paper_note/lynx/subgraph_write.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.rcParams[\"font.size\"] = 16\n",
    "\n",
    "g_label_fontsize = 16\n",
    "\n",
    "colors = [\n",
    "    \"#233142\",\n",
    "    \"#F95959\",\n",
    "]\n",
    "\n",
    "edgecolors = [\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "]\n",
    "\n",
    "hatches = [\"\", \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 7 / 2), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"ModelC\\n2DP$\\\\times$4EP\",\n",
    "    \"ModelC\\n1DP$\\\\times$8EP\",\n",
    "    \"ModelC\\n2DP$\\\\times$8EP\",\n",
    "    \"ModelD\\n16DP$\\\\times$8EP\",\n",
    "    \"ModelD\\n8DP$\\\\times$8EP\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time\n",
    "data_a = {\n",
    "    labels_name_a[0]: [\n",
    "        random.uniform(190, 250) * 10,\n",
    "        random.uniform(190, 250),\n",
    "    ],  # modela 8DP\n",
    "    labels_name_a[1]: [\n",
    "        random.uniform(190, 250) * 10,\n",
    "        random.uniform(190, 250),\n",
    "    ],  # modela 8TP\n",
    "    labels_name_a[2]: [\n",
    "        random.uniform(190, 250) * 10,\n",
    "        random.uniform(190, 250),\n",
    "    ],  # modela 2DP 4TP\n",
    "    labels_name_a[3]: [\n",
    "        random.uniform(190, 250) * 10,\n",
    "        random.uniform(190, 250),\n",
    "    ],  # modela 2DP 8TP\n",
    "    labels_name_a[4]: [\n",
    "        random.uniform(190, 250) * 10,\n",
    "        random.uniform(190, 250),\n",
    "    ],  # modela 4DP 2TP\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"DLRover-Lynx without Graph Rewrite\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_width = 0.2\n",
    "group_spaing = 0.15\n",
    "\n",
    "group_positions = {}\n",
    "current_pos = 0\n",
    "\n",
    "x_label_cnt = len(data_a.keys())\n",
    "x_label_group_cnt = 2\n",
    "\n",
    "for idx in range(x_label_cnt):\n",
    "    group_positions[idx] = []\n",
    "    for _ in range(x_label_group_cnt):\n",
    "        group_positions[idx].append(current_pos)\n",
    "        current_pos += bar_width\n",
    "    current_pos += group_spaing\n",
    "\n",
    "group_centers = {}\n",
    "for idx, positions in group_positions.items():\n",
    "    group_centers[idx] = sum(positions) / len(positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(13.333333333333346, 0.5, 'Time (s)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_set = set()\n",
    "for idx, (x_label, y_data) in enumerate(data_a.items()):\n",
    "    positions = group_positions[idx]\n",
    "\n",
    "    for i, (pos, value, color, edgecolor, hatch, label) in enumerate(\n",
    "        zip(\n",
    "            positions,\n",
    "            y_data,\n",
    "            colors,\n",
    "            edgecolors,\n",
    "            hatches,\n",
    "            legend_labels,\n",
    "        )\n",
    "    ):\n",
