l9+UvCwrUJ7OU3Zedrdm2TUykyaqmTYFOnTQqSYjGjYF6+pvG\nEYmUV4dRtby26e87SURERER1gouLC1xcXBAZGQk3N7fabg4REREREQBpMky+mkFqaqpwWy8uvJPG\nmo/6v0sAACAASURBVDZtqrQ8YtOmTWuhNVQVNTYPtUQC5ORon9wqKNBs+/XrS5NWrVppXpLQ2how\nNtbt66xlTZuKkJLiUG65vb0IQLOab1AFmBAjIiIiIiIiIiIig3P69Gmly8+cOcOEWB3DUu2Go1Lz\nUJeVSUsMajrPluxP0/m2rKykSatXXtG8JKGlpdbzbRkiT8/L2LdvlJLlEQDKL69tTIgRERERERER\nERGRwclWUcYsKyurhltCVcVS7YbDo29fnNizB5b5+f/+FRTg1datgUWLlCe3nj2TjviqiJGRtMSg\nra105JYmo7aaNKmx+bYMUceON+DtXYywMHeIRPawtxfB3T0MnTrFgAkxIiIiIiIiIiIiIiItsVS7\nHiorA7KypMkrZX8iUbllLhkZ0CiNWa+eNGHl4CAduaXJqK3GjaXzdFGNsbe3R5cu/6BLl3+eW16+\njKI+YEKMiIiIiIiIiIiIDI61tbXSUWKNGjWqhdYQ6TmJBMjPV5vMKpfsSk8HSksr3na9etKkVYsW\nQLdu0v/t7fG0uBiOXbooT25ZW7MkYR1Q18qZMiFGREREREREREREBmfw4MFKL9QOGjSoFlpDVMOK\niv4tPajh6C0UFmq27SZNAHt7oF07aQJL2Z+9/b//q0huPYmMhCNH+9Vpda2cKRNiRERERERERERE\nZHDq2oVaUu+HHxLxyy+WSE7ugWbN0vHxx/lYvNiptptVM0pLgYwMzUZuyR6Tk6PZths2lCatunRR\nn9SS/TVuLB3xRfT/6lI5U35yiYiIiIiIiIiIyCDVpQu1pNoPPyRiyZJ/k19PnthiyRJbAIl1Lykm\nkQDZ2dqN3Hr2TLpeRczMpEms1q3VJ7Xk/+rXr/7XTKQnmBAjIiIiIiIiIiIiIr31yy+WSpf/+qsF\nFi+u4cY8r6BA83m3ZH9iccXbNTaWzqXl4AB07qxZacIGDTjvFpEaTIgRERERERERERERkd5KTm6s\ndPnTp8qXV5pYrHreLVXJrvx8zbZtYyNNWrVsWXFZQjs76eONjXX7+ohecEyIEREREREREREREZHe\natYsA0+e2JZb7uiYAcBO+UplZUBmpnbzbmVladYgS0tp0qpjx4pLEtrbA02aAKamlX8DDMwLPR8c\n1SomxIiIiIiIiIiIiIhIb338cT6WLGmCZkjGS3gMO6TBDmmY2SMe+KJEecIrPV2aFKuIqak0ceXs\nrFlZQltbaUKMKsWg5oOjOocJMSIiIiIiIiIiov8XHR2N0NBQiEQiREREwMPDAy4uLrXdLKIXR24u\ncPcuEBsr/YuLw+LYWCwwuwPz4jzFxx79/z8ZIyPpaCw7O6B9+4rLEtrZAdbWnHerBun1fHBk8JgQ\nIyIiIiIiIiIigjQZ5u/vL9xOTU0VbjMpVjexNJueKi0FHj4E4uIUEl+IjQWSkso9vMzUFNk2Nrhv\n54ysRo2Q36AB8i0t0XXgQLR0df034dW4MWBiUvOvhzRWY/PBESnBhBgRERERERERERGA0NBQ3LrV\nGaGhHhCJ7GFvL4KHRygcHMKYEKuDWJqtlkkk0rKFzye84uKA+HiguLj8Ok5OwMCBQIcO0r/27YEO\nHfDH8eNISUsr9/Ck+vUx18OjBl4M6Uql5oMj0hEmxIiIiIiIiIiIiACcO9cU/v7ewu3UVAf4+4+D\nkZE/5s6txYZRpbA0Ww0pLJQmuJ5PfMXGAhkZ5R9vbQ1066aQ8EL79kC7dkCDBkqfIjU9XelykUik\ny1dCNUA6H1z5hNhHHxXUQmvoRcOEGBEREREREREREYDQUHely8PClC8n/cbSbDpUVgY8flw+4RUX\nBzx6JB0NJq9ePaB1a8DdXTHx1aED0LSp1nN22dvbIzU1VelyIiJNMSFGREREtYKTlRMRERGRvklN\nVX5xPTWVZbzqIpZmq4SsLOUjve7eBQqUjOBp1gzo319xpFeHDsDLLwOmpjprloeHh8L8fjLu7kxW\n1zUcuUm1iQkxIiIiqnGcrJyIiIiI9JGjIxMohoSl2VQoLgbu3y8/0is2FlAyCguWluXm9EKHDtIS\nh40a1UiTXVxcsHt3CXbscERqqj2aNhVhxoynjB/rII7cpNrEhBgRERHVuNDQUKXLw8I4WTkRERER\n1R4mUAzL4sVOCA19iBMnWqCkpB7q1SvBsGFJWLy4VW03rfpJJEBycvmEV1ycNBlWWqr4eGNjoFUr\nwNW1fPKreXPp/bXohx8S8f333YXbKSkO+P57B9jaJmLxYqdabBlpiyM3qTYxIUZEREQ1TtXEx5wQ\nmYiIiIhqk/TCeiJ+/dUCT582hqNjBj76qIAX3OuoH35IRFBQK+F2SYkpgoJa4YcfDCiJkpsrLWeo\nLPGVk1P+8ba2QO/eiiO92rcH2rQBzM1rvv0aYpk9w8GOB1SbmBAjIiKiGscJkYn0F+f3I/o/9u48\nPKr67P/4O4Bg2AMEEkIABcUqVCEqLom2davghhRFq1ZqW7UuidpJax9trfpYa7QmrrW1+mitCy2L\nAmr9obaCGzaKFesGCAaTmAQCAglLyPz+OIBgBoyQ5GQm79d1cSXzzcxwT08Zc+Zzvvctqa2LRDKJ\nRKC4uJisrKywy9FuSJgQZdMmWLKkYeD1wQfw6acN79+pEwwd2nCn1777BoFYHLLNXuLwwgOFyUBM\nkiS1OAciS62T8/0kSVIiiasQJRqF5csb7vT64ANYtCiY+/VlmZlwzDENg6+BA6F9+5Z/Dc3INnuJ\nxQsPFBYDMUmS1OK2fLA+d+5cKioq6Nu3L9nZ2X7gLoXM+X6SJCmRtMoQZd26oMXhtoHXlu+rqxve\nv3t3OPDAhju99tkHunRp+fpDcu65Jfzudw2P5TnnLMNATFJjGYhJkuKKrbwSx/Dhwxk+fLhXhEmt\niPP9JElSIgltVlF9PSxb1nCn14cfwtKlwW6wbXXoAHvvDdnZ2wdfw4ZB376QlNS89caBQYNeZfz4\nhcydm01lZSqpqZVkZ89l8OAq4KCwy5MUJwzEJElxw1ZektS8nO8nSZISSbPPKlq5MvZOr48+gtoY\noVtaGhx11PY7vYYNg732gj32aJqaElRlZSUjRlQwYsS7X1pvF1JFkuKRgZgkKW7YykuSmpfz/SRJ\nUiKKRpOApM1fv6YNG2Dx4tjBV4wLiejcueFMr2HDghaHPXrs9mtpq7xwS1JTMBCTJMUNW3lJUvNy\nvp8kSVBQUEJhYWfKy0eSlracvLyapttRpBZVUFBCfv4Xx660tPfmFool2x/TaBTKyxsGXh9+GIRh\nmzZt/8Tt2sHgwTBqVMPwq3//4OdqUl64JakpGIhJkuKGV4RJUvNzvp8kqS1rdICiuFBY2Hm7211Y\nwz58xMe//TfUlG4ffK1e3fAJeveG0aO3D7z23ReGDoVOnVroVQi8cEtS0zAQkyTFDa8IkyRJktSc\nvhygbFFUlEwk0sLFaLd9VtaDMcziIv7ASN5iAJ8GP6gGrtt8p06dgoDryzu99t03CMTUanjhlqTd\nZSAmSYobXhEmSZIkqTmVl6fEXC8ri72uVqq2Fv7yF95rfyv71H0EwCdkMptj+IBhfNYjg+sf39zu\ncOBAaN8+5IIlSS3BQEySFFe8IkySJElSc0lLq6a0tOGuoPT0aqBPyxekr6eiAu6+G+65B6qq2Ktd\nBx7iPH7PlfyHA7fe7ecXzYfvHhRioZKkMBiISZIkSZIkSUBeXs3mmWHby82tDaEaNdp//wu//z08\n8gisXw8pKXD11fy1WzdmFO/DZ3P70a5yE6mplWRnz2Xw4CrAQEyS2hoDMUmSJEmSJAmIRDKBEoqK\nkikrSyE9vZrc3NrN62pVolF44QW47TZ45plgbcgQuOIKOP986NKFpddfz4gR7zJixLvbPbSysl3L\n1ytJCp2BmCRJknbLggULmDNnDpWVlcybN4+cnBxn+0mSpLgViWQSiWCb9tZqwwZ4/PFgR9jbbwdr\n2dlw5ZVwyinbzQNLTU2loqKiwVOkpqa2VLWSpFbEyyEkSZK0yxYsWMCUKVOoqKggGo1SUVHBlClT\nWLBgQdilSZIkKZGsWAG//S0MHgw/+AG88w6ccQa8/jrMmQPjxm0XhgHk5OTEfKrs7OwWKFiS1Nq4\nQ0ySJEm7bM6cOTHX586d6y4xSZIk7b6FC6GwEB58EGpqoFu3oC3i5ZcH4dhObPl9dO7cuVRUVNC3\nb1+ys7P9PVWS2igDMUmSJO2yysrKr7UuSZIkfaVoFF5+OWiLOH16cDszE66/Hn70I+jRo9FPNXz4\ncIYPH24LTEmSgZgkSZJ2nXMZJEmS1GTq6mDqVLjtNpg3L1g7+GC46ioYPx722CPc+iRJcc0ZYpIk\nSdplzmWQJEnSbvv8c7j9dhg6FM48E954A045Bf71ryAYmzjRMEyStNvcISZJkqRd5lyGBFRTE3wo\nJUmS1Nw++QTuuAP+9Kfg94/kZLj4YsjLg333Dbs6SVKCMRCTJEnSbnEuQ5zZtAk+/RQ+/hgWL274\ntbwcgG533w0eT0mS1Bz+/e9gPtjkycHvJmlpkJ8PF10EvXuHXZ0kKUEZiEmSJEmJprp6+