plot_graphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import seaborn as sns\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "# sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "sns.set_context(\"paper\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "FOLDER = 'figures/'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Design</th>\n",
       "      <th>Bits</th>\n",
       "      <th>Exp</th>\n",
       "      <th>Area</th>\n",
       "      <th>Power</th>\n",
       "      <th>Delay</th>\n",
       "      <th>Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1072.041611</td>\n",
       "      <td>1.1954</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.163674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1084.036811</td>\n",
       "      <td>1.1753</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.256576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2207.822421</td>\n",
       "      <td>2.4366</td>\n",
       "      <td>2.70</td>\n",
       "      <td>6.578820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2231.577620</td>\n",
       "      <td>2.4828</td>\n",
       "      <td>2.67</td>\n",
       "      <td>6.629076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>5144.764831</td>\n",
       "      <td>5.6565</td>\n",
       "      <td>4.07</td>\n",
       "      <td>23.021955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Flo-Posit</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>683.020804</td>\n",
       "      <td>0.7207</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.542298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Flo-Posit</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>688.430403</td>\n",
       "      <td>0.6920</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.349400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Flo-Posit</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1500.811205</td>\n",
       "      <td>1.7047</td>\n",
       "      <td>3.03</td>\n",
       "      <td>5.165241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Flo-Posit</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1481.995205</td>\n",
       "      <td>1.6927</td>\n",
       "      <td>2.92</td>\n",
       "      <td>4.942684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Flo-Posit</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>3393.230403</td>\n",
       "      <td>4.1340</td>\n",
       "      <td>4.79</td>\n",
       "      <td>19.801860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MArTo</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>585.883201</td>\n",
       "      <td>0.6590</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.159840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MArTo</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>556.718401</td>\n",
       "      <td>0.6257</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.094975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MArTo</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1332.408004</td>\n",
       "      <td>1.5131</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.449868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MArTo</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1293.364804</td>\n",
       "      <td>1.4491</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.550295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MArTo</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>2929.651213</td>\n",
       "      <td>3.1911</td>\n",
       "      <td>3.81</td>\n",
       "      <td>12.158091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Proposed</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>596.937603</td>\n",
       "      <td>0.6137</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.190578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Proposed</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>576.240003</td>\n",
       "      <td>0.5908</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.057532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Proposed</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1263.494410</td>\n",
       "      <td>1.2643</td>\n",
       "      <td>2.89</td>\n",
       "      <td>3.653827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Proposed</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1176.705608</td>\n",
       "      <td>1.2216</td>\n",
       "      <td>2.89</td>\n",
       "      <td>3.530424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Proposed</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>2622.950420</td>\n",
       "      <td>2.7736</td>\n",
       "      <td>4.30</td>\n",
       "      <td>11.926480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Design  Bits  Exp         Area   Power  Delay     Energy\n",
       "0     PACoGen     8    1  1072.041611  1.1954   1.81   2.163674\n",
       "1     PACoGen     8    2  1084.036811  1.1753   1.92   2.256576\n",
       "2     PACoGen    16    1  2207.822421  2.4366   2.70   6.578820\n",
       "3     PACoGen    16    2  2231.577620  2.4828   2.67   6.629076\n",
       "4     PACoGen    32    2  5144.764831  5.6565   4.07  23.021955\n",
       "5   Flo-Posit     8    1   683.020804  0.7207   2.14   1.542298\n",
       "6   Flo-Posit     8    2   688.430403  0.6920   1.95   1.349400\n",
       "7   Flo-Posit    16    1  1500.811205  1.7047   3.03   5.165241\n",
       "8   Flo-Posit    16    2  1481.995205  1.6927   2.92   4.942684\n",
       "9   Flo-Posit    32    2  3393.230403  4.1340   4.79  19.801860\n",
       "10      MArTo     8    1   585.883201  0.6590   1.76   1.159840\n",
       "11      MArTo     8    2   556.718401  0.6257   1.75   1.094975\n",
       "12      MArTo    16    1  1332.408004  1.5131   2.28   3.449868\n",
       "13      MArTo    16    2  1293.364804  1.4491   2.45   3.550295\n",
       "14      MArTo    32    2  2929.651213  3.1911   3.81  12.158091\n",
       "15   Proposed     8    1   596.937603  0.6137   1.94   1.190578\n",
       "16   Proposed     8    2   576.240003  0.5908   1.79   1.057532\n",
       "17   Proposed    16    1  1263.494410  1.2643   2.89   3.653827\n",
       "18   Proposed    16    2  1176.705608  1.2216   2.89   3.530424\n",
       "19   Proposed    32    2  2622.950420  2.7736   4.30  11.926480"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adder_df = pd.read_json(\"Addition/synopsys_results.json\")\n",
    "# adder_df = adder_df[(adder_df['Exp']==1) | (adder_df['Bits']==32)]\n",
    "adder_df  # .head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    aux_adder = adder_df.groupby([\"Design\", \"Bits\"], sort=False).sum()\n",
    "    # print(aux_adder.head())\n",
    "    def two_dec(x): return \"%.2f\" % (x)\n",
    "    print(aux_adder.to_latex(multirow=True,\n",
    "                             position=\"!t\",\n",
    "                             column_format=\"lccccc\",\n",
    "                             # header=[\"Area (\\\\si{\\\\micro\\\\metre\\\\squared})\", \"Power (\\si{\\milli\\watt})\", \"Delay (\\si{\\nano\\second})\", r\"Energy (\\si{\\pico\\joule})\"],\n",
    "                             formatters=[two_dec, two_dec, two_dec, two_dec],\n",
    "                             caption=\"Synthesis results for different \\positenv{n}{2} adder designs.\"),\n",
    "          )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Design</th>\n",
       "      <th>PACoGen</th>\n",
       "      <th>Flo-Posit</th>\n",
       "      <th>MArTo</th>\n",
       "      <th>Proposed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bits</th>\n",
       "      <th>Exp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>1</th>\n",
       "      <td>1072.041611</td>\n",
       "      <td>683.020804</td>\n",
       "      <td>585.883201</td>\n",
       "      <td>596.937603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>1</th>\n",
       "      <td>2207.822421</td>\n",
       "      <td>1500.811205</td>\n",
       "      <td>1332.408004</td>\n",
       "      <td>1263.494410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <th>2</th>\n",
       "      <td>5144.764831</td>\n",
       "      <td>3393.230403</td>\n",
       "      <td>2929.651213</td>\n",
       "      <td>2622.950420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Area                                       \n",
       "Design        PACoGen    Flo-Posit        MArTo     Proposed\n",
       "Bits Exp                                                    \n",
       "8    1    1072.041611   683.020804   585.883201   596.937603\n",
       "16   1    2207.822421  1500.811205  1332.408004  1263.494410\n",
       "32   2    5144.764831  3393.230403  2929.651213  2622.950420"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_df = adder_df[[\"Area\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack('Design')\n",
    "power_df = adder_df[[\"Power\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack('Design')\n",
    "delay_df = adder_df[[\"Delay\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack('Design')\n",
    "energy_df = adder_df[[\"Energy\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack('Design')\n",
    "\n",
    "area_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        [ 8 bits   | 16 bits  | 32 bits  ]\n",
      "area  : [0.12603306 0.15812568 0.22700492]\n",
      "power : [0.14846677 0.25834458 0.32907596]\n",
      "delay : [0.09345794 0.04620462 0.10229645]\n",
      "energy: [0.22804931 0.29261248 0.3977091 ]\n"
     ]
    }
   ],
   "source": [
    "area_gain = (area_df['Area']['Flo-Posit'].values - area_df['Area']\n",
    "             ['Proposed'].values) / area_df['Area']['Flo-Posit'].values\n",
    "power_gain = (power_df['Power']['Flo-Posit'].values - power_df['Power']\n",
    "              ['Proposed'].values) / power_df['Power']['Flo-Posit'].values\n",
    "delay_gain = (delay_df['Delay']['Flo-Posit'].values - delay_df['Delay']\n",
    "              ['Proposed'].values) / delay_df['Delay']['Flo-Posit'].values\n",
    "energy_gain = (energy_df['Energy']['Flo-Posit'].values - energy_df['Energy']\n",
    "               ['Proposed'].values) / energy_df['Energy']['Flo-Posit'].values\n",
    "\n",
    "print('        [ 8 bits   | 16 bits  | 32 bits  ]')\n",
    "print('area  :', area_gain)\n",
    "print('power :', power_gain)\n",
    "print('delay :', delay_gain)\n",