5Br2++X\nLoWNGxs+pn17GDSINaNH8/Gee/Ls0qV0vfdecnJy3PEnSZJ2X309zJwZzAd76aVgbfjwYD7YWWdB\np07h1idJSngGYpIkSVK8Wb8elizZ8S6vVatiPy41FUaNgr32gr33Dv5s+X7AABa8/z5TpkzZevea\nioqttw3FJEltRUFBCYWFnSkvH0la2nLy8mqIRDLDLit+1dTAQw8FM8I++ihYO+EEuPJKOO44SEoK\ntz5JUpthICZJkiS1NvX1UFa248CrtBSi0YaPS04Owq2cnO3Drr32Cv507brTv3bOnDkx1+fOnWsg\nJklqEwoKSsjP/yL8Ki3tTX5+b6DEUOzrKi+Hu+6Ce++FFSugY0eYNCkIwvy9QpIUAgMxSQlvwYIF\nzJkzh8rKSubNm2frJ0lS67BqVRBwxQq9Pv442AX2Ze3awYABcPTRDQOvvfeGvn136yrrysrKr7Uu\nSVKiKSzsHHO9qCiZSKSFi4lX77wTzAd79FHYsCGYCXbNNXDJJcGsMEmSQmIgJimhLViwYLvWTxW2\nfpIktZQNG+CTT3a8y2vFitiP69ULRoxoGHbttRcMHBhcXd1MUlNTqaioiLkuSVJbUF6eEnO9rCz2\nujaLRuG554Ig7LnngrV994UrroDzzoPOsYNGSZJakoGYpIRm6ydJUrOJRuGzz3YceC1bFrQ+/LJO\nnWDwYBg9OnZbwx49WvylbDF48OCYgdigQYNCqEaSpJaXllZNaWnvBuvp6dVAn5YvqLVbvz7YCfb7\n38OCBcHa0UfDVVfB2LHB7nZJkloJAzFJCc3WT5Kk3bJmzc7bGtbUxH5cRgYceWTsXV7p6a32w6El\nS5bEXF+6dGnLFiJJUkjy8mo2zwzbXm5ubQjVtGLLlwezwe66K7hAqH17OPvsYEfYwQeHXZ0kSTEZ\niElKaLZ+kiTtVF0dlJTseJfXji6g6N49aAO0994NA69Bg2DPPVv2dTQRLySRJLV1kUgmUEJRUTJl\nZSmkp1eTm1u7eV18+CHcfjs89BDU1ga/E/3sZ3D55ZDp/0aSpNbNQExSQsvJydluhtgW2dnZIVQj\nSWpx0WhwBfO2Ide233/yCWza1PBxe+wRBFsjR8be5ZWSAklJLf96mpkXkkiSFIRikQgUFxeTlZUV\ndjnhi0bhpZfgtttg5szg9qBBkJcHF1wA3bqFXaEkSY1iICYpoW2ZEzZ37lwqKiro27cv2dnZzg+T\npERSUwNLlux4l9eaNbEfl5YWzPGKFXhlZAStf9oYLySRJElbbdwIf/97EIQVFwdro0cH88HGjYMO\nfqwoSYovof+X6+233+bWW2/lL3/5y3brL7zwAnfffTcdOnRg/PjxnHHGGSFVKCneDR8+nOHDh3t1\nnyTFq02b4NNPdxx4lZfHflzXrrHDrr33hsGDoXPnFn0Z8cALSSRJEqtWwZ/+BHfcEbSWTkqC00+H\nK6+EI45IyF3ykqS2IdRA7E9/+hNPPfUUycnJ261v3LiR3/72t/z9738nOTmZs846i+985zv06dMn\npEolSZLUrKqrY4ddixfD0qXBFcpf1r49DBwIxxwTO/jq08cPbHaBF5JIktRGLVkCRUVw//3BDvvO\nneHSS4PWiEOGhF2dJEm7LdRAbODAgdx5553k5+dvt75o0SIGDhxIjx49AMjKyuKNN97gxBNPDKNM\nSZIk7cSCBQuYM2cOlZWVzJs3j5ycnIY7itav33lbw1WrYj95amowxyvWLq/MTFv1SJJajYKCEgoL\nO1NePpK0tOXk5dUQiWSGXZb01V5/PWiLOGUK1NdD//7wP/8DF14YzE2VWgnfZyXtrlA/QTjhhBNY\ntmxZg/U1a9bQbZuBnF26dGHNjmY/SJIkKTQLFiwIZk7V19NtzRo6LVnCR889R2r//vRbs+aLwKu0\nNBjA/mXJyUHAlZMTu62hQ9olSXGgoKCE/PwvPpQtLe1Nfn5voMQPa9U6bdoETz4Jv/89vPxysHbg\ngcF8sDPPhI4dw61P+hLfZyU1hVZ5SW3Xrl1Zu3bt1ttr167dLiDbmeItQz5bgdZUi3aPxzJxeCwT\ni8czvn366acsXLiQNWvW8K9//YuhQ4eSkZERdlnakWiUDitX0vHTT+lUWkrH0lI6lZbS9d13uaSq\nip4rV9Jh06aGD0tKYkO/fmwYOZL1GRmsz8hgQ0YG6/v3Z31GBnW9e8dua7hhA3z4YQu8MO2M77OS\n1DiFhbHnUhYVJROJtHAx0s6sWQP/939QWAiLFgVrY8YEQdi3v227abVavs9KagqtMhAbMmQIS5cu\nZeXKlXTu3Jl///vfXHDBBY16bGuZc+DMhcThsUwcHsvE4vGMbwsWLOCtt97aenv16tW89dZb7L33\n3g1b7anlVFd/0dZwy9ct3y9ZAttcsLRFKlCTnMxn/fqxsmdPVqakUJ2Swqpevfj+tdeSNHAgnTp2\npBPgXq/40lreZw3lJMWD8vLYbeXKymw3p1aitBTuvBPuuy/4na9TJ/jRj+CKK2D//cOuTvpKvs9K\nagqtKhCbMWMGNTU1nHnmmfziF7/gggsuIBqNMn78ePr16xd2eZIkqYnMmTMn5vrcuXMNxJrTmjUN\ng65tv+5ojlf37jB0aNDKcPDg7b7eP3s2n37+eYOH9OvXL3iMJEltQFpaNaWlvRusp6dXA31aviBp\ni7ffDuaDPf44bNwIffrAr38NP/0p9O0bdnVSo/k+K6kphB6IDRgwgMmTJwNw8sknb13/zne+w3e+\n852wypIkSc2osrLya62rkdat+2I3V6xdXlVVsR/XuXMQcGVnfxF4bRt+9ey5w/Y5hyUlBTPEviQ7\nO7tJXpIkSfEgL69m8yyb7eXm1oZQjXZXQUEJhYWdKS8fSVracvLyauJrRlF9PTz7bDAf7Pnng7X9\n9oMrr4RzzglmuEpxxvdZSU0h9EBMkiS1PampqVRUVMRc105s3AiffLLjwKusLPbjOnaEQYNg1KiY\nu7xITXVehCRJuyEIS0ooKkqmrCyF9PRqcnNr4ytEERCEYfn5Xxy30tLemz+EL2n9x3PdOnjkkSAI\ne++9YO073wnmg333u9CuXbj1SbvB91lJTcFATJIktbicnBx3FcWyaRN8+umO2xouWxZc8ftl7dvD\nwIHBIPRtg64t36enN9sHILa/lCQpEIlkEom0nhmM2jW3375nzPXCwj2JRFq4mMaqrIR77oG77w6+\n79ABzj03mA82cmTY1UlNxvdZSbvLQEySFFcWLFjAnDlzqKysZN68eeTk5Pihexzacszmzp1LRUUF\nffv2JTs7O/GPZTQK5eWx53d9/HGw+6uuruHjkpIgIwOOOKJhO8PBg2HAgOCDjxDY/lJSS6qvr+e6\n667jgw8+oGPHjtx4440MGjRo68+nT5/On//8Z7p168a4ceOYMGFCiNVKikfl5Q1bsgXrvVq4kkZ4\n7z24/XZ4+GFYvz5oc/3zn8NllwW/O0qSpO0YiEmS4saCBQu221VUUVGx9XbCBykJaPjw4QwfPjyx\nru6LRmH58tjtDD/+GJYuDVrZxNKvHxx8cMN2hnvtBZmZ0KlTy72Or8H2l5Ja0uzZs9mwYQNPPPEE\n8+fP5+abb+bee+8FYMWKFdxxxx1MnTqV7t27c/7553P44YczYMCAkKuWFE9SUyupqOgXcx3SWr6g\nL4tG4cUX4bbb4Omng7W994a8PJg0Cbp2Dbe+VijuZ8JJkpqMgZgkKW7Ymk2twqpVsdsZbvm6Zk3s\nx/XqBQccEHuG16BB0Llzi72EpmT7S0ktqbi4mJycHAAOOuggFixYsPVny5YtY9iwYfTs2ROAESNG\n8PbbbxuISfpaTjjh3/zlL2MbrB9//JvAmJYvaIsNG+CJJ4L5YPPnB2tHHBHMBzv11KCFthqI65lw\nkqQmZyAmSYobtmZTi1i7NnY7wy3fr1wZ+3HdugVX58YKvAYPhu7dW+oVtKg22/5SUijWrFlD1212\nP7Rv3566ujo6dOjAoEGDWLhwIVVVVXTp0oVXX32VwYMHh1espLiUnz+Impq/M3duNpWVqaSmVpKd\nPZf8/P3CKai6Gv74R7jjDigtDebCTpgAV14Jhx0WTk1xpLAw9kVnRUXJrXcmnCSp2RiISZLihq3Z\n1CTWrQtmde0o8NpRwJqcHARbW+Z4fTn0SkkJZn21QQnZ/lJSq9S1a1fWrl279XZ9fT0dNs9Q7NGj\nB1dffTWXXXYZPXv25IADDiAlJeUrn7O4uLjZ6v26WlMt2j0ey/h23nkdOeKIR7eG8EOHDmX9+vUt\nelw7LltGv8ceo/dTT9G+tpZNnTtTddZZVEycyIYt88H8/9lXKi8fGXO9rCzFf6dxzuOXODyWiaW1\nH08DMUlS3LA1mxpl40ZYtmzHbQ1LS2M/rmPHoHXhQQc1nOE1eDD07dtmAy+1Lc7ZUGs2atQoXnzx\nRcaMGcP8+fPZd999t/6srq6O//73vzz66KNs3LiRSZMmccUVV3zlc7aWIN+LChKHxzL+bTl+oRzL\nV14J5oNNnw719TBgAFx+Oe1//GP69exJw+lm2pm0tOWUlvZusJ6eXu2/0zjm+2zi8FgmltZyPHcW\nyhmISZLihq3ZBMCmTUGotaO2hsuWBff5svbtITMTvvWt7YOuLV/79w9a0EhtmHM21Nodd9xxvPzy\ny0ycOJFoNMpNN93EjBkzqKmp4cwzzwRg