    "print('energy:', energy_gain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OTEwMhYWFwOUlKU0mE3A5AKxWa5H5/318fDCbzfj6+lb4e16ZRhouL2uZl5dnfO2JGU9qVXjfBgdF9nsrysvLK/JaidxKAoOaVqid3W7Xz305VHoofP7556SlpXHx4kXOnz9f5HP5VqsVf39/IyTg8huVw+HAarUa20oaby/JlTF8uDw9tI+Ppsktjo+Pj6bOllvW2QsV+6PM09OzyHuEXJaenl7s9koPhY0bNwKwa9cuvvjiCy5evMjevXvp0KEDhw4dIiQkhDp16pCdnY2/vz8Wi4UmTZqQm5vLpUuXOH/+vC6uiohUEZd/JHX8+PEkJCRgsViIjo7G29ubuLg4YmNjsdlsxMbGAjBx4kRGjBiBw+Fg1qxZri5LRESK4bJQ6NGjBz169AAgNTW1yGNhYWFF1hkG6NWrF7169XJVOSIiUga6o1lERAw1/o5mW24ujoKbnxDP7O2FV0DxS3tu3LiR5cuX07BhQwCCg4OZO3cunp7uf3kjIiKM6zoiIuVV40PBUWBjz8jRN72frm+suuHjw4cPZ9iwYQDMnj2bHTt28NBDD9309xURcacaHwru5nQ6sVgszJw5k9/85jcEBwfz6KOPkpiYiMlk4vHHH2fYsGE8/fTTNGzYkIMHDzJw4EBiYmJISUnhgw8+wMPDgylTptC+fXsmTpxIXl4eQUFBvPLKKxw7dowXX3wRm81G7969GT16NBs2bGDdunWadltEbppCoZKkpKTw8ccfYzKZ6NKlC927dyc8PJw+ffoQFRXF8uXLueOOO4iJieHRRx+loKCAyMhI2rdvz+DBg+nfvz8fffQR69ev5/z584wbN45Zs2YREBBAUlISn3/+ORaLhcTERGbOnEnLli155plnjBlVN2zYwA8//MDUqVOr+qUQkVuYQqGSXD18BJcXzWnWrBlw+Y7KwMBAANq2bcvJkycxm83cfffdmEwm7rrrLk6ePEm7du0wm80EBgZis9lo27YtXbt25U9/+hONGjXi/vvv5/jx48yYMQOAixcvcvr0aYKCgvD09KRNmzbUrl3b/QcvIjWGPn3kQlfuzPbw8CAnJweHw0FmZiaNGjXC4XDw73//m8LCQn788Ufq16/PwYMHcTgc5OTkAHDo0CF8fHxYs2YNDRo0YNeuXTRt2pTExERSU1MZMmQITZs25aeffqKgoIAff/yR/Pz8qjxkEbnFqafgBpMnT2bcuHHY7XYGDRpkfEpp2bJlnDp1iiFDhtCkSRP69u3LkCFDsNvtvPjiizRv3pwFCxbwzjvvULduXUaNGkXTpk2Jj4/n119/JTQ0lMGDBzNixAiioqJo3bq1egoiclNMzqtnp7sFpaen06VLF+PrH374ocj8Pu74SGpFxMTEsHz5cvz8Sl73ubJd+9qI3ErOXsjjj7M+KXe75Bl9qVdX86Fd69r3zitqfE+hMt/IRURquhofCtXVtVN/iIhUB7rQLCIihhoXCl5eXly8eLGqy6h2Ll68iJdXyavHiYhADRw+aty4MadPn+bs2bNVXUq14uXlRePGjau6DBGp5mpcKJjNZpo2rdiyfSIit7saN3wkIiIVp1AQERGDQkFERAwKBRERMSgURETEUOM+fSQicjV/cwH23LxytzN5euHhc/tNk6NQEJEazeSwc/y1P5W7XfNnb7wEb02l4SMRETEoFERExKBQEBERg0JBREQMN7zQvGfPHt5++2327duH2WzG29ubu+++m6ioKDp37uyuGkVExE1KDIWXXnqJO++8kwkTJhASEmIsQn/48GE++OAD3nvvPWbOnOm2QkVExPVKDIXnnnuOOnXqXLe9devWTJo0iQsXLri0MBERcb8SQ2H27Nl0796dEydOMGnSpOser1u3rksLExER9yvxQrO3tze+vr58++237qxHRESqUImh4OvrS//+/QkMDHRnPSIiUoVKHD564YUXAFi4cCH/+te/yM/PNx7r1q3bDXdqtVp57rnnyM3NpXfv3gwZMoTY2FgsFgvh4eGMHj2a7OxspkyZgt1uZ+jQoQwYMIDMzExmzpyJ0+lk0qRJ9OzZs5IOU0REyqLUuY+efvppAgICilxDKC0U3n//fR555BEiIyMZMWIEDoeDiIgI+vfvz6hRoxgwYABJSUnEx8cTFhbG8OHD6devH4sXL2bJkiX4+fkxZswYhYKIiJuVGgoeHh68/vrr5drpkCFDKCwspKCggLy8PPbv309ERAQmk4nu3buTkZHBwYMH6dSpEyaTidDQUA4fPsy5c+cIDg4GwMfHhwsXLuiCtoiIG5UaCg888ADr168nJCTE2FZaTwEuDyFFRETQpk0bLl26hK+vL3D5zd5iseBwODCZTMY2q9WK0+k02l/ZplAQEXGfUkNhz549eHh4sG/fPmNbWUIhICCALVu2sHTpUt544w2sViv+/v5YrVYaN25s3AwHGI9dCQmAvLw8/P39y3QQWVlZZXqeiNy6AoOaVrCls/SnFMNut/Pv2/C9pdRQyM/PZ82aNeXaaXJyMi1atODhhx/Gz8+PUaNGkZaWxmOPPcbu3bsZOHAgbdq0Ye/evXTo0IFDhw4REhJCnTp1yM7Oxt/fH4vFQkBA2Ra4aNeuXbnqE5Fbz9kL5V8o5zJT6U8phqenZ41+b0lPTy92e6mh0KxZM1JTU2nbtq3xl3xpPYXHHnuM559/njfeeIOgoCCmTZvGtGnTSE5OJjw8nIYNGzJ+/HgSEhKwWCxER0fj7e1NXFwcsbGx2Gw2YmNjy3+UIiJyU0oNBZvNRmZmJpmZmca20kKhQYMG1/UuVq0quopRkyZNSE1NLbItLCyM9evXl1q0iIir5Xl6YMs7X+52XmYvAmr5VX5BblJqKMydO9cddYiIVCs2RyFjP5xe7nbLn7i13zNLDYXevXtjMplwOBycPXuW+vXr89lnn7mjNhERcbNSQ+HqAMjJyeHZZ591aUEiIlJ1yrXympeXFx4eHq6qRUREqli5ho9ycnKoVauWsW3r1q3uqFFERNykXMNHIiJSs5U4fDR//nyys7OLfezEiRP6VJKISA1UYk9h5MiRLFq0iOPHj3PXXXdRr149Ll26RFZWFi1bttQFZxGRGqjEUGjQoAFz5swhNzeXjIwMzp8/T2BgIJMnT8bP79a9MUNEREpW6jWFgIAAHnjgAXfUIiIiVaxcH0kVEZGardSegoiIlJ1PAeRbc8rdzuzthVcZZ4Z2pVJDYfv27fzP//wPhYWFOJ1Ozp49y7p169xRm4jILcdks7Pn6THlbtf1jVWlP8kNSh0+WrhwITExMfj4+PDb3/6Wpk0rutCFiIhUd6WGQoMGDejatSseHh4MGjSoxHsXRETk1ldqKPj6+rJt2zYKCwv58MMPyckp/1iZiIjcGkoNhZdfftm4P2H//v3MmDHDHXWJiEgVKPVCs5+fH9999x0//fQTDz/8MKGhoe6oS0REqkCpPYXp06fzyy+/8PXXX5Ofn098fLw76hIRkSpQaiicOnWKCRMmUKtWLR566CGsVqs76hIRkSpQpjuaT5w4AcDZs2fx9NT9biIiNVWp7/DTp08nPj6eo0ePMmHCBKZPL/9C1iIicmsoNRS2bdumO5hFRG4TpQ4fffPNN7qOICJymyi1p3D8+HG6d+9OUFAQJpOJM2fOcODAAXfUJiIiblZqT2Hr1q3s3LmTkSNH4uvrS9u2bd1Rl4iIVIEb9hT279/P2rVrSUtLA2DFihW0atXKLYWJiIj7ldhTGDRoEMnJyfzud7/jo48+4q677lIgiIjUcCX2FLp06UJaWhrbt2+nXr16mEwmd9Yl4hK5lgIK7IXlauPt6UGAn7eLKhKpXkoMhYSEBAoLC/niiy9ISkoiIyODpKQkHn/8ca2pILesAnshf5z1SbnaJM/o66JqRKqfG15o9vDwIDw8nGXLlvHRRx/h4+PDhAkT3FWbiIi4WZmmuQAIDAzkj3/8I++//74r6xERkSpU5lAQEZGar9JD4dKlS4wZM4Zhw4bx7LPPkpuby9NPP01UVBSrVl1emDo7O5vo6GgiIyONnkdmZiaRkZEMHjyYb775prLLEhGRMqj0UPjHP/5Bnz59eOutt2jVqhUpKSlERESwdu1adu7cyc8//0xSUhLx8fGkpqaydu1aCgoKWLx4MUuWLGH16tW8+uqrlV2WiIiUQaXPg/3kk0/i5eUFQGFhIW+99RabNm3CZDLRvXt3MjIyOHjwIJ06dcJkMhEaGsrhw4c5d+4cwcHBAPj4+HDhwgXq1q1b2eWJiMgNVHoo+Pv7A7B3717S0tIICwvD19cXuPxmb7FYcDgcxn0PPj4+WK1WnE6nsY8r28oaCllZWZV8FFJTBQaV/+PUdrtdP2PVQEXO3WXO0p9Saa0q3s5ut3O0GvycuWTFnPT0dObMmcPrr7/OzJkzsVqt+Pv7Y7Vaady4MWbz/x+1uvLY1TfH5eXlGeFSFu3atavU+qXmOnshr9xtPD099TNWDVTk3F1WsRtvK3q7bkXbufvnLD09vdjtlX5N4dixY7z88sskJSXRsGFDOnToQFpaGk6nk927d9OxY0fatGnD3r17sdlsHDp0iJCQEOrUqUN2dja5ublYLBYCAgIquzQRESlFpfcU/vu//5vc3Fzi4uIAGD58OBs2bCA5OZnw8HAaNmzI+PHjSUhIwGKxEB0djbe3N3FxccTGxmKz2YiNja3sskREpAwqPRRmz5593bY+ffoU+bpJkyakpqYW2RYWFsb69esruxwRESkH3bwmIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKIiBgUCiIiYnDJcpwiNYm/uQB7bvmXgjR5euHhoxUE5daiUBAphclh5/hrfyp3u+bPrnJBNSKupeEjERExKBRERMSgUBAREYNCQUREDAoFEREx6NNHIi6S5+mBLe98udt5mb0IqOVX+QWJlIFCQcRFbI5Cxn44vdztlj8x1wXViJSNho9ERMSgnoJINeNTAPnWnHK3M3t74RWgO6jl5igURKoZk83OnqfHlLtd1zd0B7XcPA0fiYiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIwaWfPpo7dy733nsvXbt2JTY2FovFQnh4OKNHjyY7O5spU6Zgt9sZOnQoAwYMIDMzk5kzZ+J0Opk0aRI9e/Z0ZXkVkmspoMBeWO523p4eBPh5u6AiEZHK45JQcDgcTJs2jd27d3Pvvfeydu1aIiIi6N+/P6NGjWLAgAEkJSURHx9PWFgYw4cPp1+/fixevJglS5bg5+fHmDFjqmUoFNgL+eOsT8rdLnlGXxdUIyJSuVwWCo899hhNmzYFICMjg9///veYTCa6d+9ORkYGBw8epFOnTphMJkJDQzl8+DDnzp0jODgYAB8fHy5cuEDdunVdUaKIiBTDJaHg6enJgw8+SEZGBgCXLl3C19cXuPxmb7FYcDgcmEwmY5vVasXpdBr7uLKtLKGQlZXlgqMoXmBQ0wq1s9vtbq1Tilex8+cs/SmV1qri7ex2O0dr8M9YRX/3dP7Kxy13NPv5+WG1WvH398dqtdK4cWPM5v9/jfvKY1dCAiAvLw9/f/8y7b9du3aVXnNJzl4o/wLucDko3VmnFK9i589U+lMqrVXF29X0n7GK/u7p/BUvPT292O1u+fRRhw4dSEtLw+l0snv3bjp27EibNm3Yu3cvNpuNQ4cOERISQp06dcjOziY3NxeLxUKA5nEREXErt/QUoqOjmTJlCsnJyYSHh9OwYUPGjx9PQkICFouF6OhovL29iYuLIzY2FpvNRmxsrDtKExGRq7g0FCZOnGj8f9WqopN1NWnShNTU1CLbwsLCWL9+vStLEhGRG9DNayIiYlAoiIiIQaEgIiIGhYKIiBgUCiIiYlAoiIiIQaEgIiIGhYKIiBjcckezgL+5AHtu+eduMXl64eGj6T5ExD0UCm5ictg5/tqfyt2u+bOrSn+SiEgl0fCRiIgY1FOooWy5uTgKbOVuZ/b2wkuz04rcthQK1Vyepwe2vPPlbudbUEj6yPIPV3V9Q8NVIrczhUI1Z3MUMvbD6eVulxL+kguqEZGaTtcURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERFDtVqO02azER8fz88//0xYWBh//vOfq7okEZHbSrXqKXzyySeEhYXx97//HavVyr/+9a+qLklE5LZSrUIhIyODbt26AXDffffx7bffVnFFIiK3l2oVCpcuXcLX1xcAHx8fLBZLFVckInJ7MTmdTmdVF3HFyy+/TP/+/encuTMffPABOTk5PPXUUzdsk56e7qbqRERqli5duly3rVpdaO7QoQNpaWl07tyZnTt3MmjQoFLbFHdQIiJSMdVq+Khfv35kZWURGRmJh4cH//Vf/1XVJYmI3Faq1fCRiIhUrWrVUxARkaqlUBAREYNCQUREDAoFERExKBSq0Pbt29m6dSs5OTnExMQQGRnJqlWrbtjm1KlTjBw5EoCCggJeeukld5Qq/+fKOQNwOp1MnDiR77//HoBz584xduxYoqKimDNnzg33c+DAAeLi4ox2ixYtcm3hYpy7M2fOEBMTw+DBg3nvvfcAOHjwIEOHDiUyMpLly5eXuI81a9YQGRlJVFQUBw4cqJHnTqFQhd555x169+7NP//5T/r168e6devYsmVLiXdy79mzh8mTJ3P+/HkAvL29ady4sW7gc6Mr58xisTB27Ngi83OtXLmSIUOGsG7dOho1akR+fn6x+9iyZQsvvPACNpsNgDvvvBOr1cqpU6fccgy3qyvnLjU1lTFjxrBu3TrefvttnE4nr776KvPnz2f9+vXs2LGDs2fPXtf+/PnzfP7556xfv56FCxeydOnSGnnuFApVJDMzk8aNG2MymWjfvj2//vordrsdAC8vr2LbmM1mVq5cWWRbeHg4//jHP1xerxQ9Z/n5+YwfP56ePXsajx84cIDMzExiYmK44447qFWrVrH78ff3v+6vy9/+9rfGX61S+a4+d3Fxcdx///1cunQJAJPJxKxZs2jWrBkAdrsdT8/r7+sNCAhg6dKlxnOu/J7WtHOnUKgi+/bto1WrVgDUrl2b1NRU+vXrR2hoKN7e3sW2ueeee6hbt26RbS1atCAzM9Pl9UrRcxYYGEinTp2KPH7mzBlatGjB6tWrWbt2LefOnSt2Pz179rwuMFq1akVGRoZrCpci585kMnH06FGeeOIJOnbsiNPppF69egAkJycTFhZ23e8ZgIeHB3fccQd5eXn85S9/YfTo0UDNO3cKhSpy8eJF7rjjDgBWrFjBnDlz2LJlC3l5eezZs6fM+zGZTJjNOo3ucPU5K05AQAD33Xcf3t7edOjQgR9//LHM+w4MDOTixYuVUKUU59pz16pVK7744gusVitff/01ACkpKezZs4eEhIQS92OxWBg3bhxDhgwxZlyoaedO7yZVpG7dukb31dfXFz8/P0wmE/Xq1Sv37LAlDTdJ5br6nBXn7rvvZs+ePTidTg4dOkTTpk3LvO9Lly7dMHDk5lx97ubPn8/BgweBy797ZrOZTZs2kZaWxuLFi2/4+zRp0iQGDx7MY489ZmyraedOoVBFOnfuTFZWFgATJkxg4cKFREdHc+nSJXr16sXGjRvZtm1bqfs5duwYbdu2dXW5QtFzVpyxY8fy1ltv8Yc//IHw8HDq16/PypUrb9jmiqysLO65557KLFeucvW5+8Mf/sBLL73EsGHDMJvN9OzZkyVLlnD69GlGjBhBTEwMJ06cuO7cpaens3v3btauXUtMTAwzZswAauC5c0qVGT9+vNPhcBT72N69e53btm0rdR8rV650fvvtt5VdmpTgRuesOB9++KHzyJEjpT7vpZdecp4+ffpmSpNS6NyVjXoKVWjQoEHGZ96v1bhxY3r16nXD9gUFBZw8eZLOnTu7ojwpxo3OWXG6du1KSEjIDZ+Tk5NDrVq1aNSo0c2WJzegc1c2miVVREQM6imIiIhBoSAiIgaFgoiIGKrVGs0i1cmuXbuYPHkyISEhOBwOzGYziYmJzJs3j0WLFvHJJ5/QtWtXAgMDb7ifmJgY8vPzi9zFPHv2bFq0aOHqQxApN4WCyA306tWLefPmAbBhwwaSk5ONeYtSU1MJCwsrNRQAXnnllXLdzCZSVTR8JFJG58+fJzg4mN69e7Nt2zaysrKYOnUqR48eJSoqiqFDhzJ69OgSZ0e91uzZs1m5ciW//vorERERHDlyhISEBBISEhg+fDjDhg3jzJkzLj4qkaLUUxC5ge3btxMTE0NeXh4nTpzgnXfe4c033+TBBx+kXbt2zJ07ly+//JL77ruPCRMm8Pnnn3PhwgWCgoKK7Gfy5MnG8JGvry8rVqxgypQpDB06lAMHDjBkyBBjwrbQ0FDmzZvHxo0bWbFihXHnrIg7KBREbuDq4aOdO3cyffr0657z5JNPsnz5ckaMGEFwcHCxUx4UN3xUu3ZtnnzySZYuXcrChQuN7d26dQOgU6dObNq0qTIPR6RUGj4SKaPg4GBjzQu4PEOt0+lk69atPPDAA6SkpNC6dWv++c9/lml/Z8+eZcOGDYwcObJIKHz33XfA5emeW7duXbkHIVIK9RREbuDK8JGHhwcFBQU8//zzxjKanTt3ZvLkySxYsIBp06ZRq1YtvL29mT17NocPH2b9+vVGz+Lq4SOA8ePH8+abb/Lcc8/x8MMP89RTT/HVV18B8Omnn7Jp0yZq167NggUL3H/QclvTNBciLpCfn09SUhKxsbHlapeQkMDAgQPp0aOHawoTKYWGj0RcwG63M3LkyKouQ6Tc1FMQERGDegoiImJQKIiIiEGhICIiBoWCiIgYFAoiImJQKIiIiOH/AXyjbF/2qZkrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "area_df.plot.bar(\n",
    "    y=\"Area\",\n",
    "    rot=0,\n",
    "    ylabel=\"Area (µm²)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "power_df.plot.bar(\n",
    "    y=\"Power\",\n",
    "    rot=0,\n",
    "    ylabel=\"Power (mW)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delay_df.plot.bar(\n",
    "    y=\"Delay\",\n",
    "    rot=0,\n",
    "    ylabel=\"Delay (ns)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_df.plot.bar(\n",
    "    y=\"Energy\",\n",
    "    rot=0,\n",
    "    ylabel=\"Energy (pJ)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2)\n",
    "# fig, axs = plt.subplots(2, 2, figsize=(10,6))\n",
    "# fig, axs = plt.subplots(2, 2, figsize=(15,8))\n",
    "\n",
    "area_df.plot.bar(ax=axs[0, 0], legend=False,\n",
    "                 y=\"Area\",\n",
    "                 rot=0,\n",
    "                 ylabel=\"Area (µm²)\",\n",
    "                 width=0.9\n",
    "                 )\n",
    "axs[0, 0].grid(False, axis=\"x\")\n",
    "axs[0, 0].set_yticks(np.arange(0, 6000, 1000))\n",
    "# Display values\n",
    "for i, v in enumerate(area_df.values.flatten()):\n",
    "    axs[0, 0].text((i//4)-0.415+(i % 4)*0.225, (v+200 if v < 2500 else v-2200),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "power_df.plot.bar(ax=axs[0, 1], legend=False,\n",
    "                  y=\"Power\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Power (mW)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[0, 1].grid(False, axis=\"x\")\n",
    "axs[0, 1].set_yticks(np.arange(0, 6, 1))\n",
    "# Display values\n",
    "for i, v in enumerate(power_df.values.flatten()):\n",
    "    axs[0, 1].text((i//4)-0.415+(i % 4)*0.225, (v+0.25 if v < 2.5 else v-1.5),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "delay_df.plot.bar(ax=axs[1, 0], legend=False,\n",
    "                  y=\"Delay\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Delay (ns)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[1, 0].grid(False, axis=\"x\")\n",
    "axs[1, 0].set_yticks(np.arange(0, 6, 1))\n",
    "# Display values\n",
    "for i, v in enumerate(delay_df.values.flatten()):\n",
    "    axs[1, 0].text((i//4)-0.415+(i % 4)*0.225, (v+0.25 if i < 8 else v-1.2),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "energy_df.plot.bar(ax=axs[1, 1], legend=False,\n",
    "                   y=\"Energy\",\n",
    "                   rot=0,\n",
    "                   #  logy=\"sym\",\n",
    "                   ylabel=\"Energy (pJ)\",\n",
    "                   width=0.9\n",
    "                   )\n",
    "axs[1, 1].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(energy_df.values.flatten()):\n",
    "    axs[1, 1].text((i//4)-0.415+(i % 4)*0.225, (v+1 if i < 8 else v-7),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "# plt.legend(loc=\"upper left\")\n",
    "axLine, axLabel = axs[1, 1].get_legend_handles_labels()\n",
    "fig.legend(axLine, axLabel,\n",
    "           loc='upper center',\n",
    "           ncol=4,\n",
    "           bbox_to_anchor=(0.5, 1.05)\n",
    "           )\n",
    "\n",
    "# fig.suptitle(\"Posit Adder synthesis\")\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(\"figs/adder.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, axs = plt.subplots(2, 2)\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 4))\n",
    "# fig, axs = plt.subplots(2, 2, figsize=(15,8))\n",
    "\n",
    "area_df.plot.bar(ax=axs[0, 0], legend=False,\n",
    "                 y=\"Area\",\n",
    "                 rot=0,\n",
    "                 ylabel=\"Area (µm²)\",\n",
    "                 width=0.9\n",
    "                 )\n",
    "axs[0, 0].grid(False, axis=\"x\")\n",
    "axs[0, 0].set_yticks(np.arange(0, 6000, 1000))\n",
    "# Display values\n",
    "for i, v in enumerate(area_df.values.flatten()):\n",
    "    axs[0, 0].text((i//4)-0.39+(i % 4)*0.23, (v+200 if v < 2500 else v-2200),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "power_df.plot.bar(ax=axs[0, 1], legend=False,\n",
    "                  y=\"Power\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Power (mW)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[0, 1].grid(False, axis=\"x\")\n",
    "axs[0, 1].set_yticks(np.arange(0, 6, 1))\n",
    "# Display values\n",
    "for i, v in enumerate(power_df.values.flatten()):\n",
    "    axs[0, 1].text((i//4)-0.39+(i % 4)*0.23, (v+0.25 if v < 2.5 else v-1.5),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "delay_df.plot.bar(ax=axs[1, 0], legend=False,\n",