3LhxdOrUiUmTJtGrV6+QK5akRqirg2nTgvlgr70WrI0a\nFcwHmzAB9tgj3PriWF5ezebfZbaXm1sbQjWSpLAZiEmS4oqt2dqAaBQ++yx2O8MlS2Dp0mAX2Jcl\nJQWh1uGHN2xnuNdewdW1HfzVR9oZ52yotWvXrh3XX3/9dmtDhgzZ+v2ll17KpZde2tJlSdKuWb0a\nHngACguD33MBTj45CMKOOsruBE0guKCnhKKiZMrKUkhPryY3t9YLfSSpjfJTIUmS1PI+/xwWLYLF\ni+n30kvBBwHbhl/r1sV+XN++wdWyX25nuNdeMHAgdOrUgi9CSjzl5bHnLZWVffUcJkmS1EglJXDn\nnfDHP8KqVbDnnnDRRZCXB8OGhV1dwolEMolEWk8rL0lSeAzEJElS04tGoaxsa+jFokXb/6mq2nrX\nAds+LiUF9t+/YTvDLV87x969IqlppKVV73DOBvRp+YIkSUokb74ZzAebPDlok9i3L1x/PVx8MfTx\nv7OSJDU3AzFJkrRrNmwIdnPFCrwWL4baGH3599gjCLYOPhiGDIEhQ1hYX8/QY48N1nv0aOEXIWlb\n555bwu9+1zAQO+ecZRiISZK0C+rrYdasYD7YP/8ZrB1wAFx5JZx9drA7TJIktQgDMUmStGOrVsUO\nvBYtClq91Nc3fEz37rDfflsDL4YMgb33Dr5mZkL79tv/FcXFcOCBLfSCJO3MoEGvMn78QubOzaay\nMpXU1Eqys+cyeHAVcFDY5UlSq1ZQUEJhYWfKy0eSlracvLwa5xS1ZTU18PDDcPvt8OGHwdpxxwXz\nwY4/3vlgkiSFwEBMkqS2rL4eystjB16LFsHy5bEf178/HHlkw8BryBDo3dsTfClOVVZWMmJEBSNG\nvPul9XYhVSRJ8aGgoIT8/C/Cr9LS3uTn9wZKDMXams8+g7vvhnvuCX6X3mMPOP98uOIK+OY3w65O\nkqQ2zUBMkqREt6W1YazA6+OPd9zacK+94NBDGwZee+3lLC8pQaWmplJRURFzXZK0Y4WFsX83KipK\nJhJp4WIUjnffDdoiPvJI8Pt3Sgr88pdw6aWQnh52dZIkCQMxSZISw6pVO97ltWxZ7NaGPXrAN76x\nfdi15c+AAQ1aGzY12wpJrU9OTg5TpkxpsJ6dnR1CNZIUP8rLU2Kul5XFXleCiEZh9my47Tb4xz+C\ntaFDg91gP/gBdOkSbn2SJGk7BmKSJMWD+nooK2sYdm2Z77Wj1oYZGdu3Ntz2T69eobU2tK2Q1DoN\nHz4cgLlz51JRUUHfvn3Jzs7eui5Jii0trZrS0t4N1tPTq4E+LV+Qmtf69fDYY8GOsHfeCdZycoL5\nYCed1OwXlkmSpF1jICZJUmuxfn3D1oZbAq/Fi2HduoaP6dhx+9aG2/7Zay9ITm7xl9EYthWSWq/h\nw4czfPhwiouLycrKCrscSYoLeXk1my/u2V5ubozW1Ipfy5fDH/4Ad90VzOFt3x4mToQrr4RDDgm7\nOkmS9BUMxCRJakkrV8YOvBYtgpKSoO3Kl/XoAfvvH3uXV0ZGXF6BaluhxGL7S0lSWxf8d6+EoqJk\nyspSSE+vJje31v8eJoqPPoLCQnjwwWD+brduwW6wyy6DQYPCrk6SJDWSgZgkSU2pvh5KS2MHXosW\nwYoVsR+XkRG0WdlRa8MEY1uhxGH7S0mSApFIJpEI7rBNIF3fegtuuAGeeiq4cG3gQMjLgwsucswy\nKAAAIABJREFUgO7dwy5PkiR9TQZikqS40ip2oqxfDx9/HDvw+vjjnbc2POyw2K0N99yzZV9DyM49\nt4Tf/a5hIHbOOcswEIsvtr+UJEkJZ+NGuOwyht13X3D7kEOCHWHjx0MHP0qTJCle+V9xSVLcaNGd\nKNXVsQOvRYtg2bLYrQ179oQDDmgYeO29d9y2Nmwugwa9yvjxC5k7N5vKylRSUyvJzp7L4MFVwEFh\nl6evwfaXkiQpoaxaxZJDTmbwR3OYz4Fc1/t/OfJ7I4icOTDsyiRJ0m4yEJOU8FrFjiI1iSbdiVJf\nD59+GjvwWrQoCMRiGTAAjjqqYeCVoK0Nm0tlZSUjRlQwYsS7X1pvF1JF2lW2v5QkSQnj44+pOvwE\nBn/2ETM4ibN4jLXLu/Lkz4Ek20FLkhTvDMQkJTRn2ySWr70TZd06WLIkduD18cdB68Mv69QpaGF4\nxBENA6822NqwuaSmplJRURFzXfElK2t1zEBs1Kg1GIhJkqS48eqrcOqp9Kms5Hby+Bm3Us8XHR5s\nBy1JUvwzEJOU0Jxtk1hi7UTpSTWH9y6GJ5Y3DL0+/TR2a8OUFBgxIvYur4wMaOcupeaWk5PDdde9\nz5w5OVtbJubkzGH8+P3CLk1fU3Fxt5jrb77ZtYUrkSRJ2kVPPAE/+AHU1XFJ0l3cE72kwV1sBy1J\nUvwzEJOU0Jxtk1jyL/qMV3/1/xjLLL7BewxhESmshEpg4jZ3TEoKWhsefXTDwGvIkCAQU6ieeaYH\nU6Z8b+vtiop+TJnyPUaPLmH48BAL09fm+6wkSYpb0Sj87//CtddCt24wfTrTLzgEShve1XbQkiTF\nPwMxSQnN2TYJoLISnnwSpk3jkn/MJpcNAKyjE4vZm7lkk57dl4MnHPhF4DV4sK0NWzl3byYO32cl\nSVJcWr8efvxj+MtfYOBAmDULhg8nL69kc5v97eXm1oZQpCRJakoGYpISWl5ejScz8eiTT2DatODP\nnDlQXw/A+x1GMJnvMY1xvMsBRAlaG2Z8XMWyy/3gPZ64qyhx+D4rSZLizvLlMG5ccK5x6KHBBXhp\naQCbZ02XUFSUTFlZCunp1eTm1jqDWpKkBGAgJimheTITR957LwjApk6F4uJgLSkJjjgCTj8dxo3j\nwKGDqKfhfC9DlPjjrqLE4fusJEmKKx9+CGPHwsKFMGECPPQQJCdvd5dIJJNIBIqLi8nKygqpUEmS\n1NQMxCQlPE9mWqloNAi+toRg778frHfoAMcfH4Rgp5669UpNgLS05YYoCcJdRYnF91lJkhQX/vnP\n4Dyjuhp++Uu44QZo1/CCO0mSlJgMxCRJLWfTJpg7NwjApk8PWiNCcEXm5l1gjB0LKbF3fBmiJA53\nFUmSJKlF/d//wU9+Enz/4INw/vlhViNJkkJgICZJal7r18Pzzwch2JNPQlVVsN6zJ5x7bhCEHX88\ndO78lU8ViWQyZ84Snnkmg7q6DnToUMeJJ35KJDK4eV+DmoW7iiRJktTs6uvh2mvhppuCC++mToVv\nfSvsqiRJUgjcFy4p4RUUlJCRsZxDDx1JRsZyCgpKwi4p8a1eDZMnw1lnQWpqsOvrz38O2iFedBE8\n9xxUVMDDD8NppzUqDIPgWM6YMZi6uj2AJOrq9mDGjMEeU0mSJEkN1dbCmWcGYdjQofDaa4ZhkiS1\nYe4Qk5TQCgpKyM//ogVbaWnvzS33SmzN1tSqqmDGjOCKy//3/4KdYQB77w0XXhi0QzzssN3q0V9Y\nGDs4KypKJhLZ5aeVJEmSdltBQQmFhZ0pLx9JWtpy8vJqPOcI02efwSmnwLx5kJMTzC7u3bD9uiRJ\najsMxCQlNAOUZrZsWTALbOpUeOmlYEYYwIgRX8wE++Y3ISmpSf668vLYs8XKymKvS5IkSS3BC/Fa\nmQUL4KSTYOlSOO88+OMfoVOnsKuSJEkhMxCTlNAMUJrBhx8GAdi0acHVllscfngQgI0bF7QjaQZp\nadWUlja8qjM9vRro0yx/pyRJkvRVvBCvFfnHP2DChKCN+403wi9/2WQX6EmSpPhmICYpoRmgNIFo\nFObP/yIEe/fdYL19ezj22CAAO+006N+/2UvJy6vZfKXt9nJza5v975YkSZJ2xAvxWol774XLLgtm\nFz/+eDA/TJIkaTMDMUkJzQBlF23aBK+8EgRgU6cGrUYA9twTTj01aId40knQq1eLlhW0mymhqCiZ\nsrIU0tOryc2ttQ2NJEmSQuWFeCHbtAmuugqKiiA1FZ58MuhgIUmStA0DMUkJzQDla9iwAV54IQjB\npk+HiopgvXt3OPvsIAT77nehS5dQy4xEMolEoLi4mKysrFBrkSRJkgCyslbHDMRGjVqDgVgzW7MG\nzjoLZs6E/fcPvu61V9hVSZKkVshATFLCM0DZibVr4dlng11gs2bBqlXBet++8JOfBO0Qv/Md6Ngx\n3DolSZKkVqy4uFvM9Tff7NrClbQxy5bByScHLd6POw7+9jfo0SPsqiRJUitlICZJbc2KFcFVk1On\nBgOn160L1gcNgh/+MAjBjjgimBEmSZIk6Ss5QywExcVBGFZWBhdeCHfeCXvsEXZVkiSpFTMQk6S2\noLQ0aIM4bRq8+GLQYx/ggAOCAOz00+GggyApKdw6JUmSpDjkDLEWNn06fP/7UFsLt90GV1zhuYwk\nSfpKBmKSlKgWLgwCsKlT4bXXvlg/9NAgBBs3DoYNC68+SZIkKUHk5dWQn98wEMvNrQ2hmgQWjcLv\nfw+RCCQnB+c7p54adlWSJClOGIhJUqKIRuE///kiBHvnnWC9fXv49reDXWCnnQYDBoRbpyRJkpRg\nIpFMoISiomTKylJIT68mN7d287qaxMaNcOml8Mc/Qv/+MGMGjBoVdlWSJCmOGIhJUjyrrw92f02d\nGgRhixcH6506wUknBSHYySdDH9u0SJIkSc0pEskkEoHi4mKysrLCLiexrFwJEybA7NkwcmQQhmVk\nhF2VJEmKMwZikhRvNm4M5oBNmxb0zi8vD9a7dYOJE4NWiCeeGNyWJEmSpHj28ccwdiy8915wsd+j\nj0LXrmFXJUmS4pCBmKSEV1BQQmFhZ8rLR5KWtpy8vJr4a11SUwP/+EcQgs2YEVwhCcHOrwsuCHaC\nHXNMsDNMkiRJkhLBK68Ebd8rK+GKK6CgIGgJL0mStAsMxCQltIKCEvLzvwi/Skt7bx52XdL6Q7GV\nK2HmzCAEe+YZqN08kDszE847LwjBjjwSOvhWLkmSJCnBPP44nH8+1NXBPffAxReHXZEkSYpzfooq\nKaEVFnaOuV5UlEwk0sLFNEZ5OTz5ZDAT7IUXgpM/gP32CwKwceMgKwuSksKtU5IkSZKaQzQKN94I\nv/oVdO8enB+dcELYVUmSpARgICYpoZWXp8RcLyuLvR6KxYuDXWDTpgUtQaLRYD0r64sQ7BvfCLdG\nSZIkSWpu69fDj34EjzwCgwbBrFlwwAFhVyVJkhKEgZikhJaWVk1pae8G6+np1UCfli8IgsDr3XeD\nXWDTpsH8+cF6u3aQkxOEYKedFpwASpIkSVJbUFUVXAw4dy6MHh3sDOvXL+yqJElSAjEQk5TQ8vJq\nNs8M215ubm3LFlJfD/PmBQHY1KmwcGGw3rEjjBkTnPidcgr07duydUmSJElS2D74AMaOhUWL4Iwz\n4P/+D5KTw65KkiQlGAMxSQktEskESigqSqasLIX09Gpyc2s3rzezjRvhpZe+aIdYWhqsd+kCEyYE\nO8HGjAn64kuSJElSW/Tii8G50cqV8D//A9dfH3TPkCRJamIGYpISXiSSSSQCxcXFZGVlNe9fVlsL\n/+//BbvAZsyAFSuC9V694PzzgxO9Y4/1akdJkiRJevBB+MlPICkp2BX2gx+EXZEkSUpgBmKStLtW\nrQqGPU+bBs88A2vXBusZGXDppUE7xKOOgg6+5UqSJEkS9fXBbrCbb4aUlOBc6uijw65KkiQlOD+d\nlaRdUVERDHmeNg1mzw7aIwLss0+wC2zcODjkEFt9SDtRUFBCYWFnystHkpa2nLy8mpZpZypJkqTw\n1NTAeefBlCnB+dPMmbDvvmFXJUmS2gADMWkH/KBWDSxdGgRgU6fCyy8HVzUCjBwZBGCnnw777x+0\n+5C0UwUFJeTnf/GeWlram/z83kCJ77WSJEmJqrwcTjkF3ngj6KIxdSr07h12VZIkqY0wEJNi8INa\nARCNwnvvfRGCvflmsJ6UBEceGYRg48bBXnuFW6cUhwoLO8dcLypKJhJp4WIkSZLU/N55B046CT75\nJJgV9sc/QseOYVclSZLaEAMxKQY/qG3DolH497+DAGzaNPjgg2B9jz3ghBOCXWCnnAJpaeHWKcW5\n8vKUmOtlZbHXJUmSFMeefRbOOANWr4b//V+4+mo7a0iSpBZnICbF4Ae1ieUr21/W1cHcuUEINn06\nlJQE6507w/jxwS6wsWOhZ89wXoCUgNLSqiktbdgeJz29GujT8gVJkiSpedx9N1x+eXCR4RNPBMGY\nJElSCAzEpBj8oDZx7Kj9ZfuNH3HlNz8IdoE9+SQsXx7coWfPYMDzuHFw/PFBKCapyeXl1WxuRbu9\n3NzaEKqRJElSk9u0Ca66CoqKoG/f4LzrsMPCrkqSJLVhBmJSDH5Qmzi2bX/ZldWM4WlOZypjr5kF\n0bXBD9LS4OKLgxDsW98KrlyU1KyCXZolFBUlU1aWQnp6Nbm5tc5plCRJSgSrV8NZZ8GsWXDAATBz\nJgweHHZVkiSpjQs1EKuvr+e6667jgw8+oGPHjtx4440MGjRo68+nT5/On//8Z7p168a4ceOYMGFC\niNWqLfGD2sQRtL+MchW3cSPXsCfrAVgU3ZuuPzs9mAk2ejS0axduoVIbFIlkEolAcXExWVlZYZcj\nSZKkplBSAiefDG+/HXTdmDwZevQIuypJkiRC/QR49uzZbNiwgSeeeIKrrrqKm2++eevPVqxYwR13\n3MFf/vIXHnnkEWbMmMGyZctCrFZtTSSSybJlfZg3bz7LlvUxDItT6f2W83uu5FYirKAX1/Frvsnb\nHN3/NSgogMMPNwyLMwUFJWRkLOfQQ0eSkbGcgoKSsEuSJEmSBFBcHFxw+PbbcNFFwQ4xwzBJktRK\nhLpDrLi4mJycHAAOOuggFixYsPVny5YtY9iwYfTs2ROAESNG8PbbbzNgwIBQapUUhzZs4Ll+P2b/\nsif5L9/gBP7BMoJg85Y8Q5R4tKOZcFBiaC1JkiSFafp0OPtsWLcObr8dcnMhKSnsqiRJkrYKdVvE\nmjVr6Nq169bb7du3p66uDoBBgwaxcOFCqqqqqK2t5dVXX6WmpiasUiXFm9Wr4aST2H/+k3w6KIsz\n0p+ktF1/MjKquOUWw5N4te1MuG0VFSW3cCWSJEmSAIhG4dZbg3b07doFwVhenmGYJElqdULdIda1\na1fWrl279XZ9fT0dOgQl9ejRg6uvvprLLruMnj17csABB5CSkvKVz1lcXNxs9X5drakW7R6PZXzp\nsGIFQ3Nz6fLee6zMyeGz3/6Wh/b8HJi/9T7FxRXhFahdVl4+MuZ6WVmK/07jnMcvcXgsE4vHU5K0\nUxs3wk9/CvffD/37w8yZMDL27+ySJElhCzUQGzVqFC+++CJjxoxh/vz57Lvvvlt/VldXx3//+18e\nffRRNm7cyKRJk7jiiiu+8jmzsrKas+RGKy4ubjW1aPd4LOPM4sUwcSIsXAg//CE977uPUZuDdo9l\n/EtLW05pae8G6+np1R7bOOa/zcThsUwsreV4GspJUiu1ciV873vw/PNBCDZjBmRkhF2VJEnSDoUa\niB133HG8/PLLTJw4kWg0yk033cSMGTOoqanhzDPPBGDcuHF06tSJSZMm0atXrzDLldTavfUWnHgi\nfPYZ/M//wA032KYjweTl1WyeGba93NzaEKqRJEmS2qjFi2HsWHj/fTjlFPjrX2GbkRiSJEmtUaiB\nWLt27bj++uu3WxsyZMjW7y+99FIuvfTSli5LUjx64QU47TRYswbuvBN870hIwey3EoqKkikrSyE9\nvZrc3FpnwkmSJEkt5ZVX4NRToaoKrrwSbrkF2rcPuypJkqSvFGogJklNYvJkOOe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Q2rV8PFF8Mjj0DLlkGnkiRJUgypVyEG0LZtW4YOHcrQoUOjkUeKX7t3w5gx8OijcOSRsHAh9O4d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\nf9CRJEmSJEmSJEmSmqVAC7Gio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      "text/plain": [
       "<matplotlib.figure.Figure at 0x221b68da3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MCL.plot(plots=['PI1','PI2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Munich model's effect on ultimates can be drastic by origin year.  For this data, a paid Mack approach would understand the latest origin period relative to both the Munich method and the Mack Incurred ultimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221b753fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x221b7582668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MCL.plot(plots=['MCLvsMackpaid','MCLvsMackinc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An 'R' view\n",
    "\n",
    "Not specifying plots will render a view similar to the chainladder package in R which includes residual graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221b7043860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MjAy8fPlS6ISxceNGtcGk169fw8HBAe/evROW3bt3TyHYd+TIEXh6eiq0uz58+ICTJ0/i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\nBwYGqlxeFM88qHgxYEZECrp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vd//3cvdgYAAAB4QVGR2XvsnwHZddu3m9GL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x221b71011d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MCL.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}