    "                  y=\"Delay\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Delay (ns)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[1, 0].grid(False, axis=\"x\")\n",
    "axs[1, 0].set_yticks(np.arange(0, 6, 1))\n",
    "# Display values\n",
    "for i, v in enumerate(delay_df.values.flatten()):\n",
    "    axs[1, 0].text((i//4)-0.39+(i % 4)*0.23, (v+0.25 if i < 8 else v-1.2),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "energy_df.plot.bar(ax=axs[1, 1], legend=False,\n",
    "                   y=\"Energy\",\n",
    "                   rot=0,\n",
    "                   #  logy=\"sym\",\n",
    "                   ylabel=\"Energy (pJ)\",\n",
    "                   width=0.9\n",
    "                   )\n",
    "axs[1, 1].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(energy_df.values.flatten()):\n",
    "    axs[1, 1].text((i//4)-0.39+(i % 4)*0.23, (v+1 if i < 8 else v-7),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "# plt.legend(loc=\"upper left\")\n",
    "axLine, axLabel = axs[1, 1].get_legend_handles_labels()\n",
    "fig.legend(axLine, axLabel,\n",
    "           loc='upper center',\n",
    "           ncol=4,\n",
    "           bbox_to_anchor=(0.5, 1.05)\n",
    "           )\n",
    "\n",
    "# fig.suptitle(\"Posit Adder synthesis\")\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(\"figs/adder.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Design</th>\n",
       "      <th>Bits</th>\n",
       "      <th>Exp</th>\n",
       "      <th>Area</th>\n",
       "      <th>Power</th>\n",
       "      <th>Delay</th>\n",
       "      <th>Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1154.596807</td>\n",
       "      <td>1.4514</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.249670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3355.128017</td>\n",
       "      <td>5.0461</td>\n",
       "      <td>2.53</td>\n",
       "      <td>12.766633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PACoGen</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>10309.992072</td>\n",
       "      <td>18.3376</td>\n",
       "      <td>4.47</td>\n",
       "      <td>81.969072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Flo-Posit</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>616.224003</td>\n",
       "      <td>0.7376</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.084272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Flo-Posit</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2250.628813</td>\n",
       "      <td>3.5270</td>\n",
       "      <td>2.74</td>\n",
       "      <td>9.663980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Design  Bits  Exp          Area    Power  Delay     Energy\n",
       "0    PACoGen     8    1   1154.596807   1.4514   1.55   2.249670\n",
       "2    PACoGen    16    1   3355.128017   5.0461   2.53  12.766633\n",
       "4    PACoGen    32    2  10309.992072  18.3376   4.47  81.969072\n",
       "5  Flo-Posit     8    1    616.224003   0.7376   1.47   1.084272\n",
       "7  Flo-Posit    16    1   2250.628813   3.5270   2.74   9.663980"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_df = pd.read_json(\"Multiplication/synopsys_results.json\")\n",
    "mult_df = mult_df[(mult_df['Exp'] == 1) | (mult_df['Bits'] == 32)]\n",
    "mult_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Design</th>\n",
       "      <th>PACoGen</th>\n",
       "      <th>Flo-Posit</th>\n",
       "      <th>MArTo</th>\n",
       "      <th>Proposed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bits</th>\n",
       "      <th>Exp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>1</th>\n",
       "      <td>1154.596807</td>\n",
       "      <td>616.224003</td>\n",
       "      <td>615.753601</td>\n",
       "      <td>585.412802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>1</th>\n",
       "      <td>3355.128017</td>\n",
       "      <td>2250.628813</td>\n",
       "      <td>2170.896009</td>\n",
       "      <td>2070.700813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <th>2</th>\n",
       "      <td>10309.992072</td>\n",
       "      <td>8132.980841</td>\n",
       "      <td>7690.334447</td>\n",
       "      <td>7468.305652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Area                                       \n",
       "Design         PACoGen    Flo-Posit        MArTo     Proposed\n",
       "Bits Exp                                                     \n",
       "8    1     1154.596807   616.224003   615.753601   585.412802\n",
       "16   1     3355.128017  2250.628813  2170.896009  2070.700813\n",
       "32   2    10309.992072  8132.980841  7690.334447  7468.305652"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_df = mult_df[[\"Area\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack(0)\n",
    "power_df = mult_df[[\"Power\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack(0)\n",
    "delay_df = mult_df[[\"Delay\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack(0)\n",
    "energy_df = mult_df[[\"Energy\", \"Design\", \"Bits\", \"Exp\"]].groupby(\n",
    "    [\"Design\", \"Bits\", \"Exp\"], sort=False).sum().unstack(0)\n",
    "\n",
    "area_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.05       0.07994566 0.0817259 ]\n",
      "[-0.07266811  0.04213212  0.07722673]\n",
      "[0.25170068 0.24452555 0.2154195 ]\n",
      "[0.19732318 0.27635529 0.27601009]\n"
     ]
    }
   ],
   "source": [
    "area_gain = (area_df['Area']['Flo-Posit'].values - area_df['Area']\n",
    "             ['Proposed'].values) / area_df['Area']['Flo-Posit'].values\n",
    "power_gain = (power_df['Power']['Flo-Posit'].values - power_df['Power']\n",
    "              ['Proposed'].values) / power_df['Power']['Flo-Posit'].values\n",
    "delay_gain = (delay_df['Delay']['Flo-Posit'].values - delay_df['Delay']\n",
    "              ['Proposed'].values) / delay_df['Delay']['Flo-Posit'].values\n",
    "energy_gain = (energy_df['Energy']['Flo-Posit'].values - energy_df['Energy']\n",
    "               ['Proposed'].values) / energy_df['Energy']['Flo-Posit'].values\n",
    "\n",
    "\n",
    "print(area_gain)\n",
    "print(power_gain)\n",
    "print(delay_gain)\n",
    "print(energy_gain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "area_df.plot.bar(\n",
    "    y=\"Area\",\n",
    "    rot=0,\n",
    "    ylabel=\"Area (µm²)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "power_df.plot.bar(\n",
    "    y=\"Power\",\n",
    "    rot=0,\n",
    "    ylabel=\"Power (mW)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delay_df.plot.bar(\n",
    "    y=\"Delay\",\n",
    "    rot=0,\n",
    "    ylabel=\"Delay (ns)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_df.plot.bar(\n",
    "    y=\"Energy\",\n",
    "    rot=0,\n",
    "    logy=\"sym\",\n",
    "    ylabel=\"Energy (pJ)\"\n",
    ")\n",
    "\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eTXF0r2Hu8IqNF3ceuH79OqdPn2bgwIG0atWKUaNKVheQAAoHN9zrDSDqyg+oUxMMnneQpqFKMK78mERuhczWsaBDLBAiSQlmFX/vHJ5NRyORSNCo03l4YScVGg3j3l8bRJIyQebOAyEhIQBUrFiRKVOmUK9ePcaPH8/FixepU6eOeYMUCpxUpqB87d4kR98hPSkaKztX3XMSjYq7n71n1HEqjt+cdyMzEUlKMCuNKhV1aiJyG0fUaYloVCkgkaBVv7oTJxITEwkJCSEuLg5XV1fq1KmDjY1p3Z9169bFxsYGqVRKs2bNuHnzplFJSlSbt3yubp6kxN0n6vL3SK1scKpQl5jbfyCRynHyrE8p7yb/tdTmepysVCoVNyz0dy+SlGBWZap25N6fnyNTOKBRpVCuVi+ibx7DuWJjc4dW5J49e8bq1asJDw/H29sbNzc34uPj+fTTT/Hz82PcuHFGT35YsWIFLVu2pE2bNly4cIEhQ4YY9briUm3+VfYsLpmoKz/gXn8QquQYIs9twbf9bCRSGff+XJ8lSUlyPU5W5t5pILfdA0SSEszK3q0qldpMR52mRG7tAICtayW9NiWlbz0vX3zxBaNHj9aNK2UVERHB5s2bmTlzplHHeu+995g6dSpffPEFjRo1ombNmgUdrmBOWg1WtqWwsi2Fq19rpDKr/x43/u6puBBJSjA7iUSiS1CZnoT+Qtlqb2U8X0L61vPSsWNHKlSokO1z3t7eRiWorDsPvLjLgFByOFaoS8TJtVRsMQ7Xym0BeHhhZ77LiSXLZaQnx+bZzkpqhaO1fb6+R34VWZLatGkTJ0+eBODGjRsMGjSIAwcO6E7K7du3c/XqVebPn49Wq2XixIk0bdqUP/74g7Vr1yKTyZg/fz7+/v5FFbJgRk4V6pg7hCK3efNmbty4gb+/P82aNaN58+Z4eXmZOyzBArn4tMCpQl0kkudFg0pX6YDCIX9r4dI1akb/PCvPdls7BpOaGG3UMaUKK6wcX75XI9ckdf78eXbu3MnFixeRSqUoFApq1apFv379qFu3rknf6L333uO9994jMjKSWbNmUbFiRd5//3169+6ta7NmzRrWrl2Lvb09o0aNomnTpnz22Wds2bKFmJgYFi5cyIYNG/L3TgWLlRJ7n+SYjAKzUquMArPWTh7mDqvIffbZZwDcunWLc+fO8emnnxIVFUXFihXFhp+CAZlC/45G4VCW+MiLhXqBJ0lXcX64ccsZGnxVML0aOSapBQsW4OLiwtixY/H19UUqzcjYN2/e5KeffuL7779n/vz5Jn/DdevWMX78eI4ePcrly5f57rvv6NGjB7169SImJoby5csDYGtrS2RkJPb29rr/njx5ks+3KViq6JvHSEuMwsalIklP76GwL83TpzewK+1HqUpNTT6eJXdbGEutVpOUlERKSgpSqRRbW1tzhyQUE1qNytwhFLgck9SHH36Ik5OTweOVK1dm4sSJxMXFmfzNUlJSePDgAfXq1ePRo0d069aNihUrMmTIEFq0aIE2y6Cfra0tUqkUOzs7k7+PUHwoo8LwavY+AM4VGxN59is8G4/g7qnP85WkjO222NAl2ORjF7aZM2cSFhaGj48PLVq0YNasWZQrV87cYQnFQHpyHFp1Gs5eDcwdSoHLMUktXLiQRo0ace/ePSZOnGjwvLOzs8nf7Pfff6d169YAtGrVCgeHjMHyevXqcfv2bSSS51Mmk5OT0Wg0JCUl6R7LvJszhljvYdlc3TJmsGnUKlLjH2Lt5E5q3APQatGo0l5YJ2X8jCVjW9qmalHGRxnVViOB+8+eGR1DfkVGRuLk5ETlypWpXLmySFBCjhIfX+XJ1Z8BCS4+zYm7exap3Bp7t2q4Vn7d3OEVqByTlEKhwM7OjgsXLhTYNztz5gxdunQBYPLkyUyePJnKlStz5coVhgwZgpOTEw8fPsTBwQGlUkmFChVISEggMTGR2NhYXFyMr031qq33OHnyJA4ODvj6+hIcHIxEImHSpEkWW1T0WVzGlPJyNbvxKOQbNOlJyG2ccKvZk7i7pyldpX2W1sav9zC2pUSl5uJ77xvVtsFXm43+POW23iMvW7duRalUcubMGb777jsWLlxIhQoVaN68OT179sz3cYWSJ/rGUSq2GI9WnUrEybX4tA1CIrXi3p/rDJJUulrDkfBYZBIJzb2d+PLvRySlaxhSpxzlHRXmeQMmyDFJ2dnZ8dZbb/HrrwW3+dz9+/fx8MgYEJ84cSJz585FIpHQtWtX3NzcmDx5MhMmTCA9PV1XGHPcuHEMHToUjUYjBo9zsHDhQpKSknj69CmxsbEMHjwYe3t7Zs+ezcaNG/M+gBnZlPLCu+V41OkpaFSpyKxscPFtZe6wzMbe3p6WLVtSqlQpPDw8OHr0KLt27RJJStCj1aqRSKVo1ZmXZVJAC1qNQdulJ+9T0dmaJJWGvZef0LdWWUrbWrH2r0iCO/gUadz5kWOSylyTsXz5ci5dukRqaqruuYYNG+brm23e/Hy2R9WqVdmzZ4/e8wEBAezdu1fvsZYtW9KyZct8fb9XRWhoKDt37iQ9PZ133nmHt99+G4AtW7aYNzAjpMTeJ+rfH9BqVEhl1v+VRZLhVqMLti7e5g6vSK1YsYILFy6gVCqpX78+TZo0Yf369fnqWhdKNhefFtw5tgKFYzkcPepw79RnSGRWOHrUMWgbl6picN2KAIx6kMgbfhk9UnsuG9fdbW55rpMaPnw4jo6OeidKfpOUUDjkcjmHDx/mjTfe0CX+f/75B7VabebI8hb174+41xuIlW0p3WPpyTE8OL8D75bjcn5hCVS5cmUCAwPFWJSQJyfP+jh51td9nRr/EKncWq/AbCZrmZSdIVEkpatJU2s4fjsOWyspcqnx3ejmlGeSkslkrFu3rihiEfJp+fLlbN68mTfeeINSpUoB8NNPP/HRRx+ZNS5jaDVq5Db6dwpyG2e9STSvim7dugHwyy+/8N1335GamopWq0UikbBt2zbzBidYlEch+3J87sXND2e08uK3W7EEuNnRzrcUuy49wV4hZWKz7KubWJo8k1SLFi3Yu3cvvr6+usfEnZRlcXNzY9Ys/WnXc+fONVM0pnH2qs/dPz7DrnQlpHIbNKoUkqPv4FQCp9Ia69NPP2XVqlXZLgERBACHcgE8Dfs/3Gp2z7OtvUJG12qldV93qeqKRCKhrL3hpIn4a09JvB2DOlWNzEaOvZczzgHmnXyVZ5I6f/48MpmMixcv6h4TSUooKKUqNcPBvWbGzrzpKUitbHDxfR25TfEsElsQ/P398ff3N2nJhfBqcShfg5TY+2jSk3Eon/u+a+cjE1h/9iHWciktvZ0Ie5KMlUxCyMNEBtV53rV8/+droAXH11yRKeSo01Qk3Igm/vpTvLoZzm49Hx/HzeQkUrUaHGRyAuzsqWbvYNDuZeWZpFJTU/n6668L/BsLBefNN9/k6dOnuq4+QNdNdOTIEfMFZqTUuAfIFPbYulTiydWfSHx0hTL+nXJNVE+T0klRafB0si7CSItGw4YNad++PZ6enqK7T8hRmaodjWq3MySKz9+uTHRKOpP+F86u3lWRSiRM/l84g+o8b5fyWEnl4fX0XutctSw3Nhsuq9j1+CF2UinV7R24pExABoQkJhCWpKR72YIdU80zSXl5ebF9+3b8/f114wTiTsqy7N69mxEjRvDVV1/pJariIOrKD2jUaahTE1CnJVHKpzlSuTWPL31DhUbDdO1O34vni78fIQG6Vi3N/92MwVYupaGnI31qWOZasPzav38/69atw7EAinMKgkYLUil4OFozvF55pBIJao0W9QvbekhkEmKvRuHo54rMWo46VUXCrWikCpnBMe+mJBPknTEEVNPBkeV3bzO1og+LI8LJuwPSNHkmqfT0dK5evcrVq1d1j4kkZVlcXV2ZOXMmN2/epEGD4jWWkxr/AK9m76PVqLlzfBVOFTIKF8eE/6HXbs/lJ6x9y48UlYZxP9/iqx5VsJZJmPx/4SUuSVWoUAFfX18UCstfaClYvl7VyzD+4C3WvVOZNypnTD+fc+QO7f1K6bWr2Ks6UcfvEHU8AnWqCpmNHDsvZyr2DDA4pha4mBhPdTsHLikTsZFKiU5PIz2bdVovK88kFRxseTXOBEPGJielUomVlRVWVlZ8//33SCQSunTpYr7xD4mMhIdXcHSvQcXmYwFIjokArf70eY1Wi0wq0VWUkEoyThRNydvjjadPn9KhQwfdNh2iu094GS0rOdPc2wlplhmzc16viK2V/h2SlYOCCp2r6L5W3ovD3iv7NXrD3T355skjvo16jJeNDYHlPbiSmMhAt4LfvSDPJNW2bVskEgkajYZnz55RpkwZjh49WuCBCAVvy5YtvPvuu7qvDxw4wJdffolWq9UlNXt7e+bPn59jRfvExESUSiUODg7Y2xd81XD3uv2IvvU7ju41kCkyignH3/8Ht5o99Np1qVqa976/gbeLNa19nJn4Szg2cimtK+W90DXxdgxIwKGS8WW1zGnFihXmDkEoYaQvLOmwtZLx3dWndA8oo3ss+uJDvTZRJyNwa5mxoN61jrvec24KBWMrVNR7rEWpwjm/8kxSWRNSdHQ048ePL5RAhILn5+en9/XevXv56aefiI2NpVu3bpw4cQKAQYMGGbz28uXLLFq0iLS0NOzs7FAqlchkMmbNmpXtXmL5TWZyGyfKBrxNavxDNKpUpFY2uNXooreZG0B7Pxfa+z0/CW7HpGBnJaWcQzbTaG88I/LgdaRWMkpVL4vyfjxSuZTE8BjKt/U1aG8pli5dyuDBg7PdnffevXvs2LGDGTNm5HqM4OBgmjRpQoMGDZgwYQJKpZJ27doxcuTIwgpbKKYqOutPOoq9HIU6OR3H1zKmq2vTNaTFpJgjND0m7cxrZWWFTGY4iCaYV3Jysm7Podu3b3P9+nWqVatmUE5Kq9USHR1NmTJlWLx4MZCRXNLS0gyOuXjxYlavXo27+/MrqAcPHjB+/Hi+/fZb3WOmJrMXJT4O5dm1/8PKrjRSuTUaVSppyieU8e+IQ/nqunaPEtLYdP4h9+PTeJyYRtUydpRzsGJovfK42Op/jB8fu02VMY1QJaZyc/PfBExtgUQq4cYXf1O+rRE/UDMZNmwYq1ev5u7du1SqVInSpUuTmJhIaGgoPj4+uV4gajQaZsyYwblz52jSpAm7d++mR48evPXWW4wYMUJXH1N49aSoNNjIMy767sencicmBV9XG+pX0J+Y4xtYm8e/30adpsa9nS+Jt2Mo3yb72n4zbl0nXq3CPpt8sMyvYHdPN6m7Lzo6Gmtra91jxWF686tg1KhRbNu2jQMHDrB7924aN27Mtm3b6Natm97Ox1OmTOH9999n7969tGjRAoAxY8Zke5Wdnp6u24AyU7ly5QzGroxNZjmJvnEEr2bvI5Xb6B5Tp6dw//QmvST12ZkHjGnkjoeTNdeeJnHyTjxtfZ1Z/ed9Pm5XSe+YGdO2wdrVDvcOlZFIJWjVGoMBrKI80YxRtmxZFi9eTEJCAiEhIcTGxuLq6sqkSZPyvDvVaDR07twZT8+MLVBCQkLo1q0bEomERo0aERISwhtvvFEUb0OwMB8djWBJBx8O34zh4PVoapWz54fQZ7TzK0XH1/TLKJV73YeEW9Hc2XsFTXrOZdVmevuy6v4dJntVwkFm0r2OyUzq7hMs2/79+9m2bRu2traoVCoGDx6sl6QaNWrEN998o/eanAbku3fvTq9evWjYsCEODg4kJiby999/06OH/liRscksJ1qtFiT6SUIikRqURUpMU+Px35qo10rb8sX5R4xoUJ6EVMMTya25Nzc2nqfKmEa41s1InuHbQ3Cpox9nUZ5opnB0dNRdRBhLLpfTqlUrQkJCgIw75MwNQ21tbVEqlQUep1C8HLoZw5IOPtjIpag1WqYfum2QpAAc/VyxKWtP7NWcC9A6yuX0d3PnQWoqVezMlKQy+8ezXiFnMrZ/XCgaMTEx3Lp1izJlyqBSZWwfrVarDbrxjhw5wv3792nRogWzZ88mKioKV1dX5syZQ61atfTaDhw4kDfffJNLly7pxpqGDx9usD9V9+7d6d27Nw0aNMg1meXEtfLr3D25FoWDm64sUkZ335t67V4rbcvC3+9Sz8OBvx8kUsfdgeO34yhlY/gRLlXDDeeAskiyFNCs1L8mMmv9tkV5ohU1e3t7kpKScHBwICkpSbdFTl7EZqGWL3PD0Lxl9BzEpai4G5uCi60c9X+9CWqtlnS19oWWz1k5WVO2Scbs0id/3qNsMy+DdlXscr+7V6lUhBfA5ynHM/Nl+seFotW9e3fWr19PeHg4O3bsIDAwkG7duhn8jtavX8/mzZuZPHkys2bNokaNGkRERDB58mSDrrnY2FjWrVvHhQsXSElJwd3dnbp16zJy5Ejd+BfoJ7PExMQck1lOHN1r4lAugLTEqIyJE3IbFA5lkUj1767GNvbgzP147sWm8tZrLtSv4Mi9uFSaVvQyOKZGpSHmn4dIbeTYV3Tm7v6rSCRQ4W1/bMrqn1h5nWjmcOrUKZo3b/5Sx6hRowZnz56lc+fOnDt3ju7djVti+aptFlocZW4YmreMi7T2fi7sufyEe3Gp/Bj2jK7VSjP2p5sEZimJlFs5Z+uydka1AzgU/ZQOrhkzBuVyeYFsFppjknqZ/vHstGvXTnc1N27cODZu3Kg38+jhw4dMmTIFlUrFgAED6Nq1K1evXmX+/PlotVomTpxI06ZNTf6+r4Jhw4YZPPbDDz8Y/J5kMhnOzs7Y2dlRpUrGeojMtTgvmjVrFoGBgUyfPp1jx45x9+5dKleuzIwZM1izZo2uXb9+/Zg/fz5t2+ZvRoIp1ZwbezrROMtFpJezNZceJVKrvH69sHvfhWJT1o60Bwk8OhpOxe4BSK1lRB68jt+7zydzqLQa/oiNxVYm5TVbOzY/uI9EIiGwnAfu1uYrt/T555+/dJIaOHAgU6ZMYcuWLbRr105s//EK61m9jMFj696pbLBO6kWZ66ScXiuda7usPBQFf97k2ceRn/7xFz1+/Jh69eqxfPlyADZt2mQw82j9+vVMnTqVgIAABg8eTKdOnVizZg1r167F3t6eUaNGiSSVg7Vr1/Lhhx9y9+5dgoKCiIqKwt3dnQULFlCpUiVdu65du/Luu+9SpUoVAgMDadiwIWfPnqVDhw4Gx3z27BlNmjQBoGPHjgwaNIj33nuPTZs26bVLTExk4cKF1KhRg5EjR+LqatjHnRtTqjlnJ+xpskGSSk9Ixbt3dbRaLTc2nMO+4n9rqV7o0/jyYSTuCmvupKTz/ZMohnt4YiORsuPxA6ZWNN+OpSqVir59++Lj46MbmzN2Uf24cc/34Mq6yajw6lp16n6Oz01q/vyqT2+dlBai/sh5ndSLbiUn4WdrRw2Hgi/lVSQd8deuXePWrVsMHDiQgIAAHjx4YDDzKCwsjNq1ayORSKhSpQo3b94kJiZGNyhva2tLXFyc2KU0G5m3ygsXLmTatGnUqVOH69evM3/+fL3iwAMGDKBZs2acPn2a0qVL4+zszMcff0zVqlUNjlm+fHmWLFlC8+bNOX78OFWrVuXPP//EyspKr52rqytbt27l+++/Z8SIEXh6etK8eXPd//NiSjXn7GRXEkmmkPHk1F3KNPOiyvuNAIi9EpWxw3YWsenpjPLwQqvVMv/OLSrb2hkcyxymTJli7hCEEqSJlxNb/nnMB41zH5c0ZZ3UqbgYva9/efaUt0pn3LE1dy7YRb1FkqRcXFz44IMPaNu2LYsWLeK3335j6dKlwPOZRxqNRnfVaGtrS1JSUsbMr/9kPmZsknqVBoCTkpIIDQ0lJiYGa2tr3XuPi4vL9udQu3Zt3b+1Wi379u2jZs2aem2GDRvGoUOH2L9/P5UqVaJ9+/bcuHGDsWPH6h1TqVQSFhZG1apVWbRoEXfu3OHy5cucPn0617uqrIO/eVdz1nL6Xjybzz9CJpUwqLYbrf6rNBF06DZLOvhkaQkV+9Qg+vwDvRmCaTHJVOwRoNfORirj1+indHApzUc+lQE4Fx/3Yi4rsAFgYwUEBLBp0yaePHlC69ats72IEF5eTusLK1asmMcri5dmFZ24/iyZxDQ1zSrmvEeZKeukzsbHoVSrqeXgiBZI12p4mp5eKPHnmaROnjzJt99+i1qtRqvV8uzZM90W5caqUqWK7kRr0aIF9+7dM5h5lHXKcuZzWf/IJCcn4+Bg/F4l+RkA/v333zl9+rRuNlu9evWKxdoStVrN/PnzUSqVhIaG8vbbb7Ny5Up8fHyM+jmcPHky23YvzvirUaMGt27d0qtk8c477+i9tlq1anTq1CnP72n84C+AJKPAbGc/tFotS0/eJzldzZuvufJCIWckZNxJZc5GyuTW0ht1crpeu9EVvDgeG633OXuSnsZwD/3ZUwU1AGysoKAg2rdvz/nz53F3d2fWrFns3LnzpY8r6MtpfWHXrl3p06dP3gcoRt6ta9yYpLHrpCZ6VeKnp1GkaDT0KFuOa0lKupYpnMXieS5mWb58OYGBgdja2vL666/rFguaYseOHezduxeACxcuUKtWLc6ePYtWq+XcuXPUrFmT1157jX/++Yf09HSuXbuGr68vTk5OPHz4kISEBJRKZaFuXTB//nx+//13GjZsSOfOnWnQoAF//PGHwY63lui7775j27ZtLF26VHdH5Ofnx5IlS4x6/XvvvWf098r8PWbKWhswq1u3bhl9TGPIJBIcFDIcreXMeb0iv92K5fS9eEzZZf7+z9f0vraWSnUzkTK9VbosCol5NxuMj4+nW7duyOVyvbteoXBkri+cMmWKruv6Vebo54rn2/55jkO9U8aN6vYOrIu8S6qm4KufZ8rzTqps2bI0aNCAb7/9lt69e+frF9i3b18mTpzIr7/+io+PDxMnTmTatGl6M4/GjBlDUFAQSqWSgQMHolAomDx5MhMmTCA9PZ0JEybk4+0Z78aNG+zYsUPvsXbt2tGvX79C/b4FJT09HX9/f111c4VCYVDC6t69eyxfvly37YpUKsXPz4/p06frTbDIzcyZM41qt3fvXqPbGqNKGVuCT9xjfBMP7BUyZr9ekVm/3eGJ0vguBu/exo15bXv0gNEVsp/1WBSsrKw4f/48Wq2WsLAwbGxs8n6RYDJj1xe+Ku5+Z9ilnflYxe7Z9yQE2DvgrrDmQmJ8ocWVZ5Kys7PjxIkTqNVqfv75Z6Kjo03+Jg4ODgYzjV78ukKFCmzfvl3vsYCAAIMr98Iil8s5dOgQzZo1w8HBAaVSyR9//FEolb8LmrHVzWfOnMnUqVP1uvEuXrxIUFCQQRdubGwsGzdu5PTp07ru17p16zJmzBijZvAVZIICGNXQncuPlShkGbdOzjZyVnXy5X/XYwzaalQaov9+QOLtGN2+OPZezpRuWAFpHtNuAbMmKIAFCxawbNkyoqOj+fzzz5k3b55Z4ympjF1f+KpwrlqGh4dv4flO3iXBvnpoOGMw87Fh7qb3tuUmzyS1aNEi7t69y6RJk9iyZQtz584t0AAsxcqVK1m/fj0bNmzQLUqtW7euboKHJTO2unlqaqrBOFOdOnWyPea0adPo1q0bY8eOxd7eHqVSyfHjx3Wfg0wvm8xM4WIjJzZFhbONnH2XnyCRSOgZYLgG5N6Bq9i6O1LudR+k1jI0qWoSbjwj4tt/8en//P0nqFQcinmGDGhZyoWtjx6QotEwsJw73ja2BsctKgcPHuTDDz/Ex8d80+BfBcauL3xVOFcrS1JkPOoUFc7Vcl+MX8fBif1PHjO4fMHvH/WiPJOUvb09//77L48ePaJNmza6RaAlTenSpZk+fTpWVlbEx8dz584dKlasWCy2Yze2unnTpk0ZPXo0zZs315Uw+vPPP3XrobJKSEjgrbfe0n3t4OBA586dDbpEjU1mL2vP5SguPlSSlK7BxVZOQFk7bK2krP7zPjNa68/GSk9Iw7uPt95jtuUduPGF/qSGzQ/v08DRiRSNhoUR4Yz28MJZLmfLw0jd1tjm4OHhwbp163j48CH169enXbt2BhcXwsvLvMA6c+aMbrJUYV1gFRfu7f3ybgTUc3TidkoySrWaeo45zxgsCHkmqVmzZuHl5cWff/5JnTp1mDp1Kl9++WWhBmUOn3zyCTKZjCpVqrBmzRqqVq3K9evXDYq0WqLsqpuPHTvWoLr5xIkT+ffff7lw4QKPHz/GwcGBsWPHUqOG4ViNj48PCxYs0CU0pVLJqVOnDK7ujU1mL+vc/URWdvIlJV3D+z/dYH7bjCT0513DvnCZnZyoU3dxrOyKzFqGOlVNws1orF7YeypVo6FVqYw/RsdjY/C3kBJJnTt3pnXr1pw+fZpNmzaxZ88ezpw5Y+6wShxTLrBM2dHa4na/LiQ9yxZNFZM8k1RkZCRLlizhzJkztG7dmg0bNhRFXEXujz/+YN++fQwaNIg9e/bg6OhIWloa/fr1s/gk5e/vT+PGjfniiy9o3bo1H374IVKp1OBq8NChQ3To0AEfHx82btzIP//8Q3R0NL6+vrqK2ZkWLVrE4cOHOXv2rK77s0GDBgbVKYxNZgXh5rNkKpe2ZV6bjAQVlZhGmtpwVpF3r+o8OxvJ42O3M8akrOXYeTnjlWWdFEApuZyvHt5HpdUil0g4+OwJtlJptlt3FKWePXui0Wjo1KkTc+bMyfYiQnh5xl5gmbKjtSltIyIikEqleHl58csvv5CYmEi3bt1QKAw38nyVGbWY9969e0BGqRy5vGRVi84klUp58uQJlSpV0k13j4uLM9gy4ujRoyxZsgS5XM4HH3yg+5APHjzYYNuLzHJFERERzJgxI8dyRS9r6tSpvPXWWzx8+JDhw4ezZ88ebGxsmDBhgt4Jt2PHDjp06MCiRYvw8fFh2rRpnDlzhjlz5rBy5Uq9Y65evZrRo0dnWzIpK2OT2csa39SDrf88Zs7rFankkjHbbcO5hwyrV96grdRKhn2lUiCVoElTI7e3wsHXBZlCP/m85+HFpcQE3BQKbKVSDkU/Qy2TMbyAB35NNXz4cP78809Onz7N48ePiYqKol27dmaNqSQy9gLLlB2tjW376aefcvr0adLS0vDw8MDe3p6yZcsyY8YMg3PxVWdUd9/UqVMJDw9n7NixxWLdUH5MnTqVUaNG4erqyttvv42vry+xsbHMmTNHr92GDRv49ttv0Wg0TJ48GaVSSe/evfWqY2TKXNi5aNGiXMsVvazMKzDI2Ck3cy3biwk20927d1m0aBGQsZ7q119/NWjz/fff8+eff/LBBx/w+uuv5/i9jU1mL8u7lA1z2+iPM734dabHJ+6Q+iwJO09nku7EYO1qR8KtGBx8SlGm0fMEpNFqcbe2przCmn+ViTjL5bhbW2Nn5jupt956i+bNm3Pq1Cl27tzJ6dOnRZIqBMZeYJmyo7WxbU+dOsWePXtIT0+nc+fOHDp0CIDAwMDCeKvFWp5J6sSJEyZXmCiO6tevz/79+7l9+zYxMTGUKlUKLy8vg1tvmUyGk1PGQOHnn3/O8OHDKV26dI4JASAtLU03i65KlSqkv1A+JLd9uYwpLOrl5cXChQuZOXMm69atA2DLli0GJaRu3LjB8uXLUalUnD17lkaNGnHkyJFsE6y3tzcrV65k9erVrFu3ji5duuhq8mWt32dsMitKCTeeUXl4fQBK1/fg9o4QfAfX4cbmv/WS1MYH96nv6MTpuFjupaZQ096Rv+PjCUlMYEj5CuYKn379+qFQKGjTpg2LFi0q0Ltu4TmJREKHDh0MktKLVVVM2dHa2LZarZbz588TExNDXFwc9+/fx97e/pVdo5WbPJPUX3/9xaBBgwzGLEoiiUSCr6/+rK4tW7boVVWoWbMmEydOZMGCBTg4OPDpp58yfPhwHjx4YHC8hIQE+vXrR3x8PAcOHNCVK3px24R27dqxcuVKgz5rYwUHB3P06FG9gVlXV1eWLVum1+6XX34hNDQUV1dXlEolSqWSX375JdvKFBKJBDc3N4KDg4mKiuL//u//WLFiBXfv3uWnn37StTM2mb2s8QdvkZCq0ntMS0Z5o6976K/r0Kg0JD9KxLa8A8mPEtBqtajT1AZlXhLVKpo6l2LZ3dtM+6/q+esuriy6U7DVMky1fv16Hj9+THh4uNhR1wxeXIj+4o7WMTExOe5obWzbefPmsW7dOqpWrcqcOXPo378/zs7OJban6mXkmaTu3r1Lo0aNcHNzQyKR8PjxY65cuVIUsVmErFdUkLFI9dy5c7o7LFdXV/bs2cO+fYb7In333XekpaVx7do1XdUAPz8/gyrX7du35/Lly8THx9O+fXuTY5RKpQav69Kli0E7FxcXPDw88Pb2pkKFjDuFlStXEhYWZrBza9Ydmd3c3Bg8eDCDBw82OKaxyexlzWjlxdKT91jyhg82VrnPkvJ8259734eiTk7HytEaz65ViT4XaVAs01oq5V9lIgF29vyTEE8Newf+TUrEysyzsPbv38/x48epXbs2u3fvpkmTJowdO9asMZVEL44ZP378GA8PDxYsWKDXbs6cOSxYsIBLly4xffp0HB0dSU5OZtGiRQZLA4xtGxAQwGeffab7ul27dkilUqzNuI+ZpcozSR05coTExES+//579u7di79/3quRS4KQkBBq165Ny5Yt9R6Pjo7m5MmTnDlzhsDAQJydnbG2tiYlxbCk/apVq3j//ff1KoznVLhy4sSJBfsGsvH5559z6tQp1Go11apVY968eUgkEhYvXmxwtWfsImZjk9nLcndU0Lt6Ga4+SaKeR+6Fhu0qOFFldEPd1yplGmWaeRl0yQ5zr8DeqEfcS03h0dMn2MtkVLa1410zdvUBHD58mN27dyOVStFoNPTt21ckqUJg7JhxREQEkHFBt2nTJry8vHjy5AkTJ040mAlobNvIyEhWrFiBo6MjnTp1Yt68eUilUqZMmZKvC9WSLNckdfnyZXbv3s3Zs2cB2Lhxo8GdRUmRtSahVqtl8+bNusKrmZMSIGNtRYcOHVCpVAwaNIh169bh5eXFyZMnDfqdM8drxo4dS5s2bYyOZc+ePUbVDExQppGmyrlScVYKuYwTJ07oykytXbuWWbNmsXjx4mzHpIy1dOlS1Go1N2/e1M2K9PX1Nagb+LLiUlTciE5BLgFPJwWfnH5AcrqG0Y3cea20fnWIZ+cjUSWm4eDjwr0fwpDZyFGnqPB4szJO/s8rVDjLrXjP43kJpKdpaZSxgOm/arUatVqNVCpFrVbnOt4pvLy8xow1/xVPVSgUup2sy5Ytq6v3l5+2QUFBfPjhh0RGRvLhhx9y6NAhrK2tGTZsmEhSL8gxSfXu3ZuKFSvSpUsXPv74Y0aPHl1iExRklKKJi4ujVatWAKSkpHD/vmF9qrS0NN3dUK1atRg3bhxfffVVnpMP1q9fn+N4zYsTJ06ePElISAiQ+8SJNJWadz8+ZNT72zK3A1qtFpVKhVwu58MPP+Sjjz5i5cqV2f4RfPPNN3n69KlexQ2tVotEIuHIkSO6x44fP86qVauoWLEidnZ2KJVK7ty5w4QJEwr0ZFv+x31aejuRlK5h4v/CCWrlhYutnLV/RrK8o/444rO/H1B5eH3Ct13Ed0gdFM42qJLSCd92US9JXUvSH+/Z/fgh/ctl3Bmac2Fv//796d69O/7+/ty4cYOhQ4fm6zjt2rXTdePOnDkzX9vXlGTGjhk7ODjQrVs3YmNj2bZtG3369GHKlCnZVgExtm16ejoNGjSgQYMGhISE6M4zcUFiKMckVb9+fc6ePcvJkyfznL1WEmzevJl169aRlJTEhAkTOHPmDB988IFBO4lEwsmTJ2nevDk1atRg0qRJjBgxIttZOaZMPjh+/Djjx49HKpUSGhpK9+752049NwMHDuSdd95h165duLi4MHfuXGbNmpXtHki7d+9mxIgRfPnll7i45LzT5rp169i5c6feXl+JiYkMGTKkQJNUikrDm69lLE7+340Yapb7L4lk87GUSCRoNVqsnKyRWWd8xKVyw3Gm3Y8fIgG8bGzQAnEqFX/ExSDBvEmqZ8+etG3blnv37uHp6ZmvEj2PHz+mXr16LF++vBAiLBmMHTPOLGAQHR1Neno6crmc/v37Z7vztLFt69evz+jRo1m3bp2uHuqSJUtEvcZs5JikgoKCUKvV/P7776xfv56QkBDWr1/PO++8k689pYqDMWPG8NdffzF+/HiSk7PflC84OJhPP/2UmjVrUqpUKVq1aoWVlVW2fwyMHa8ZPXo0derUYceOHcyePRsnJycaNWpUcG/sP5UqVcLGxobx48czceJE6tWrR3BwMImJiQZtXV1dmTlzJrdu3dKtnM+OWq02WOAtlUoLvARMaVs5q07dR6XRYiWVsO/yE+yspDgqDLsV3Vp6c+vLv7Fxc+D6+rPYVXAi+VEibq3011XN9PZlb9QjPKytaedSmmV3b5t1Ie+hQ4dYvnw5NjY2BAcHv1S9vmvXrnHr1i0GDhxIQEAAM2bMKHFleQqCQqEwaswY0LtYaN68OWfOnKFx48b5ajt16lRCQ0P1fidt2rShYcOGCPpyHZOSyWS0a9eOdu3aER0dzY8//sjYsWP54YcfTPomiYmJuoWvrq6uLFq0iE6dOumuGlatWoVKpWLKlCmoVCoGDBhA165duXr1KvPnz0er1TJx4kSaNm2a/3dqpKZNm+Lr68vhw4ezfd7Dw8OgC65p06YcOHDAoK0pFdSbNGmCn58f8+fPJybGcPuJghAcHMyaNWtIT08nKCiIcePG0bp1a+Li4gzahoSEsGLFChQKBR9++CH162esO5o8ebLeivhRo0bRs2dPfH19dUVrb9++XeD7f01r6cW5yATcHRXYWUn5PvQZKq2Myc0Nk0rUHxF4dKqCJl2dUb/P1goPD0eD2n0KqZTA8h6cjI1h84P7pGsLb+M2Y3z11Vfs37+fuLg4Fi1a9FIlyFxcXPjggw9o27YtixYt4v/+7//0SgDlJDTUcE+h3KSmpupmpEVGRhIREYGPj4/eBZolKu3mgTbvPV+RoOFZlOHyEoDffvtNt2YyLzm1zfrzdnJy4ttvv9VLmtlxdTP2Qsr4sWZjW5oyeq1SqQg38fOUHaNrHLm6uvLuu+/muBNrbvbv388bb7xBr169WLt2LVu3bqVXr156f8jmzp3L1KlTCQgIYPDgwXTq1Ik1a9awdu1a7O3tGTVqVKEmqeymo/76669GlzAaPny40YV3c2pbtmxZ1q5dy7///mtq+EaRy+V4e2fcTWzevJlhw4blWOU9ODiYJUuWoFKp9BJaVFSUXjs3NzccHBw4e/Ys69atw8HBAR8fH2bMmFGg3X0yqYQmXs9P8pENcv4jqElV8+hoOHYejri19EZun/1kiEURtxhSrgItS7ngbWPDmXjDZF2UFAoFTk5OODk55Xgnb6wqVapQtWpVAFq0aMHFixeNep2p41aZ5cCybsF+9OhRi9+C/VlcslHjuVvmdsDDw4PNmzfj5OSkq40pk8n4+OOPDX5ecXFxRrfNzsmTJ/Ns9yzO2M+G8UM0xrY0ZdBHLpcb/XnKbsghU5Hc//fs2ZN33nkHyOgecnR05PTp0wwcOJCNGzcCEBYWRu3atVEoFFSpUoWbN28SExND+fLlcXR0xNbWNtsr/oLy4nTUI0eOMGfOHKMX2JpSGT63tjKZTNfNExQUZPQxjVGuXDlWrFhBUlISLi4ufPLJJ8yYMYM7d+4YtJXL5VSqVInKlSuzefNm1qxZQ0hIiMHYZHBwMMuWLWPnzp0EBwfz6NEjFAqFQTIrSnJ7K/yG1sWmnAPh20K4s+cyz85HknBTf8POFLWGXVEP2Rv1kFJyK3q7GdYBNJeXmXEJGXUaM2dyXrhwodCXjpi6BXtkZGSOz2XX/ZybxMREoqKiCq1aw9SpU6lcuTJqtZrhw4fzxRdfsHXrVlavXp3vtnFxcaxYsYJNmzZx7do1OnbsSOfOnXU9FsJzRVItNnNQ/Z9//uHs2bOMGzeOKVOmUK9ePcaPH8/FixfRaDS6P4C2trYkJSXpnaiZj71Y6icnpnZbJCUlERoaSkxMDNbW1rrXx8XF6R0rPj6eR48e4efnx5EjR7h9+zaenp506NDBoMLCX3/9RdOmTUlNTeWbb77hzp07+Pn50b17d6O2BO/atWuu78P42/6MW++hQ4dy4sQJrl+/ruuimT17Nt99953B97G1tWXGjBn06dNHN441adIkkpOT9dqmpaWRlJQEZJygH330EbGxsQbtXiZ2U7sttNqMyROudd1xretO8qNEEm/HEHftKY6VXXXtHOVypnpV4s/4WFbfu0MZhYLq9g6Utcr4f6aC6rbIS3h4uG6mZ9Z/g3HlsbLq27cvEydO5Ndff8XHx4c33nijQGPNZOwW7OfOndP7evHixbqqDi+OwzRv3pygoCD69++f6/e+fPkywcHBqFQqbt26Rc2aNbG2tmb69OkGlWNehim1MY1ta2xRaKGIkhRk3KksXryYdevW4ejoiI2NDVKplGbNmnHz5k29AcTMHV6z/mKTk5P1ZpDlxdRuC7Vazfz581EqlYSGhuqmo/r4+OgdK7PL87vvvsvYGbZnTy5evMjmzZv59NNP9Y4ZHBzMsGHDmDVrFpUqVWLYsGGcOXOGHTt26I3rXLhwgXr16qHVavn2228JCwsjICCA7t275zrYbfxtf8adUY0aNbLd9qFZs2YGj61fv56ff/6ZqlWrYmtrS7Vq1ahRowZffvml3s/Dx8eHgwcPMmbMGOzs7Ni0aROjRo0iMTEx19+BKbGb2m3hXFV/t17b8g7YlncwaAcZfzyaO7vQ3NmFeykphCUlcjEhXi9JFVS3RV5WrVql+/fLzu50cHBg8+bNL3UMYxi7BfuiRYuQSqW6O7onT57oxnFfTFIBAQFERkbSr18/Ro4cSdu2bbNNCMuXL2f9+vU4Oztz//59NmzYwLhx45g6dWqOZYvyw9jamKa0NbUo9KusSJLUnTt3dAPBbm5ufPzxx7Rs2ZI2bdpw4cIFhgwZwmuvvcY///xDjRo1uHbtGr6+vjg5OfHw4UNdGX1HR8dCi9HY6agqlYrXX3+dLVu26DZGa9myZa6Lb/OqOr5mzRq2bdvGsmXLSElJoU2bNpw5c4YFCxYwb968Anl/DtI0VAnGJQaJ3Aq5raPeImbIGDN7sQty6dKl/Pzzz7qTy8vLi61bt5p1Y8yyzSrm3Qio46D/efKyscHLiDvcwlIYMzoLm7FbsO/du5fg4GBee+01Bg4cSGBgYI53h1ZWVkyZMoWIiAi2bNnC6tWr8ff3x9PTU68yS2Jiou5vQtmyZQkPD6dcuXLZVn95GcbWxjSlrSmJ71VXJEnqiy++ICEhgcmTJwMZY1RfffUVX3zxBY0aNaJmzZq4uroSFBSEUqlk4MCBKBQKJk+ezIQJE0hPTy/w2WLZMWY6apkyZfjmm290iaply5b89ddf2U5AMKXqOMClS5fYuXMnAK1atSrQsv0SjYq7n71nVNuyk74iPTk2z3ZWUiscre2NSmaWqINrmbwbCSYZO3Ysn3/+ucHj1tbWfPTRR+zfv59p06YZVHXIKvP88Pb2Zt68eWg0Gq5fv24wdtq+fXv69+9Po0aNOHfuHN26dWPXrl1Urly5QN+TsbUxTWlrSuJ71RVJklq4cKHBYy/+YatQoQLbt2/XeywgIEA3+FvYXuwzzyprd8SiRYvYtGkTp0+fJiYmhl9++YW6devq9o7Jytiq43fv3mXXrl0oFAquXbuGv78/165dy/VELkzpGjWjf867GvPWjsGkJkbn2Q5AqrDCqhDvhAXzqF+/PjY2NtjY2KDVann69Kmuey5rZZJMPXv2pHr16rkWH36xTqFUKqVq1aq62YqZxowZQ7t27QgPD6dbt274+fnx7NmzPMeyLIEpie9VVzK32c2H7du3c+XKlWwX52VNUvb29kYXg3VxcaFZs2Z6Yz7Z7br52WefERoaio+PD6GhoXh4eDBv3rwC6+orLJJ0FeeHjzKqbYOvCn98RCh63377LcuWLeODDz6gevXqBAYGGlxsQsYeWfPnz8ff3z/bhJNVlSpVWL16NXK5nJ49ezJnzhyUSiVz5syhevXqBsfs1KmT7rHSpUsX6PszpZs8WS4nnbzX2tmlSZAYWXNTXNyJJKWzZs0ahgwZwogRI4q8RuGLExoeP35McHCwKJEiWDwfHx9Wr17NokWLqFq1ao4D/4mJiSxcuJAaNWowcuTIXEs9TZ06lY4dO6JUKunTpw+rV6+mTJkyzJ49W9cdbuox88uUbvJSEzYb1QOxrd0CcXFnApGk/iOVSnVriHKTtfBqZsHV7Aqvvtg2U3Ztjx49ytKlS5FIJAQGBvLtt99iZ2dH69atdZXYBcFS2djYsGDBAvbt26erAv4iV1dX3fqpESNG4OnpqSu2/GJdu+TkZHr37g1kTLjI7Ml4MQGackyh+BJJ6j/GbuGeWXj1q6++yrFag6ltN2zYwP79+1EqlfTo0YPffvsNGxsb+vfvL5KUUGz06dNHN9noxS3YMy/OunfvTvfu3QkLC+PMmTMcPXrUIKG4ubkxY8YM0tPTUSgUbNq0CXt7e4OZb6YcUyi+RJL6j7FbuGcWXr1582auhVdNaatSqZDJZLorRZlMlrHludq4fmtBsDQvbsHerl07vedzG5dasWIFx48fx9vbG3t7e7Zu3Up6errBhCNTjikUXyJJ/cfYLdwvXrxIZGQkTZs2ZfLkyYSGhlK5cmWmTJlCxYoV89U2s1bha6+9RufOnenbty/W1ta8/fbbhfZ+BaEwZU1QQI41P1+844KMxdNZE1DmcoZbt27prZU05ZhC8SVq92cxceLEPIuiLl68mFq1avHRRx/RsWNHvv/+ewYMGMD06dPz3TYiIoLff/+d2bNnc+XKFUJDQ4mKiqJ169YF+v4EoaDNmDEjx/+MYcoSE2PbFtWyFaFoiDspjN+GXSGXIZPJ8PHxQa1W6+qhNWnSRK+kTSZj275Y3LZOnTpcv36d+fPn8/XXX7/s2xOEQmNsN3lsbCwbN27k9OnTurJndevWZcyYMflua8oxheJLJCmM34Z9y9wONG7cmClTplC1alXGjRtHs2bN+Ouvv7LdoM6UtpBRrLVOnTpAxloRcy3mFQRjGdtNPm3aNLp168bYsWOxt7dHqVRy/PhxJk2apCsvZmpbU44pFF+iu89EEyZMoEePHmg0Gl1hyy5dujB79ux8t01ISKBfv35ERUVx4MAB0tLSCA4Oply5ckX1tgQh34zpJk9ISOCtt97SFY52cHCgc+fOpKam5rutKccUii9xJ5UPL1aReNm2xha3FQRLYko3uY+PDwsWLKB58+a6gtGnTp3KdsG6sW1NOaZQfIkkZSGMKW4rCJbElG7yRYsWcfjwYc6ePUtiYiIODg40aNCADh06GLQ3tq0pxxSKL5GkTCDqeAlC/kgkEjp06GBUAjG2rSnHFIovkaRMIOp4CULhSkhVkq7Je8KQuLh7dVh8kkpPT2fq1KlERUUREBCQ7QQFQRD0WdJ5Y0oPRLpMwuifZubZTlzcvTosPkkdOnSIgIAA1qxZw8yZM7l06VKOU7gFQchgSeeNqT0QgpCVxU9BDwkJ0VVBbtasGRcuXDBzRIJg+cR5I5QUEm1Oe5lbiJkzZzJkyBD8/f05cuQIYWFhBjt3viizgoMgmEP9+vXNHYI4b4RiJ6fzxuK7++zt7XV7PGWWPsmLJfyREARzEueNUFJYfHdfjRo1OHv2LACnT5+mdu3aZo5IECyfOG+EksLik1SnTp0IDQ2lb9++yGQyXW07QRByJs4boaSw+DEpQRAE4dVl8XdSgiAIwqtLJClBEATBYokkJQiCIFgskaQEQRAEiyWSVD6cPHmSI0eOEB0dTWBgIH379mXz5tzLuURGRjJs2DAgYwfeBQsWFEWowPN4AbRaLePGjeP69esAxMTEMHr0aPr168fixYtzPc6VK1eYPHmy7nWrV68u3MB5Hvvjx48JDAykT58+fP/99wCEhYUxYMAA+vbty4YNG3I8xtdff03fvn3p168fV65cKbLYBX3ivBHnTX6IJJUP33zzDW3btuXHH3+kU6dO7Nmzh8OHD6NUKrNtf/78eSZNmkRsbCyQsXeUh4dHka3wz4xXqVQyevRoLl26pHtu06ZN9O/fnz179uDu7p7jrqaHDx9m5syZui3tXVxcSEpKIjIyskhi3759O6NGjWLPnj3s3LkTrVbLp59+ytKlS9m7dy+nTp3i2bNnBq+PjY3l2LFj7N27l+XLl/PJJ58UWeyCPnHeiPMmP0SSMtHVq1fx8PBAIpFQvXp1UlJSUKlUAFhZWWX7GqlUyqZNm/Qea9euHfv37y/SeFNTUxkzZgxNmzbVPX/lyhWuXr1KYGAgpUqVwtraOtvjODg4GFxFvf7667qrs8KOffLkyTRv3pzExEQgYy+hjz/+GC8vLwBUKhVyuWEBFUdHRz755BNdm8zfUWHHLugT581z4rwxjUhSJrp48SJ+fn4A2NjYsH37djp16kSVKlVQKBTZvqZevXo4OzvrPebt7c3Vq1eLNF5XV1eDygOPHz/G29ubL7/8kt27dxMTE5PtcZo2bWpwIvr5+RESElI4gaMfu0QiITw8nC5dulCzZk20Wi2lS5cGYMuWLQQEBBj8jAFkMhmlSpUiOTmZOXPmMHLkyCKJXdAnzpvnxHljGpGkTBQfH0+pUqUA2LhxI4sXL+bw4cMkJydz/vx5o48jkUiQSgv/x5813uw4OjrSrFkzFAoFNWrUICIiwuhju7q6Eh8fXwBRZu/F2P38/Pj9999JSkrizz//BGDbtm2cP3+eoKCgHI+jVCp5//336d+/v67yQmHHLugT581z4rwxjUhSJnJ2dtbdOtvZ2WFvb49EIqF06dI59q3nJKdujoKUNd7s1KpVi/Pnz6PVarl27Rqenp5GHzsxMTHXE/llZY196dKlhIWFARk/d6lUysGDBzl79ixr1qzJ9Wc5ceJE+vTpQ+fOnYssdkGfOG+eE+eNaUSSMlHdunUJDQ0FYOzYsSxfvpyBAweSmJhIy5YtOXDgACdOnMjzOHfu3MHf37+ww9WLNzujR49mx44d9OrVi3bt2lGmTBk2bdqU62syhYaGUq9evYIMV0/W2Hv16sWCBQsYNGgQUqmUpk2bsnbtWh48eMDQoUMJDAzk3r17BrH//fffnDt3jt27dxMYGMjcuXOLJHZBnzhvnhPnjYm0gsnGjBmj1Wg02T73zz//aE+cOJHnMTZt2qS9cOFCQYeWrdzizc7PP/+svXXrVp7tFixYoH3w4MHLhJan4hy7oE+cNxnEeWMacSeVD71799atn3iRh4cHLVu2zPX1aWlp3L9/n7p16xZGeAZyizc7DRo0wNfXN9c20dHRWFtb4+7u/rLh5ao4xy7oE+eNOG/yQ1RBFwRBECyWuJMSBEEQLJZIUoIgCILFEklKEARBsFiG9TCEYu3MmTNMmjQJX19fNBoNUqmUFStWsGTJElavXs2hQ4do0KABrq6uuR4nMDCQ1NRUvdXyCxcuxNvbu7DfgiAUOXHeWC6RpEqgli1bsmTJEgD27dvHli1bdPXDtm/fTkBAQJ4nG8CqVatMWqQoCMWZOG8sk+juK+FiY2MpX748bdu25cSJE4SGhjJ9+nTCw8Pp168fAwYMYOTIkTlWcX7RwoUL2bRpEykpKfTo0YNbt24RFBREUFAQgwcPZtCgQTx+/LiQ35UgFC5x3lgOcSdVAp08eZLAwECSk5O5d+8e33zzDVu3bqVVq1ZUq1aN4OBgjh8/TrNmzRg7dizHjh0jLi4ONzc3veNMmjRJ121hZ2fHxo0bmTJlCgMGDODKlSv0799fV8iySpUqLFmyhAMHDrBx40bdCnVBKC7EeWOZRJIqgbJ2W5w+fZpZs2YZtOnZsycbNmxg6NChlC9fPttSJ9l1W9jY2NCzZ08++eQTli9frnu8YcOGANSuXZuDBw8W5NsRhCIhzhvLJLr7Srjy5cvr9u2BjCrSWq2WI0eO0KJFC7Zt20blypX58ccfjTres2fP2LdvH8OGDdM72f79918gY5uAypUrF+ybEIQiJs4byyHupEqgzG4LmUxGWloa06ZN021fXbduXSZNmsSyZcuYMWMG1tbWKBQKFi5cyM2bN9m7d6/uCjJrtwXAmDFj2Lp1Kx9++CFt2rRhyJAh/PHHHwD89ttvHDx4EBsbG5YtW1b0b1oQXpI4byyTKIsk6KSmprJ+/XomTJhg0uuCgoLo3r07jRs3LpzABMGCifOmcInuPkFHpVIxbNgwc4chCMWKOG8Kl7iTEgRBECyWuJMSBEEQLJZIUoIgCILFEklKEARBsFgiSQmCIAgWSyQpQRAEwWKJJCUIgiBYLJGkBEEQBIslkpQgCIJgsUSSEgRBECyWSFKCIAiCxRJV0AWhBNq3bx/R0dE8ffqU2bNnmzscQcg3cSclCCVQ69atGT16NGq1Go1GY+5wBCHfRJIShBLIxcWFVatW0bt3b6RScZoLxZf49ApCCTR79mySkpL4/fffSUtLM3c4gpBvJXKrjr///tvcIQivsPr16xfJ9wkODqZJkya0aNGCqVOnEhUVRUBAQL7HoMR5I5hTTudNiZ04UVR/KAQhq6L4Q6/RaJgxYwbnzp2jSZMmHDp0iICAANasWcPMmTO5dOkStWrVytex7ezsCjhaQchbUlJSjs+V2CQlCCWVRqOhc+fOeHp6AhASEkKnTp0AaNasGRcuXMh3kqpWrVqBxSkIxsrt4k6MSQlCMSOXy2nVqpXu68TERN0dkK2tLUql0lyhCUKBE3dSglkkKNNIU6mNaquQy3C0VxRyRMWXvb29rrskKSkJBwcHM0ckFCZjz52Sct6IJCWYRZpKzbsfHzKq7Za5HQo5muKtRo0anD17lrp163L69Gl69+5t7pCEQmTsuVNSzhvR3SdYPAdpGqqEaKP+UycnmDvcItepUydCQ0Pp27cvMpmMOnXqmDskQSgw4k5KsHgSjYq7n71nVNuK4zcXcjSWY9y4cbp/r1mzxnyBCBYp4+Iu2ai2ErkVMlvHQo4of0SSEgThlbR3794SXZGjpFzclczfjiAIQh5WrVrFu+++S1hYmLlDEXJRbJLUl19+ydKlS80dhiAIJUSVKlWYOXMmwcHBfPDBBxw7doyUlBRzhyW8oFgkqRs3bnDokHEzwQRBEIwhkUioWrUqW7duZdSoUZw+fZoBAwbQoUPJmBVXUlj8mFR6ejqfffYZ77//PmfOnDF3OIIglBBWVla6f9esWZOaNWuaMRrzSpbLSE+OzbOdXZoEiZHrG6UKK6wcX34yhsUnqQ0bNhAYGIhabdwPRhAEwRizZs0ydwgWI12jZvTPef88trVbwPnho4w6ZoOvCmYyhsUnqWPHjnH27Fni4+OJjY2lcePGvP7663m+LjQ0tPCDE/LN1c2TtMQoFA5uRrQ2vlC/SqXihvjdC0bo0qUL7777LuPGjcPa2trc4ZhE+eQ69mWrmDuMImHxSerAgQMAnDlzht9//92oBAWiUKalexaXzJ3jq3HxbUnpKm8glVnl0lpi9HHlcrlZf/diu4vio27dulSoUIFevXrRu3dvunbtirOzs7nDMsqD89twKFeNstW7ILe2zPVNBaVYTJwQSiZbV2+s7Fy4+8enxNz+A3VazuX6BaGgSSQS+vfvz65du0hLS2PEiBH069ePyZMn5/q66OhozL0Nn00pT5w8GxB55kui/v2J1PhHZo2nMFn8nVSmxo0b07hxY3OHIRQoCaW8m+LoUYe4u2eIPPslSKRY2brgXm+AXssR310nJkWFk7VM95iWjHusr3v4F23YQomQmWgcHR0ZMWIEI0aMID4+noiICL12e/fu5dmzZzRu3JhZs2bh7OxMfHw806dPN7pnp+BJsHfzx67sayQ+vMKz64dIS4xCamVLxeZjzRRT4Sg2SUooiTL+SMisbHH1ex1Xv9dRpyeTrnxq0HJlJ1/mHLnDovaVcLQWH1vh5X300UcGjzk5ORnM8vvmm2/YtWsXw4cP5+uvv8bd3Z3Y2FiGDx9uxiSVce5IJFIcPWrh6JGxf5g6TX+blqjEtByP4OZQPCqki7NdMBu3Gt0NHpNZ2SIr5WXwuLONnPcauBMRm0qNcuJjK7w8Pz+/bB/fsmUL7777ru5rqVSKSqXCzc0Nx/+mVJt7ooVH/cEAqNNT0KrTkFrZIZXJkSns9dotPXmfR4lpeDlbk7WHUiKBJR18ijLkfBNn+yssMjKSChUqmO37WzuW0/s68txWKjQckmP7GuXsc3xOEArKi8lr5MiRDBw4kNdee42uXbtSq1YtQkNDGT16dJ7Hio6OxsXFBYnE+Mk/xkhPiubB+S1oNRrSEqOwKeWJRCqnbMDbejNmF79RiemHbjO5eQXKFtO9pUSSeoWcO3dO7+vFixczc+ZMABo2bFjk8dz8v7lIZHKkMgVarRZ1agLhR4KRSCT4tA3K9bUfH4tgbhvvIopUKKnu3LmDlZWV3sVa2bJl9dq88cYbNG3alH/++YcWLVpQqlQpZs+eTenSpQ2OZ+r4VWJiIklJSZQqVQqFwvgk8iT0IB4N3kWmsCM9KZpnN45Sxv8NHv6zB6+mz9cxWculTGxWgbgUNWVNuMZ7di6S0g1zvoC9k5LMjaQk0rQaHGQyqtnZ46YonLtLkaReIYsWLUIqleLvnzHR4MmTJ7op/uZIUhVbjONJ6EFKV2mPjbMn9/7agFfT7K9Oe+6+irVMirVcghaISVbx7oFrYuKEkG+ff/45p06dQqVSERAQwLx585BIJCxevJht27bp2kVERHDnzh0aNGjAxo0bCQsLw8/Pj/feew8XFxe9Yxo7fnX58mWCg4NRqVTcunWLmjVrYm1tzfTp0/H19c0zdo0qBamVDQAya0fSlVHIbZzRqtMN2nqXstH9O02tQaMFG7n+xO6F8xZy937m+kItCTejUd6PB6Bid/0lHQefPeFRaiq+tnaEJSVRTmHNv8pEqtnZ08bFMHG/LDEF/RWyd+9eatWqRY0aNQgODsbHx4fg4GCCg4PNEo/CoSzu9QYSF3Ga2Dt/ktt6qLWd/fAvY8us1hXZ0sOfqmXs2NLDXyQoId9OnDjBrl272LdvHy4uLroKFC9OL58xYwbOzs589NFHuLq6EhQUROXKlZk0aZLBMY0dv1q+fDnr169n3759/PDDD3h6evLxxx9nO5kjOw7la3Dv1DqehP6P+6c34VihHrF3/kLxQhd62JMkJv8vnGm/hnP6Xjxjf7rJuJ9vcuRWjF47z4qepD5LwqVWOVzruCO3V+BapzyudcobfO/LiQkM9/CkjYsrYytU5H5qCmMqVOSv+DijYjeVSFIlzNmzZw269TJZW1vz0UcfYWNjw7Rp00hPN7zqKmpSmRXlavUCiQy0mhzbeTpZE9TKi4PXo/k57BkF3MVvNomJiZw6dYpffvmF06dPiyrcRUir1aJSqQD48MMPUSgUrFy50mD8SCKRUKdOHaKjo3n33Xfx9fWlZ8+eJCcbbiiYOX4lk8no2rUrEydOpHv37gQGBuq1S0xM1CWxsmXLEh4eTrly5Yz+/Zd+rR3lavXExrkC5Wv3ppR3Exzca2acS1lsOv+QyS0q8F4Dd1adimR5R18+f6cyP1+L1mv37vB3cW/ry7OzkShcbZHbynHwccHBR/9OESBdq+Xef3HeTU1Bq9WSqtGQpsn5/H0ZoruvmDtx4gQLFy7ExsaGjh07EhISgrW1NadPn9bbuTWrnj17Ur16dX766acijjZnpbwbU8o7Yx1cTuWSrOVSPmxagf9dj0adw2JKSy6UmdWzZ89YvXo14eHheHt74+bmRnx8PJ9++il+fn6MGzfOYGxEKFgDBw7knXfeYefOnbi6ujJ37lxmzZplUDXEz8+P1atX06BBAxYtWkTLli3566+/sp10ZOz4Vfv27enfvz+NGjXi3LlzdOvWjV27dlG5cmWj47d2csfayV33tdzagfjIizhVqKN7TKUBD0drktPV2MilOFvLkEgk2V7kOfi6YF3Wjsifr6NKyvkCdlA5d75+FEmiWoWL3Ioh5T34PTaaLmWMKXFmOpGkirnPP/+c77//nqdPn9K3b19OnTqFVCqlf//+BkmqX79+zJ8/H39/f6pWrUrVqlXNFHXuYiNO41a9S47Pd6riSqcqrgDci0vFy/l5d4olF8rM6osvvmD06NF4enoaPBcREcHmzZt1k1qEwtG1a1c6deqkm7AglUoJDg7Wm34OMG/ePA4cOMBff/1FTEwMT548oW7dukyYMMHgmDt37mTgwIEEBASwePFirl27RvXq1Zk2bZpeohozZgzt2rXj9u3bdO/eHV9fX549e0b//v1f6j1pNSq9r1t4OzHiu+uUsbeiWllbPjp6F2u5hOpu2c+isHK0xrOLP2kxKahTVciyWZPoY2vH3Ep+JKvVpGg02MqkvOla5qXizk2RJ6nw8HDOnz9PbGwspUuXplGjRnh5Ga6LEYyjVquRyWRUrFiRqVOn6vrEs6san5iYyMKFC6lRowYjR47E1dXVDBHnLHMKem4J6kW/XI9mVEP3vBtamOnTp+f4nLe3t0hQRWDGjBk5Ppd1nFYmk9G7d2969+6t1yY+Pt5gvOnXX39l4MCBfPzxx3Ts2JHFixdz+vRpZs2axYYNG3TtYmNj2bt3LxcuXCAlJQV3d3fq1q3LyJEjsbW1zfd7cvZqoPd1nxpl6VzFVTdR4lxkAvYKGTVfWM4xdcJUaK8g/vozHvx6E9ty9qQ8ScK9vS9O/voJ6E5KMjsfP0Cl0WIjlZKs0SCTSBhQzh0/W7t8x56TIktSN27cYPXq1SgUCipXroybmxsxMTGsWLEClUrF+PHjdbPOBOMNHz6cnj178uOPP9KjRw8go1+8W7duBm1dXV3ZunUr33//PSNGjMDT05PmzZvr/l/UCmIKenFMUACBgYF6Yx8SiQRXV1eGDh1KrVq1zBjZq6Ndu3asXLmS+fPn5+v1H330EatWrcr2uejoaDp16gRA69at+fLLL/WenzVrFoMHD2b69OkcO3aMu3fvUrlyZWbMmMGaNWvyFU9O7BXPS4k18XIC4PfbsbzuU0r3eHJSMqAg6uQdKg+vh9zOCnWamtvbLhokqV2PH/K+hxeuVs+nzD9LT2Nd5D3mVMp+gfTLKLIkdezYMVasWIGdnWGmTUxMZPv27SJJ5UOnTp148803kUqfz4H57LPPsLc3vJ3XarVIJBK6d+9O9+7dCQsL48yZMxw9etQsSepVnoK+ZMkSg8ceP37MRx99pFsW8LLS0tJYu3YtDRo0oE2bNgVyzJKkffv2XL58mfj4eNq3b59ju9jYWD755BODu55FixYZtL1z5w4TJkxAqVTy22+/8frrr7Nr1y5sbGz02mWupQLo2LEjgwYN4r333mPTpk1GxR5x8hM06foFmTPP77wu8ADSNfpjuskpyUjTHJDZWSH9L6nJFDK0asOxX7VWi4tcf9cCF7mVCXsVmKbIktR7772n97VWq+XatWtUqVIFBwcH3n///aIKpURZu3YtH374IREREcyYMYOoqCjc3d1ZsGABlSpV0mvbrl07va/NPS6VOQX9yb8/kOLkQV5T0L88/4iBtd2oXNqW6b/eZumbxaOsS3ayG3QvV66c3sXGy3r8+LHYsiYPEydOzLONKXc9J06cICIigtDQUFxdXVGpVDx79owVK1botStfvjxLliyhefPmHD9+nKpVq/Lnn3/q7RacG/d6A3n0zy48m4xCKs95EfD9uFQ8nQ2nwL/hpz9rL6B6AP/b/htpMck8+fMuZZp4ceurC5SqUc7gtc2dS7EwIpzXbO2wlclIVqu5kZxEC2fDmYAFocjHpFauXImnpyePHj3iypUrlCtXjoULF+bYPikpiQ8//JCEhATatm1rkOxedZkzkRYtWsS0adOoU6cO169fZ/78+Xz99dd6bV8cEM5069atHOuYFbbMKeixEWeMmoK+4dxD/FxsSswU9Ezx8fF069aN4cOHF9gxvby8ePDgAUlJYguUFyUo00gzYnanQi4z+a7H29sbb+/n1VAmTpxIfHy8Xptly5axb98+jh49ir+/P7169eLSpUusXbvWqPgV9qVx8Xud5Jg7uW5++P5PN+lerTSD6rihkOV8ATR5+mRu/JwxLV2dpkYql+LduzrWpQ17vtq6lKa+ozO3k5NI1miwtZbSqXQZnOXGJVhTFXmSOnfuHJMnT2bw4MFs27Ytxz+cmX744Qfat29P3759GTp0KH369KFUqVJFEmtxkpaWRp06dQCoUqWKSWug9u3bl+sgMkBISAi1a9d+mRBzlXUKek6MmYJeHP37779ERESwfv160eVdRNJUat79+FCe7bbM7fDSdz1gOH6lUCgYNGiQXpt69epx69YtoyY0qdOUpMZFgkSKwqEsjy/tR6NKxa16V2xKPZ8xGlDWjnIOCj48eIuOr7nS1rcUjlm2u8mO7L/uPuvSdsRcfoxLTcO7KWe5nDqOTnqPnYmPpbFTqTxjN1WRL+bVarX89ddfVKpUiYSEBJRKZa7t+/fvT69evUhLSyM5OdmkD8arICEhgX79+hEVFcWBAwdIS0sjODiYcuUMP1ghISH069ePwYMHc+HCBd3jT58abo3x/fff6/777rvvmDFjhu7rrMLDwwv8PeWlUxVXlr+Zd+mY4mDlypUsW7aM0NBQFi9ezObNpk13Dw4O5tixY6SnpzNhwgQGDBig1zPRuHFjMR71kpYtW4anpydHjx6lcuXKBAUFYWNjk+1dT79+/bh27ZrB4zlNsHjR3r17jWr38J/dWNm5IrOy5e4fn+FauS3la/fhyVX9tY8SCXT2d2V5R1/S1RrmHLnD5P+Fs/TkPaO+j1Zl/AJdVSFdOBb5ndTQoUPZu3cv06ZN46uvvmLUqLzXqiQlJdGjRw9ee+01o4swhoaG5t2oBFi8eDHp6elERESgUCgICwvDzs6Od9991+BnMHfuXMaPH49arWbu3Ln07duXBg0a6PrQs9q7dy+JiYnUr18frVZLQkIC//zzD4De1X6vXr3o0qUL/fr1M6lApqub4fqgnBn/4Te2ZWa7BJUKR7mch6mp3E9Nwd3aGk9r/UFulUpFeCF9nv7880/279+fEZNWS58+fRg5cmSer9NoNMyYMYNz587RpEkTDh06REBAAGvWrGHmzJlcunRJzBIsIDnd9WTH2GUeFy9eJCIigqZNm7J06VJdPcApU6YYFZNWnYZzxUYAxN09g13p7C/aMvOGg0JGrxpl6VWjLIlpah7EG+4zpVVrSHmShCZNjdzeCuvSdrjWzX72rFqr5UFqKqlaDY4yGeUU1jQvKWNSb775Ju7u7kRGRtKsWTOjXuPo6Mjhw4f55JNP+O677+jTp0+er3nVBoyz/kGqVatWtuNMjo6OuskTDRs2ZNiwYdSqVQs7OzuDn9fu3btZt24dSUlJTJgwgfDw8Gyn6tarV49atWoxe/ZsevfuTdeuXXF2ds4z3mdxhiVlcmb8AFR2LeNCn+BcraxBuy0PI/G0tkEmkfBnXAz+dvYci42mqp293up5uVxu9OfpxWoFefHy8uLx48eUK1eOuLi4bO+As6PRaOjcubNuMXBISIhuynOzZs24cOFCvpLUq3JxZ+xFkr00lZSYBKPaqrRgY2PDjBkzOHbsGAMHDqRcuXLUqVNH9/9Mc+bM4cMPP2Ty5Mm0bduWIUOGEBYWxvjx4/Ospenq5onM2olHIfvQatRIpDKibx5DKrdGapV1DEnLu/XKcTQ8ljrl7dl8/hHhMSl4OVszrL7+5+zPU39x88sLKErZoLwXh52nMyplGu7t/bCv6JzliHApMYEfn0ZRxkrBreQkfGxtSVCp6Fm2PJWzzN4uqIu7Ik9SH3zwAcnJyXonY24VuLds2YK3tzdt2rTB3t6+wPdlKamyG2cqV64cK1asYMyYMbi4uPDJJ58watQoEhMTsz3GmDFj+Ouvvxg/fny2dcogY31P//79efvtt9m7dy8jRoxAJpNRoUIFVq5cWeDvyxiPjt3W+zrm4kOSH2W8x/Jtns8IfJCWyrvuFVgSEc50bx/kEilarZaFEeGFVuLlRWFhYXTs2BFPT08iIyNRKBS0bdsWiUTCkSNHcnydXC6nVatWhISEABlX8JnLO2xtbfPsRs/Jq3JxZ+xFklSj5v4642YeVxy/GVtbWwICAggICGDs2LG6ZR43b97Uqybh4OBA+/bt+eabbxg6dCiQcXH57bff5vk7eBaXjHvd/iijwrCyL4NUbk3s7ZNoNWrK18l6AS9h47mHTGnhyWdnHtDOz4WJzSsQ+iSJlX9EsrLT87uvHVu24zesHlK5FFVyOpE/XcNnYC1ubw+h8oj6WY4I/3v2hKD/zhelWs32Rw/40Mub1fcimOH9/JgFdXFX5EkqJiaGXbt2Gd2+c+fOuq5BNzc3s1XstlSXLl1i8eLFKBQKJkyYoOuGyG6caenSpfz888+6RO/l5cXWrVsNFhpm1bRpU3x9fTl0KPtB5syK0Y6OjowYMYIRI0YQHx9PRETEy761fEtPSCXlUSKlG1UALUhkUhSlbAzaySUSIlNT8LG1JTI1FW8bWyLTUpEW4XXQ//3f/xXIcezt7XWz+JKSknBwcCiQ4+YlOTlZVyHh9u3bXL9+nWrVqlGxYsUi+f6WxthlHo0bN2bKlClUrVqVcePG0axZM/766y+j734lUhkO5avrvi4b8E627WQSCZ5O1qg10LxixkSH2uUdUGsf67VLTEzERpPxvISMc0hmLUeTzZhUskaDWgtySUbbWFU6NlIZabnMzn0ZRZ6kqlSpwrVr14yexVS2bFmDqdTCc4sXL2bJkiWoVCqCgoIYN24crVu35smTJwZt5XK5QSWKsmXLEhSU++K/cuXKGVRxzpTd1gJOTk7UrFnT6PdQ0Ly6VCX64kMSw2Oo0LkKMRcfZtu3HljOgx2PH5CkVnM4+hkOMllGwUz3wt+tePbs2XTr1o0GDRoYPHf69Gl+/PFHFi9ebPTxatSowdmzZ6lbty6nT582KOFTWEaNGsW2bds4cOAAu3fvpnHjxmzbto2uXbsa1S1f0uQ1WznThAkT+PPPP/nrr79wdnbm/v37dOnSJddFxflRu7w9y07ew8fVhoW/36WuuwP/PEykahn9qeV9+vVh+WersPNwJPmREvf2vjw+cQfH1wz3h2rnUpqPbt/E28aW+6kp9CxbjoPPnlDTvmCLMGcq8iR1+fJlTpw4gUQi0a2Qzq1bQ8idXC7XLdrdvHkzw4YNK5Ap+sbWNStdujRLly7l9OnTuiv4unXrMmbMGLPWBnSt446dhyMR+/5FnaLKto27tTVTK/qQptGgVKuxk8mwLsDFtLmZPXs2X3zxBQsXLsTFxYXSpUuTkJDAkydPaNu2LbNnzzbpeJ06dWLatGn07dsXf39/vfGPorB//362bduGra0tKpWKwYMHv3JJytgK/FZSKxyt7WnWrJnR4/L5NbhuOf55kMjFR4k4KmQ8SkyjnW8pmlbUnz7+dte3+T71BGkxyShcbbn3fSiV+tRAks3aqpalXKjr6MiTtHTcFAq+enifMRUqIiukoZgiS1KZhVAzZzLl9LxgGmPHmdTJCWhVxq2dksitjK5rNm3aNLp168bYsWOxt7dHqVRy/PhxJk2axJYtW/L7tgqEjZsD3n2qE3/NsOsTMuqNZbXi3m3e9/BCC5S2Mn6mYr5is7Hhgw8+4IMPPuD27dvExsbi6uqqtwjUGFkr3Rd0zTdjxMTEcOvWLcqUKaPbm0mtVpOWZjh7rKQztgL/1o7BpCZG59kOCmabmLoeDtT1yL37t32r9qSSjtQqIymlJ6QR9slpAKpN1E+kY69fxUoiRfFfv3icSsWM8OsALPMr+HV+RZakZs+eTZMmTWjfvr1eXTmlUsn//vc/zp07x9KlS4sqnBLD2HEmrSqdu5/kPbUZMgaAja1rlpCQwFtvvaX72sHBgc6dO7Njx458vJuCkRarv3Hc0zP3sfcuBVr0xqYWR4RjLZVS6r+V8g9TU/niYSQA0yoWXcklH5/iW96pe/furF+/nvDwcHbs2EFgYCDdunVj/Pjx5g7NYknSVWbdJiY7X23/ipEzxlLudR/sPBy5+dUFKg/Lfpr9nEp+7It6RNcybnjb2LLs7u1CPV+KLEktXryYH374gREjRhAdHY2rqyuJiYk4OzvTq1cvMSEin/I7zmQMY+qa+fj4sGDBApo3b46DgwNKpZJTp06Z9Q/vzS/+RqqQYeWoQKuF1CdJ3N1/FYkE/IY+P/HmVarMrscPaeLsTB0Hp0I/2UqiYcOGGTz2ww8/ZFvgWLBcFb0r4t27OpG/3CDJ3SHXsmPlFdaM9vBi1+OHVLQxnJBU0IosSUkkErp160a3bt1ITU0lLi6OUqVKmbQAVDC/F9dfLVq0iMOHD3P27FkSExNxcHCgQYMGdOjQwWwxVnm/IZEHr+NSuzxO/mVyvCp0kssZ5eHJD0+juJGUhMYMpZbMWTexIGQ3u9Te3p7JkyebbQmCkD9SKxleXavy7HxkbmU0AVBIpbzrXoHjsdGFft6YZWdea2tr3NyKZh1KSWZskUwAJ0nBfJBeXH8lkUjo0KGDQVIy5x9fub2Cir2r8/jYbRIjYp8vu8+GRCKhW9lyXFEmEGPkmF1B+uSTT3j27BmdO3fm7bffxrGAt6kvbDnNLo2KijJ3aEI+lW5QgdINjJvh2rqUK61LFe4EKbF9/H+K43oPY4tkAuyf28KkY5uy/io7xhStLUwSiYTybX1JuPmM9PjUPNvXsHekxn9TaMOUiVS1L5p1RmvXriU+Pp6DBw8yceJEnJyc6NGjBy1amPb7MpecZpeKRfdCQSnyArPHjx/XLQC1JJk1BA8cOMC0adO4fPkyM2bMYN++fQZtzVFUtahlXiHPnTuXxYsXc/z4cQCD9VeXLl0yumitOThWLo13r4xFj4m3Y4x6TXiKKSWbXl54eDjXrl0jJiaGihUrcvbsWT788MMijSG/MmeXJiUl6WaXzpgxgzt37pg7NKGEKPI7qTNnzrB27VqaNWtG9+7dLa4/3pj1Hl26dOHdd99l3LhxWFsbbihWEhi7/sqUxcTmlnQ/Hgef50UwlWo1vzx7gp1MRm17R9Y9uIsUCUPKexRZTO+88w5VqlShR48ezJs3T3cHMnny5CKL4WXkp4qJIJiiyJPUtGnT0Gg0/PHHH6xZs4aoqCh69OhB9+7dzTqJwpT1HnXr1qVChQr06tXLpKKqxYmx668KazHxy1AlpxN1MgKZjRwn/zJE7L0CEgleXfTXcGx+cI9GTs48S09n5b07zPL2xUoqZX3kXYK8i2YrkF27dmU7DlVcJh3kNLu0qCpeCCVfkXf3paam8r///Y8dO3YQGxtLr169AMPt5Yvai+s9EhMT6dy5M4MHDzZom1lUddeuXaSlpTFixAj69etXbK5+jbF06VIqV65scIWcdU0UWGZ3z91vr2LjZg9aCN96Ed/A2vgNrcvD3/S7aZM1Gpo5u/BOGTd8bW0po1DgLJebUHP95Q0YMIDGjRvTr18/GjduTPv27enRowfHjh0rwigKXnbd5IKQH0V+J9WtWzc6dOjAjBkz9NbSmLsP25T1HpZYVLWgGbv+yhK7ezSpKlzrZNTqS4qMR+GSMSHmxexT1krBzscP6O/mzjjPjEoPh6KfYi8rutOiQoUKbN++nVKlShEXF8fMmTMJDg7mvffeKxabFb7sBBtByEuR30kdPHiQ999/n7Jly5KQkMCtW7cAmD59erbtExMTGTVqFIMGDWL8+PEmbYtuisxdNu/evcuAAQNo3749o0ePzjZ55reoaua2CiVJZjLLnBkJBbeYOL8UrrZEHryOVqPFZ0BGVeknf95Fbqu/q/Mw9wpUs3NAmmUmmqNMzkgPUzZkfDlPnjzRdY86Ozvz7NkznJycUKuNW1pgbsZOsBGE/CryO6mPP/6Yv/76i5SUFFJSUvD09Myxnh9kTGR444036NWrF2vXruW3337Tbe5WkDL3M1m4cCHTpk2jTp06XL9+nfnz5xtUYX9xssfYsWP5/PPPDY6Zdat1rVbL5s2bdd2aL96lWBJTC2VaGq9u1Yi/9hRJlj035PYKvHoG6LWTSiTUc9QvtNnUuVRRhKjTsmVLhg4dSq1atbh8+TItWrTgm2++0Y3zWTpLHJMUSpYiT1KXLl3i//7v/5g3bx4TJkzIc6ptz549sbLKuAJWq9W6fxeWtLQ0XQXpKlWqZHvnVr9+fWxsbLCxsUGr1fL06dNsN6o7ePAgcXFxtGrVCoCUlBTu379fqPEXBEstlGksiVRisBOvS+3yRfK9TdWwYUPefPNNbt++zVtvvYW/vz/R0dH07NnT3KEZxdSNNAXBVEWepJycnJBIJCiVSlxdXfOslpy5eds///zD2bNnC61wZUJCAv369SM+Pp4DBw7w9ttvs3Llymy38/72229ZtmwZH3zwAdWrVycwMJDt27cbtNu8ebPeFuxnzpzhgw8+yDEGtVrNzZs3USqVODo64uvra9GV4S2xUGZx8/nnn7Nr1y69HUzNucWJqSxxTFIoWYo8SbVq1YoNGzbg4+PDsGHDjJp2/vfff7N48WLWrVuHXG5cyKGhoSbFtXjxYtLT04mIiEChUBAWFoadnR3vvvtutscaNWoU69evx8fHh+Tk5By/X5s2bQgJCWHo0KHExMTk2O78+fPs2LGD8uXLY2trS3JyMg8ePGDgwIE0btw429e4upkydmL8AmpjW5qyJFulUhGe5b0X59gLkkqlom/fvvj4+Oj+0BenYssvU+D48ePHJCUlGVWM2JS2QslS5Elq2LBhus0Ow8LC8vzQ3blzh0WLFrFhwwaT6v1lvTI1Rdbtm2vVqpVrDbrPPvuMffv28c8//+T6/apVq0abNm04dOhQju0++ugj9u/fr7ftd2JiIkOGDMlxt89ncaZURjB+YrWxLU2Zqi2Xy/Xee3GOPTeZY5vGmjJliknti7OjR4+ydOlSJBIJgYGBfPvtt9jZ2dG6dWuDJSimtBVKtiJLUoGBgQb1vDKT1bZt23J83RdffEFCQoJuDdLgwYN54403CjXWrPbu3cvMmTNzfL5Pnz66ihQvJrQXp+dmbsGeXYVotVptcJcolUqRFtFOsYJ5BAQEsGnTJp48eULr1q2pWrWquUMymrEFjhVyGY72CjZs2MD+/ftRKpX06NGD3377DRsbG/r372+QeExpK5RsRZaklixZkq/XLVy4sIAjMU1uCepFLyY0UypEv/fee/To0QM/Pz8cHBxITEzk9u3bTJgwoSDehmChgoKCaN++PefPn8fd3Z1Zs2axc+dOc4dlFGMLHG+Zm1EhX6VSIZPJdBerMpkMrVab7XR7U9oKJVuRJakKFTJKvz969Ijly5cTExNDhw4d8Pf31z1nTrlV7DZ2jODFhGZKhegOHTrQtm1bbt26pduXydfXt9BnMwrmFR8fT7du3Thw4AC1a9c2dziFavDgwXTq1InXXnuNzp0706dPH2xsbOjcuXO+2maeJ0LJVuRjUrNmzeL9999n7dq1NG3alClTpvDNN98UdRgG2rVrx8qVK5k/f36ebY1NaKZOz5XL5fj7+2f7nFAyWVlZcf78ebRaLWFhYdgUwU6n5pK56Wmma9euYW9vj6en4SQaY9o2b96coKAg+vfvX5hhC2ZW5ElKpVLRoEEDJBIJ3t7eFnNStm/fnsuXLxMfH0/79u1zbWtsQjNlem5B3MkJxc+CBQtYtmwZ0dHRfP7558ybN8/cIRWa2NhYNm7cyJkzZ1AqlTg4OFC3bl3GjBljMO3emLYBAQFERkbSr18/Ro4cqVurKJQsRZ6kHB0d+f7770lNTeW3336zqJ1IJ06caFQ7YxOaKRWiTbmTE0oODw8P5syZQ2pq3hszFnfTpk2jW7dujB07Fnt7e5RKJcePH2fSpEls2bLF5LZWVlZMmTKFiIgItmzZwurVq/H398fT09Poc1mwfEWepBYvXszGjRtxdnbm/PnzZp8YkV8vcxJkN2PQlDs5oeSYN28eZ86cwc3NzajZrsVZQkKCXhV9BwcHOnfuzI4dO/LVNrPQs7e3N/PmzUOj0XD9+nWzF6sWClaRJqnU1FT+/fdfqlevTrt27ahdu7ZFVFQwdSrty8ppxqC4+nv1/Pvvv/zvf/97JbqpfHx8WLBgAc2bN8fBwQGlUsmpU6eyXStpTNuxY8fqvUYqlVK1atViNY1fyFuRJalLly4RFBREvXr1cHV15ciRI8ydO5cVK1aY/UNl6lRaYxXEOFNui4mF4s/Pz4/Y2FhcXFzyblzMLVq0iMOHD3P27FndzLwGDRrQoYPheWVM2ypVqrB69Wrkcjk9e/Zkzpw5KJVK5syZQ/Xq1YvyrQmFqMiS1KpVq/jiiy/w8Hi+Nfe9e/f46KOPik2dLwdpGqoE4yolSORWBTLOlNdiYqF4u3LlCq1ataJs2YyCuC8WKS5JJBIJHTp0MEhK2V2IGdN26tSpdOzYEaVSSZ8+fVi9ejVlypRh9uzZxWatmZC3IktSKpVKL0FBxmy34rQ4T6JRcfcz41a7Vxy/uUDGmUSCKtkOHjxo7hDMzpQLsaxtk5OTdZOQ9u7dS8OGDQFeia7TV0mRJamcyvtoNJqiCsEsjB1nElPQXy1r167VbVNz6NAh3R1DUFBQvquzFFemXIhlbevm5saMGTNIT09HoVCwadMm7O3tcXZ2LowwBTMpsiQVHh6e7R/i8PDwogrBookp6K+WrIVod+zYoUtSkZGR5gqp0JlyIWZM2xUrVnD8+HG8vb2xt7dn69atpKenv3JJvqQr0jGp7HTv3r2oQihSpu5uK6agv1oyp0+/+O+S3FVlyoWYMW3lcjnt2rXTfZ25PcitW7csav2l8HKKLEk1atSoqL6VRTB2d9sNXZ5fQYop6K+OrMmoMBLT77//zp07d0hISGDcuHEFfvz8MOVC7GUu2sRko5KlyBfzvozg4GCaNGlCmzZtzB2KILyUrN3fL/67IJw+fZqgoCA2bdpEdHS0xez2a8qFWF5tM0snnT59mqSkJL3SSULJUSySlEajYcaMGZw7d44mTZqYO5wCZZsGqUnRRrWVKqywEt0YJULW7u+sXd4F1f1t7A7WxZkpZZaE4qtYfJI1Gg2dO3fOtlpycSdJV3F++Cij2jb4anMhRyMUlcLu/q5Tpw5ff/01KpXKYu6iCpopZZaE4qtYJCm5XE6rVq0ICQkx+jWhoaFGt3V1Mzb5afNuYmJL44+YsdYsPMv7Mj5u076TiF3fi7Fbiszu7xYtWjB16lSioqIICAhg9uzZFjX5xpRF8MlyOenkvSzFSmplUpklofgqFkkqP6pVq2Z022dxxp1AYPwAt7EtTRkyl8vleu/L+LhN+04idn0vxp6brFPLC8uL3d+HDh0iICCANWvWMHPmTC5dukStWrUKPQ5jmbIIvtSEzUZPODKlzJJQfJXYJCUIJdWL3d8hISF06tQJgGbNmnHhwoV8J6ni0gOhSlcRdicMLy8vvLy89J67du1anq8vrr0nULxjzw+RpAShmHmx+zsxMRE7OzsAbG1tUSqV+T52cemBkFsZf3ebneLaewLFO/ac5NYDUaySlKWs9xAES2Jvb09SUhKAbip2SSdmxb46ilWSEgTBUI0aNTh79ix169bl9OnT2e78XNKIWbGvjuyrvgqCUGx06tSJ0NBQ+vbti0wmo06dOuYOSRAKjLiTEoRiKmv395o1a8wXiCAUInEnJQiCIFgskaQEQRAEiyWSlCAIgmCxRJISBEEQLJZIUoIgCILFEklKEARBsFgiSQmCIAgWSyQpQRAEwWKJJCUIgiBYLIuvOJGenm6woZsgCILwarD4O6nMDd127dpFUlISly5dMndIgmDR0tPTmTBhAgMGDGDhwoXmDkcQXorFJ6mQkBAaNmwIPN/QTRCEnIkLO6EksfgkVZAbugnCq0Bc2AkliUSr1ZqyI3CRW7RoEW+99RZ169blp59+Ijo6miFDhuT6mtx2eRSEwla/fn2zfv+ZM2cyZMgQ/P39OXLkCGFhYYwdOzbP14nzRjCnnM4bi584kZ8N3cz9R0IQzCm/O/WK80awRBbf3Sc2dBME02Re2AGcPn2a2rVrmzkiQcg/i+/uEwTBNGlpaUybNo2HDx/i7+/Pxx9/bO6QBCHfRJISBEEQLJbFd/cJgiAIry6RpARBEASLJZKUIAiCYLFEksqHkydPcuTIEaKjowkMDKRv375s3rw519dERkYybNgwIGNge8GCBUURKvA8XgCtVsu4ceO4fv06ADExMYwePZp+/fqxePHiXI9z5coVJk+erHvd6tWrCzdwnsf++PFjAgMD6dOnD99//z0AYWFhDBgwgL59+7Jhw4Ycj/H111/Tt29f+vXrx5UrV4osdkGfOG/EeZMfIknlwzfffEPbtm358ccf6dSpE3v27OHw4cM5VsM4f/48kyZNIjY2FgCFQoGHh0eRLZ7MjFepVDJ69Gi9MjmbNm2if//+7NmzB3d3d1JTU7M9xuHDh5k5cybp6ekAuLi4kJSURGRkZJHEvn37dkaNGsWePXvYuXMnWq2WTz/9lKVLl7J3715OnTrFs2fPDF4fGxvLsWPH2Lt3L8uXL+eTTz4pstgFfeK8EedNfogkZaKrV6/i4eGBRCKhevXqpKSkoFKpALCyssr2NVKplE2bNuk91q5dO/bv31+k8aampjJmzBiaNm2qe/7KlStcvXqVwMBASpUqhbW1dbbHcXBwMLiKev3113VXZ4Ud++TJk2nevDmJiYkASCQSPv74Y7y8vABQqVTI5YZr0x0dHfnkk090bTJ/R4Udu6BPnDfPifPGNCJJmejixYv4+fkBYGNjw/bt2+nUqRNVqlRBoVBk+5p69erh7Oys95i3tzdXr14t0nhdXV0NFnY+fvwYb29vvvzyS3bv3k1MTEy2x2natKnBiejn50dISEjhBI5+7BKJhPDwcLp06ULNmjXRarWULl0agC1bthAQEGDwMwaQyWSUKlWK5ORk5syZw8iRI4skdkGfOG+eE+eNaUSSMlF8fDylSpUCYOPGjSxevJjDhw+TnJzM+fPnjT6ORCJBKi38H3/WeLPj6OhIs2bNUCgU1KhRg4iICKOP7erqSnx8fAFEmb0XY/fz8+P3338nKSmJP//8E4Bt27Zx/vx5goKCcjyOUqnk/fffp3///rqKJYUdu6BPnDfPifPGNCJJmcjZ2Vl362xnZ4e9vT0SiYTSpUubXKE9p26OgpQ13uzUqlWL8+fPo9VquXbtGp6enkYfOzExMdcT+WVljX3p0qWEhYUBGT93qVTKwYMHOXv2LGvWrMn1Zzlx4kT69OlD586diyx2QZ84b54T541pRJIyUd26dQkNDQVg7NixLF++nIEDB5KYmEjLli05cOAAJ06cyPM4d+7cwd/fv7DD1Ys3O6NHj2bHjh306tWLdu3aUaZMGTZt2pTrazKFhoZSr169ggxXT9bYe/XqxYIFCxg0aBBSqZSmTZuydu1aHjx4wNChQwkMDOTevXsGsf/999+cO3eO3bt3ExgYyNy5c4skdkGfOG+eE+eNibSCycaMGaPVaDTZPvfPP/9oT5w4kecxNm3apL1w4UJBh5at3OLNzs8//6y9detWnu0WLFigffDgwcuElqfiHLugT5w3GcR5YxpxJ5UPvXv31q2feJGHhwctW7bM9fVpaWncv3+funXrFkZ4BnKLNzsNGjTA19c31zbR0dFYW1vj7u7+suHlqjjHLugT5404b/JDFJgVBEEQLJa4kxIEQRAslkhSgiAIgsUSSUoQBEGwWIb1MIRi7cyZM0yaNAlfX180Gg1SqZQVK1awZMkSVq9ezaFDh2jQoAGurq65HicwMJDU1FS91fILFy7E29u7sN+CIBQ5cd5YLpGkSqCWLVuyZMkSAPbt28eWLVt09cO2b99OQEBAnicbwKpVq0xapCgIxZk4byyT6O4r4WJjYylfvjxt27blxIkThIaGMn36dMLDw+nXrx8DBgxg5MiROVZxftHChQvZtGkTKSkp9OjRg1u3bhEUFERQUBCDBw9m0KBBPH78uJDflSAULnHeWA5xJ1UCnTx5ksDAQJKTk7l37x7ffPMNW7dupVWrVlSrVo3g4GCOHz9Os2bNGDt2LMeOHSMuLg43Nze940yaNEnXbWFnZ8fGjRuZMmUKAwYM4MqVK/Tv319XyLJKlSosWbKEAwcOsHHjRt0KdUEoLsR5Y5lEkiqBsnZbnD59mlmzZhm06dmzJxs2bGDo0KGUL18+21In2XVb2NjY0LNnTz755BOWL1+ue7xhw4YA1K5dm4MHDxbk2xGEIiHOG8skuvtKuPLly+v27YGMKtJarZYjR47QokULtm3bRuXKlfnxxx+NOt6zZ8/Yt28fw4YN0zvZ/v33XyBjm4DKlSsX7JsQhCImzhvLIe6kSqDMbguZTEZaWhrTpk3TbV9dt25dJk2axLJly5gxYwbW1tYoFAoWLlzIzZs32bt3r+4KMmu3BcCYMWPYunUrH374IW3atGHIkCH88ccfAPz2228cPHgQGxsbli1bVvRvWhBekjhvLJMoiyTopKamsn79eiZMmGDS64KCgujevTuNGzcunMAEwYKJ86Zwie4+QUelUjFs2DBzhyEIxYo4bwqXuJMSBEEQLJa4kxIEQRAslkhSgiAIgsUSSUoQBEGwWCJJCYIgCBZLJClBEATBYokkJQiCIFis/weOogDOXG6PPgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2)\n",
    "# fig, axs = plt.subplots(2, 2, figsize=(10,6))\n",
    "# fig, axs = plt.subplots(2, 2, figsize=(15,8))\n",
    "\n",
    "area_df.plot.bar(ax=axs[0, 0], legend=False,\n",
    "                 y=\"Area\",\n",
    "                 rot=0,\n",
    "                 ylabel=\"Area (µm²)\",\n",
    "                 width=0.9\n",
    "                 )\n",
    "axs[0, 0].grid(False, axis=\"x\")\n",
    "# axs[0,0].set_yticks(np.arange(0,12000,2000))\n",
    "# Display values\n",
    "for i, v in enumerate(area_df.values.flatten()):\n",
    "    axs[0, 0].text((i//4)-0.415+(i % 4)*0.225, (v+500 if v < 5000 else v-5000),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "power_df.plot.bar(ax=axs[0, 1], legend=False,\n",
    "                  y=\"Power\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Power (mW)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[0, 1].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(power_df.values.flatten()):\n",
    "    axs[0, 1].text((i//4)-0.415+(i % 4)*0.225, (v+0.9 if i < 8 else v-6),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "delay_df.plot.bar(ax=axs[1, 0], legend=False,\n",
    "                  y=\"Delay\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Delay (ns)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[1, 0].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(delay_df.values.flatten()):\n",
    "    axs[1, 0].text((i//4)-0.415+(i % 4)*0.225, (v+0.25 if i < 8 else v-1.2),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "energy_df.plot.bar(ax=axs[1, 1], legend=False,\n",
    "                   y=\"Energy\",\n",
    "                   rot=0,\n",
    "                   logy=\"sym\",\n",
    "                   ylabel=\"Energy (pJ)\",\n",
    "                   width=0.9\n",
    "                   )\n",
    "axs[1, 1].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(energy_df.values.flatten()):\n",
    "    axs[1, 1].text((i//4)-0.415+(i % 4)*0.225, (v+v/3 if i < 8 else v-v/1.15),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   # color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "# plt.legend(loc=\"upper left\")\n",
    "axLine, axLabel = axs[1, 1].get_legend_handles_labels()\n",
    "fig.legend(axLine, axLabel,\n",
    "           loc='upper center',\n",
    "           ncol=4,\n",
    "           bbox_to_anchor=(0.5, 1.05)\n",
    "           )\n",
    "\n",
    "# fig.suptitle(\"Posit Multiplier synthesis\")\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(\"figs/mult.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, axs = plt.subplots(2, 2)\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 4))\n",
    "# fig, axs = plt.subplots(2, 2, figsize=(15,8))\n",
    "\n",
    "area_df.plot.bar(ax=axs[0, 0], legend=False,\n",
    "                 y=\"Area\",\n",
    "                 rot=0,\n",
    "                 ylabel=\"Area (µm²)\",\n",
    "                 width=0.9\n",
    "                 )\n",
    "axs[0, 0].grid(False, axis=\"x\")\n",
    "# axs[0,0].set_yticks(np.arange(0,12000,2000))\n",
    "# Display values\n",
    "for i, v in enumerate(area_df.values.flatten()):\n",
    "    axs[0, 0].text((i//4)-0.39+(i % 4)*0.23, (v+500 if v < 5000 else v-5000),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "power_df.plot.bar(ax=axs[0, 1], legend=False,\n",
    "                  y=\"Power\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Power (mW)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[0, 1].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(power_df.values.flatten()):\n",
    "    axs[0, 1].text((i//4)-0.39+(i % 4)*0.23, (v+0.9 if i < 8 else v-6),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "delay_df.plot.bar(ax=axs[1, 0], legend=False,\n",
    "                  y=\"Delay\",\n",
    "                  rot=0,\n",
    "                  ylabel=\"Delay (ns)\",\n",
    "                  width=0.9\n",
    "                  )\n",
    "axs[1, 0].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(delay_df.values.flatten()):\n",
    "    axs[1, 0].text((i//4)-0.39+(i % 4)*0.23, (v+0.25 if i < 8 else v-1.2),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "energy_df.plot.bar(ax=axs[1, 1], legend=False,\n",
    "                   y=\"Energy\",\n",
    "                   rot=0,\n",
    "                   logy=\"sym\",\n",
    "                   ylabel=\"Energy (pJ)\",\n",
    "                   width=0.9\n",
    "                   )\n",
    "axs[1, 1].grid(False, axis=\"x\")\n",
    "# Display values\n",
    "for i, v in enumerate(energy_df.values.flatten()):\n",
    "    axs[1, 1].text((i//4)-0.39+(i % 4)*0.23, (v+v/3 if i < 8 else v-v/1.15),\n",
    "                   str('%.2f' % v), rotation=90,\n",
    "                   color='k', fontweight='bold'\n",
    "                   )\n",
    "\n",
    "# plt.legend(loc=\"upper left\")\n",
    "axLine, axLabel = axs[1, 1].get_legend_handles_labels()\n",
    "fig.legend(axLine, axLabel,\n",
    "           loc='upper center',\n",
    "           ncol=4,\n",
    "           bbox_to_anchor=(0.5, 1.05)\n",
    "           )\n",
    "\n",
    "# fig.suptitle(\"Posit Multiplier synthesis\")\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(\"figs/mult.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single component comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_color_palette = [\"#246799\", \"#ffa600\", \"#df5fa1\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Component</th>\n",
       "      <th>Design</th>\n",
       "      <th>Area</th>\n",
       "      <th>Power</th>\n",
       "      <th>Delay</th>\n",
       "      <th>Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decoder</td>\n",
       "      <td>Old</td>\n",
       "      <td>591.057600</td>\n",
       "      <td>0.589600</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.866712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decoder</td>\n",
       "      <td>New</td>\n",
       "      <td>392.078402</td>\n",
       "      <td>0.268700</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.236456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decoder</td>\n",
       "      <td>IEEE</td>\n",
       "      <td>85.142401</td>\n",
       "      <td>0.042387</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.006782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Encoder</td>\n",
       "      <td>Old</td>\n",
       "      <td>843.662399</td>\n",
       "      <td>0.817200</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.931608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Encoder</td>\n",
       "      <td>New</td>\n",
       "      <td>564.244803</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.476160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Component Design        Area     Power  Delay    Energy\n",
       "0   Decoder    Old  591.057600  0.589600   1.47  0.866712\n",
       "1   Decoder    New  392.078402  0.268700   0.88  0.236456\n",
       "2   Decoder   IEEE   85.142401  0.042387   0.16  0.006782\n",
       "3   Encoder    Old  843.662399  0.817200   1.14  0.931608\n",
       "4   Encoder    New  564.244803  0.512000   0.93  0.476160"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components_df = pd.read_json(\"Misc/synopsys_results.json\")\n",
    "components_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder_df = components_df[components_df[\"Component\"]\n",
    "                           == \"Decoder\"].groupby([\"Design\"], sort=False).sum().T\n",
    "encoder_df = components_df[components_df[\"Component\"]\n",
    "                           == \"Encoder\"].groupby([\"Design\"], sort=False).sum().T\n",
    "adder_df = components_df[components_df[\"Component\"] ==\n",
    "                         \"Adder\"].groupby([\"Design\"], sort=False).sum().T\n",
    "multiplier_df = components_df[components_df[\"Component\"]\n",
    "                              == \"Multiplier\"].groupby([\"Design\"], sort=False).sum().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize results with respect Old decoding format\n",
    "# designs = [\"New\", \"Old\"]\n",
    "for d in [\"IEEE\", \"New\", \"Old\"]:\n",
    "    decoder_df[d] /= decoder_df[\"Old\"]\n",
    "    encoder_df[d] /= encoder_df[\"Old\"]\n",
    "for d in [\"New\", \"Old\"]:\n",
    "    adder_df[d] /= adder_df[\"Old\"]\n",
    "    multiplier_df[d] /= multiplier_df[\"Old\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Design</th>\n",
       "      <th>Old</th>\n",
       "      <th>New</th>\n",
       "      <th>IEEE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.663351</td>\n",
       "      <td>0.144051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.455733</td>\n",
       "      <td>0.071891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delay</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.598639</td>\n",
       "      <td>0.108844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.272820</td>\n",
       "      <td>0.007825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Design  Old       New      IEEE\n",
       "Area    1.0  0.663351  0.144051\n",
       "Power   1.0  0.455733  0.071891\n",
       "Delay   1.0  0.598639  0.108844\n",
       "Energy  1.0  0.272820  0.007825"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Design</th>\n",
       "      <th>Old</th>\n",
       "      <th>New</th>\n",
       "      <th>IEEE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.668804</td>\n",
       "      <td>0.100084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.626530</td>\n",
       "      <td>0.049766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delay</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.228070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.511116</td>\n",
       "      <td>0.011350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Design  Old       New      IEEE\n",
       "Area    1.0  0.668804  0.100084\n",
       "Power   1.0  0.626530  0.049766\n",
       "Delay   1.0  0.815789  0.228070\n",
       "Energy  1.0  0.511116  0.011350"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Design</th>\n",
       "      <th>Old</th>\n",
       "      <th>New</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.918319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.861560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delay</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.847458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.730135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Design  Old       New\n",
       "Area    1.0  0.918319\n",
       "Power   1.0  0.861560\n",
       "Delay   1.0  0.847458\n",
       "Energy  1.0  0.730135"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adder_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\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>Design</th>\n",
       "      <th>Old</th>\n",
       "      <th>New</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.073822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.078652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delay</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.054137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Design  Old       New\n",
       "Area    1.0  1.073822\n",
       "Power   1.0  1.078652\n",
       "Delay   1.0  0.977273\n",
       "Energy  1.0  1.054137"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiplier_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_557280/1211876248.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  ax = decoder_df.drop(\"IEEE\", 1).plot.bar(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = decoder_df.drop(\"IEEE\", 1).plot.bar(\n",
    "    rot=0,\n",
    "    #  ylabel=\"Relative resource utilization\",\n",
    "    color=my_color_palette,\n",
    "    figsize=(8, 3),\n",
    "    #  title=\"Decoder component\"\n",
    ")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "# plt.legend(loc = 'center right',bbox_to_anchor=(1.2, 0.5))\n",
    "plt.legend([\"Sign-magnitude decoding\", \"2's complement decoding\"],\n",
    "           loc='upper center',\n",
    "           ncol=2,\n",
    "           bbox_to_anchor=(0.5, 1.15)\n",
    "           )\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.savefig(\"figs/decoder_cmp.pdf\", bbox_inches=\"tight\")\n",
    "plt.savefig(\"figs/decoder_cmp.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = decoder_df.plot.bar(\n",
    "    rot=0,\n",
    "    #  ylabel=\"Relative resource utilization\",\n",
    "    color=my_color_palette,\n",
    "    figsize=(8, 3),\n",
    "    #  title=\"Decoder component\"\n",
    ")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "# plt.legend(loc = 'center right',bbox_to_anchor=(1.2, 0.5))\n",
    "plt.legend([\"Sign-magnitude decoding\", \"2's complement decoding\", \"IEEE 754\"],\n",
    "           loc='upper center',\n",
    "           ncol=3,\n",
    "           bbox_to_anchor=(0.5, 1.15)\n",
    "           )\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.savefig(\"figs/decoder_IEEE_cmp.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_557280/2725045927.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  ax = encoder_df.drop(\"IEEE\", 1).plot.bar(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = encoder_df.drop(\"IEEE\", 1).plot.bar(\n",
    "    rot=0,\n",
    "    #  ylabel=\"Relative resource utilization\",\n",
    "    color=my_color_palette,\n",
    "    figsize=(8, 3),\n",
    "    #  title=\"Encoder component\"\n",
    ")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "# plt.legend(loc = 'center right',bbox_to_anchor=(1.2, 0.5))\n",
    "plt.legend([\"Sign-magnitude decoding\", \"2's complement decoding\"],\n",
    "           loc='upper center',\n",
    "           ncol=2,\n",
    "           bbox_to_anchor=(0.5, 1.15)\n",
    "           )\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.savefig(\"figs/encoder_cmp.pdf\", bbox_inches=\"tight\")\n",
    "plt.savefig(\"figs/encoder_cmp.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = encoder_df.plot.bar(\n",
    "    rot=0,\n",
    "    #  ylabel=\"Relative resource utilization\",\n",
    "    color=my_color_palette,\n",
    "    figsize=(8, 3),\n",
    "    #  title=\"Encoder component\"\n",
    ")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "# plt.legend(loc = 'center right',bbox_to_anchor=(1.2, 0.5))\n",
    "plt.legend([\"Sign-magnitude decoding\", \"2's complement decoding\", \"IEEE 754\"],\n",
    "           loc='upper center',\n",
    "           ncol=3,\n",
    "           bbox_to_anchor=(0.5, 1.15)\n",
    "           )\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.savefig(\"figs/encoder_IEEE_cmp.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = adder_df.plot.bar(\n",
    "    rot=0,\n",
    "    #  ylabel=\"Relative resource utilization\",\n",
    "    color=my_color_palette,\n",
    "    figsize=(8, 3),\n",
    "    #  title=\"Adder component\"\n",
    ")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax.set_ylim(0.50, 1.05)\n",
    "# plt.legend(loc = 'center right',bbox_to_anchor=(1.2, 0.5))\n",
    "plt.legend([\"Sign-magnitude decoding\", \"2's complement decoding\"],\n",
    "           loc='upper center',\n",
    "           ncol=2,\n",
    "           bbox_to_anchor=(0.5, 1.15)\n",
    "           )\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.savefig(\"figs/adder_cmp.pdf\", bbox_inches=\"tight\")\n",
    "plt.savefig(\"figs/adder_cmp.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A87Pq4uJiPDw88PDwoKioqCqaFRERqRWqJKyjo6Mxm81YLBby8vJ4/PHHASguLubgwYO0adOG3/72tyxdupQ9e/bUyO/WFRERqS6Gvc46KytLn1mLiEitcaPc0xd5iLiq5DyUF7uvPbMv+DR0X3siYjgKaxFXlRfD2ht/J3CVqorvjRaRGk1hXV20dyYiIjdJYV1dtHcmIiI3SV+RKSIiYnAKaxEREYNTWIuIiBicPrMWEZGaoRafmKuwFhGRmqEWn5irw+AiIiIGp7AWERExOIW1iIiIwSmsRUREDE5hLSIiYnAKaxEREYNTWIuIiBicwlpERMTgFNYiIiIGp7AWERExuJsO62nTprF9+3auXLlCUlISFouFhQsXAnD69GkGDhxIXFwca9euBSA1NZXY2Fi2bNkCwIYNG5w/i4iIyM37xbCuqKhg1KhRbN68GYC0tDT69etHWloau3fvpqCggHnz5pGcnIzVaiUtLY3S0lJycnJIT09n8+bNOBwOtm3bRrdu3W55h0RERG43v/hFHhUVFfTu3ZuwsDAAsrOz6du3LyaTiQ4dOpCdnc1XX31F27ZtMZlMtGjRgsOHD+Pp6UlZWRleXl5s2rSJ7t27YzKZbnmHREREbje/GNaenp48+uijZGdnA3D16lX8/f0B8PPzo7CwkIqKCmcQ+/n5YbPZ6N27N8nJybz44ossXryYxMRExo0bx+OPP06nTp1uqri8vLzK9stl9YPupgL3vZm4y7cCs9taA3uZncNu3J7upLGr2dw9fh44uHj2tNvau925c/xq82vP5a/IrFOnDjabjYCAAGw2GyEhIXh4/N/R9B+nRUdH06lTJzZt2kS3bt1YtWoVEydO5O23377psI6IiHC1vEo7c7GQR0evcFt7X73Xy21tAXh5erl1e7qTxq5mc/f4fTJ94G29Pd3NneN3u7/2srKyrjvN5bPBW7duTWZmJg6Hg7179xIVFUXz5s3Zt28fdrudgwcPEh4eDoDD4WDTpk307NmT0tJSTCYTxcVu/OJwERGR24DLYT1w4EDWrl1LTEwM0dHRBAcH8/LLLzNz5kzi4uKIi4vD29sbgK1bt9K1a1dMJhNdu3YlNjaWdu3aVXknREREbmc3fRh8+PDhzp9/vGTrR6GhoVit1p8s0717d+fPPXv2pGfPnpWpUUREpFbTTVFEREQMzuUTzEREarIG3jawXXJfg2Zf8GnovvbktqSwFpFaxewogf/X2H0NPnXSfW3JbUuHwUVERAxOYS0iImJwCmsRERGDU1iLiIgYnMJaRETE4BTWIiIiBqewFhERMTiFtYiIiMEprEVERAxOYS0iImJwCmsRERGDU1iLiIgYnMJaRETE4BTWIiIiBqewFhERMTiFtYiIiMG5HNaFhYUMHTqU+Ph4UlJSuHLlCklJSVgsFhYuXAjAxo0biY2NZfny5QDk5OSwbNmyqq1cRESklnA5rFevXs0DDzxAWloaHh4eWK1W+vXrR1paGrt376agoICtW7eSkZFBdnY2ABkZGcTFxVV58SIiIrWBy2F97NgxHnzwQQBat27NrFmzeOCBBzCZTHTo0IHs7Gx8fX2x2+2YzWZyc3Np1qwZvr6+VV68iIhIbeDp6gLNmzfn008/JSoqik8//RQAf39/APz8/CgsLMRisZCcnIzFYiE9PZ3Bgwczfvx4oqOj6dOnz023lZeX52p5lRYYFOK2tgBwuLc5e5mdw27cnu6ksavZNH41m1vHrxaPncthHRMTw4QJE0hISKBNmzZ4eXlhs9kICAjAZrMREhJCZGQks2bN4ssvvyQ8PJw1a9YwcuRIUlJSXArriIgIV8urtDMXC93WFgAm9zbn5enl1u3pThq7mk3jV7O5dfxu87HLysq67jSXD4Pn5ORgsViwWq0UFRXx0ksvkZmZicPhYO/evURFRTnnTUtLw2KxUFJSgtlspqSkpHI9EBERqcVcDuvGjRszbdo0LBYLDRs2ZNCgQaxdu5aYmBiio6MJDg4G4MCBAzRt2hR/f3969+5NUlISYWFhVd4BERGR253Lh8GDg4NJT0+/5rEfL9n6V5GRkURGRgLQrl07Vq1aVckSRUREajfdFEVERMTgFNYiIiIGp7AWERExOIW1iIiIwSmsRUREDE5hLSIiYnAKaxEREYNTWIuIiBicwlpERMTgFNYiIiIGp7AWERExOIW1iIiIwSmsRUREDE5hLSIiYnAKaxEREYNTWIuIiBicwlpERMTgFNYiIiIGp7AWERExOJfDuqSkhKFDhxIfH8/kyZO5cuUKSUlJWCwWFi5cCMDGjRuJjY1l+fLlAOTk5LBs2bKqrVxERKSWcDmsd+7cSbNmzUhLS6OgoICVK1fSr18/0tLS2L17NwUFBWzdupWMjAyys7MByMjIIC4ursqLFxERqQ1cDutmzZpRUVGBw+GgqKiITz75hAceeACTyUSHDh3Izs7G19cXu92O2WwmNzeXZs2a4evreyvqFxERue15urqA2Wxm+/btbNu2jdDQUDw8PPD39wfAz8+PwsJCLBYLycnJWCwW0tPTGTx4MOPHjyc6Opo+ffrcdFt5eXmulldpgUEhbmsLAId7m7OX2Tnsxu3pThq7mk3jV7O5dfxq8di5HNbLly/nueee4+mnn+a9997jww8/xGazERAQgM1mIyQkhMjISGbNmsWXX35JeHg4a9asYeTIkaSkpLgU1hEREa6WV2lnLha6rS0ATO5tzsvTy63b0500djWbxq9mc+v43eZjl5WVdd1pLh8G9/f3d+5JN2rUiFdeeYXMzEwcDgd79+4lKirKOW9aWhoWi4WSkhLMZjMlJSWVKF9ERKR2czmsExIS+Mtf/sKgQYPIzMzk6aefZu3atcTExBAdHU1wcDAABw4coGnTpvj7+9O7d2+SkpIICwur8g6IiIjc7lw+DN6wYUPnJVo/+vffASIjI4mMjASgXbt2rFq1qpIlioiI1G66KYqIiIjBKaxFREQMTmEtIiJicAprERERg1NYi4iIGJzCWkRExOAU1iIiIgansBYRETE4hbWIiIjBKaxFREQMTmEtIiJicAprERERg1NYi4iIGJzCWkRExOAU1iIiIgansBYRETE4hbWIiIjBKaxFREQMTmEtIiJicJ6VWeiDDz5g586dABw6dIjRo0ezevVqysrKGDBgAE899RSpqal89NFHvPDCC3Tv3p0NGzbg6elJ9+7dq7QDIiIit7tK7Vm/8MILWK1Wpk+fTqtWrcjKyiI5ORmr1UpaWhqlpaXk5OSQnp7O5s2bcTgcbNu2jW7dulV1/SIiIre9X3UYfO7cuYwYMYKDBw/Stm1bvL29adGiBYcPH8bT05OysjK8vLzYtGkT3bt3x2QyVVXdIiIitUalDoMDFBcXc+rUKdq1a0dFRYUziP38/LDZbPTu3Zvk5GRefPFFFi9eTGJiIuPGjePxxx+nU6dON9VGXl5eZctzWWBQiNvaAsDh3ubsZXYOu3F7upPGrmbT+NVsbh2/Wjx2lQ7rHTt20LlzZwA8PP5vB91msxEQEEB0dDSdOnVi06ZNdOvWjVWrVjFx4kTefvvtmw7riIiIypbnsjMXC93WFgBuPsjg5enl1u3pThq7mk3jV7O5dfxu87HLysq67rRKHwbfs2cPbdu2BaB58+bs27cPu93OwYMHCQ8PB8DhcLBp0yZ69uxJaWkpJpOJ4uLiyjYpIiJSK1V6zzo/P5+QkB8Of7z88suMHj2awsJCBg4ciLe3NwBbt26la9eumEwmunbtSmxsLH379q2SwkVERGqLSof1woULnT+HhoZitVp/Ms+/XqbVs2dPevbsWdnmREREai3dFEVERMTgFNYiIiIGp7AWERExOIW1iIiIwSmsRUREDE5hLSIiYnAKaxEREYNTWIuIiBicwlpERMTgFNYiIiIGp7AWERExOIW1iIiIwSmsRUREDE5hLSIiYnAKaxEREYNTWIuIiBicwlpERMTgFNYiIiIGp7AWERExOIW1iIiIwbkc1g6Hg0mTJjFw4ECSkpK4cuUKSUlJWCwWFi5cCMDGjRuJjY1l+fLlAOTk5LBs2bKqrVxERKSWcDmsd+zYga+vLytWrCA+Pp7ly5fTr18/0tLS2L17NwUFBWzdupWMjAyys7MByMjIIC4ursqLFxERqQ08XV1g7969eHl5MXjwYFq0aMHJkyfp378/JpOJDh06kJ2dja+vL3a7HbPZTG5uLs2aNcPX19fl4vLy8lxeprICg0Lc1hYADvc2Zy+zc9iN29OdNHY1m8avZnPr+NXisXM5rC9duoSXlxdLly5l/PjxbNu2jXfeeQcAPz8/CgsLsVgsJCcnY7FYSE9PZ/DgwYwfP57o6Gj69Olz021FRES4Wl6lnblY6La2ADC5tzkvTy+3bk930tjVbBq/ms2t43ebj11WVtZ1p7l8GDwwMJCOHTsC8NBDD+Hp6YnNZgPAZrMREBBAZGQks2bNIjAwkPDwcNasWcPIkSPZtWtXJbsgIiJSe7kc1m3atGHv3r0A5ObmMnToUDIzM3E4HOzdu5eoqCjnvGlpaVgsFkpKSjCbzZSUlFRd5SIiIrWEy2Hdo0cPrly5QlxcHKdOnWLgwIGsXbuWmJgYoqOjCQ4OBuDAgQM0bdoUf39/evfuTVJSEmFhYVXeARERkdudy59Ze3p6Mn369Gse+/GSrX8VGRlJZGQkAO3atWPVqlWVLFFERKR2001RREREDE5hLSIiYnAKaxEREYNTWIuIiBicwlpERMTgFNYiIiIGp7AWERExOIW1iIiIwSmsRUREDE5hLSIiYnAKaxEREYNTWIuIiBicwlpERMTgFNYiIiIGp7AWERExOIW1iIiIwSmsRUREDE5hLSIiYnAKaxEREYOrVFh369aNhIQEEhISyMzMJCkpCYvFwsKFCwHYuHEjsbGxLF++HICcnByWLVtWdVWLiIjUIi6H9ZkzZ2jXrh1WqxWr1cr+/fvp168faWlp7N69m4KCArZu3UpGRgbZ2dkAZGRkEBcXV+XFi4iI1Aaeri5w8OBBjhw5wsCBA4mMjOTUqVP07dsXk8lEhw4dyM7OxtfXF7vdjtlsJjc3l2bNmuHr6+tycXl5eS4vU1mBQSFuawsAh3ubs5fZOezG7elOGruaTeNXs7l1/Grx2JkcDodL3c/JyeHs2bN07dqVKVOmkJqaSlZWFgEBAaSmphIYGEiLFi1YsGABFouFv//97wwePBir1Up0dDR9+vS5qXaysrIq1SEREZGaqn379j/7uMt71i1atKBVq1YAdOrUiW+//RabzUZAQAA2m42QkBAiIyOZNWsWX375JeHh4axZs4aRI0eSkpJy02F9vYJFRERqG5c/s16+fDkZGRkAfP7557Rp04bMzEwcDgd79+4lKirKOW9aWhoWi4WSkhLMZjMlJSVVV7mIiEgt4XJYx8XF8fHHH5OQkMCFCxeIj49n7dq1xMTEEB0dTXBwMAAHDhygadOm+Pv707t3b5KSkggLC6vyDoiIiNzuXP7MWkRERNxLN0URERExOIW1iIiIwSmsRUREDE5hLSIiYnAuX2ctN2/AgAE89thjPP/889VditzAmjVrmD9/vvNKhrvuuotp06bh6amXR03xr2Not9v5zW9+Q3JyMh4eHj+Zz2azMWjQoGqqVH60Z88e3njjDZo0aeJ8bOzYsbRs2bIaqzIu7VnfIvn5+dx9991s3769ukuRm5CYmOi83329evX45z//Wd0liYt+HMP09HS8vb3505/+VN0lyS/o1auX83VntVoV1DegXYdbZN26dXTr1o0dO3bw5Zdfsm3bNvbv30+dOnUYN24cY8aMoaioiFatWjF27Fiys7OZOXMmpaWlNG3alOnTp1d3F2olh8NBYWEhOTk5zJkzB5PJRJ8+fWjbti1//etfefvttxkyZAgJCQl07NiRV199lT/+8Y+MGjWKCxcucOeddzJ9+nTmz5/vHO/333+/urtV6wwYMIDx48dz+vRp9uzZg7e3N1OnTnVOP3/+PG+++SZFRUXY7XbmzZvH0KFDWbx4Mf7+/iQlJTF79mz8/f2rsRe1z+zZs/n22285ffo0gYGBzJkzh2PHjjF+/Hjsdjtdu3bl+eef59lnn8Xf35/IyEhCQkJITU0lNDSU48ePk5KSwpIlS0hJSeHEiRPMnTv3tvh7qrC+RbZt28bixYvx8/Nj7dq11K1bl0ceeYTBgwczZcoUnnnmGTp16sTkyZP57LPPOHnyJDNnzqRhw4ZYLBauXr1KQEBAdXej1khNTWXjxo2YTCbat2/PJ598wvz586lfvz4JCQk88cQTHDp0CLvdzqVLl/j888/x8vLit7/9LRkZGTz88MPExcWxdOlS1q1bB+Acb3G/Bg0asHv3burUqcPKlSs5cOAAc+bMcd7GOD8/n+eee47o6GjeffddsrKy6Nq1Kzt27KB9+/bUq1dPQe0G69atIzc31/l7hw4daN68OSkpKSQlJfHNN98wY8YMJk6cyD333MOwYcM4efIkdrudl19+mcjISPr37096ejpnzpzBYrEQERHBN998Q2lpKRs2bOA///M/q7GHVUdhfQscOXKEb7/9luHDh1NRUcHx48d58sknady4MQDHjh0jNzeXBQsWUFhYSNu2bbnjjjuYNGkS/v7+nDt3joqKimruRe2SmJh4zeeYO3fupGHDhgC0bNmS/Px8goKC2LlzJ7/73e/IzMykvLycp59+mtTUVHJycvjoo48oKSmhR48eAM7xFvf77rvvKCoq4osvviAhIQGAevXqOac3aNCAP/7xj6xevZoTJ07wm9/8hl69ejFjxgy+//57nnjiieoqvVbp1asXo0aNcv4+e/Zs7r33XgCCgoIoKSnhxIkTjBs3DoDLly9z6tQpAJo0acL3339PUFAQ3t7eNG7c2Pma7dSpE7t27WL37t0MGTLEzb26NRTWt8C6desYNWoUTz75JABvvfUWn3zyCffffz/ww5Psqaeeom3btqxfv55mzZqRnJyM1WrFbDbTq1cvdGO56mU2mzl//jz169fnwIEDDB06lE6dOjF//nzGjRvH/v37OXToEPfeey9NmjThwQcfpFevXuzatQsvLy927dr1k5ObxH3S0tIYPnw4Z8+eZeLEiZw+fZpPPvnEOX3ZsmX07t2brl27Mnz4cBwOB2FhYVy+fJl//vOfzJo1qxqrr91MJtM1v4eFhTFhwgSCg4NJT0+nadOmzvnq1atHQUEBdrudc+fOcf78eQDnG6/Q0FC8vLzc3odbQWF9C2zevJkVK1Y4f+/duzeFhYXO34cOHcpbb73F1atXadSoETNmzKBHjx7Ex8cTGBhIUFAQ586du2ZPQNzr97//PS+99BJlZWXExMQQHBzMww8/zOTJk4mIiKB169bk5+cDP9wvf/To0axYsQIvLy9mzpzJrl27qrkHtc+PH2U4HA6ioqJ45ZVXmDFjBoMGDaKoqIixY8dy7NgxAB599FGmTJnCwoUL8ff35+zZs87Hc3Nz8fHxqc6u1Br/fhjcZrPRunXra+YZOXIkycnJFBcX06JFC2JjY53TPD09SUxMJD4+nsaNGzvH7d577+X06dPOoyq3A90bXETk/1u6dCn/8R//QZcuXaq7FLlJqampJCYmcurUKYYNG8aaNWtwOBw8//zzLFiwALPZXN0lVgntWYuIAHPmzCE7O5vExMTqLkVccPXqVfr374/JZGLkyJFUVFQQExNDTEzMbRPUoD1rERERw9MZMCIiIgansBYRETE4hbWIiIjBKaxFREQMTmEtIiJicP8L8AF+8tfvSbMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = multiplier_df.plot.bar(\n",
    "    rot=0,\n",
    "    #  ylabel=\"Relative resource utilization\",\n",
    "    color=my_color_palette,\n",
    "    figsize=(8, 3),\n",
    "    #  title=\"Multiplier component\"\n",
    ")\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax.set_ylim(0.50, 1.1)\n",
    "# plt.legend(loc = 'center right',bbox_to_anchor=(1.2, 0.5))\n",
    "plt.legend([\"Sign-magnitude decoding\", \"2's complement decoding\"],\n",
    "           loc='upper center',\n",
    "           ncol=2,\n",
    "           bbox_to_anchor=(0.5, 1.15)\n",
    "           )\n",
    "plt.grid(False, axis=\"x\")\n",
    "plt.savefig(\"figs/multiplier_cmp.pdf\", bbox_inches=\"tight\")\n",
    "plt.savefig(\"figs/multiplier_cmp.png\", bbox_inches=\"tight\", dpi=200)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "848437809242ddc122b190cc2a4e0c1302f0f28d1b32fbad0be8f01034358338"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}