    "        if label in label_set:\n",
    "            local_label = None\n",
    "        else:\n",
    "            local_label = label\n",
    "            label_set.add(local_label)\n",
    "\n",
    "        ax.bar(\n",
    "            pos,\n",
    "            value,\n",
    "            width=bar_width,\n",
    "            color=color,\n",
    "            label=local_label,\n",
    "            hatch=hatch,\n",
    "            edgecolor=edgecolor,\n",
    "        )\n",
    "\n",
    "ax.set_xticks(list(group_centers.values()))\n",
    "ax.set_xticklabels(list(data_a.keys()))\n",
    "\n",
    "ax.set_ylim(0, 3100)\n",
    "ax.set_yticks([0, 1500, 3000])\n",
    "ax.set_yticklabels([\"0\", \"1500\", \"3000\"], rotation=90, ha=\"center\", va=\"center\")\n",
    "\n",
    "ax.tick_params(axis=\"x\", bottom=False, labelsize=g_label_fontsize - 4, pad=1)\n",
    "ax.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
    "ax.set_ylabel(\"Time (s)\", fontsize=g_label_fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6553966f30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(\n",
    "    ncol=3,\n",
    "\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    bbox_to_anchor=(0.5, 1.12),\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s2TJ9+eWXbtkWUreJj+gT3wCQUqXk/Cm50/vIncJTyJ0CSYPCCgBJIigoKNHazpQpk5YuXWrZ7W5ERIRatmyptWvXxnsb6dKli3X7yYG7fsdeXl6aPHmyAgICTOfv3r2bBB2SnNV3MLl8/4DUJraxOePi7+/vxkjgTrNnz042xQR//PGHunbt6jBGcYsWLRLlhrGPj4/mz58fY9iJ5PK7eNSlpn2GO695ihQpEutwKEOHDnXo6QV40NGjRxP8gAEAIGml5PwpudP/IXcKTyB3CiQNCisAJInEHgPYy8tLEydOVNeuXU3nh4aGqmnTpjpx4kS82o8t/uSSDHbn7/ixxx5Tt27dLOdbjZkIJBarz3dy+f4Bqclff/2ldevWxXt9vpfJU1RUlIYPH+7pMCRJJ06cUKtWrRQRERFjet68eTV58uRE2266dOk0adIk+88UViQPqWmf4e5rnp49eyo4ONh0XmhoqMaPH+/W7SF1+fLLLxkyBgBSmJScPyV3GhO5UyQ1cqdA0qCwAkCSSIqxk202myZMmKDnn3/edP7NmzfVsWPHeCWXvLysd5c+Pj4ut5cY3P07btKkieW848ePa//+/W7dHhAbq+9gcvn+AalFZGSk3njjjQS1kRTHfLhu/PjxyWKs38jISLVo0cK0qOGzzz5L9KFkatSooZdeekkShRXJRWraZ7j7vaRJk8ZhCJsH/fbbb27dHlKPnTt3aurUqZ4OAwDgopScPyV3GhO5UyQ1cqdA0qCwAkCq4uvrq5kzZ1om5devX69vvvkmiaNKmapUqRLr/G3btiVRJACApPL555/rr7/+SlAb3t7ebooG7nLo0CG9/fbbng5DkjR27FjTc4jy5curTZs2SRLD+++/L5vNpjt37uju3btJsk1YY58Ru9jOyXfs2EGPBHAQFhamDh066N69e54OBQCQTJE/dQ9ypwDw6KGwAkCqkydPHn3yySeW8z/99FPdvn07CSNKmQICApQ+fXrL+YzpDACpy+bNmzV06FBPhwE3u3v3rtq1a6eQkBBPh6Lz589r0KBBpvM+++yzJOu5oFSpUqpXr54kzmeQ/OXIkcNyXkREhK5du5aE0SAleOutt7R3715PhwEASObInyYcuVMAePRQWAEgVerRo4fy589vOu/y5csaM2ZM0gaUQgUGBlrOi4yMTMJIAACJad++fWrYsCFPt6YyERERatWqlf7++29PhyLpfk8RN2/edJieL18+1apVK0lj6dKliySGA0HyF9v5uMQ5OWL6/PPPNXbsWE+HAQBIIcifJhy5UwB4tFBYASBV8vPzU58+fSznjxo1ShEREUkYUcoTFRUV682GbNmyJWE0AIDE8t9//6l27dq6evWqp0OBG927d08dO3bUr7/+6ulQJElnz57VzJkzTee1bds2yXqriNaoUSM9++yznA8i2Tt//rzlPC8vL2XNmjUJo0FyNnbsWL333nueDgMAkIKQP00YcqcA8OihsAJAqtWpUyf5+vqazrt06ZLWrFmTxBGlLEeOHIm1srpUqVJu3+aePXs0aNAg1a5dW7lz51ZgYKD8/f0VHBysp59+Wq+88ooWLlyosLAwl9o9cuSIDhw44PIrrie3L1++7FQ7rna/vm3bNn3wwQeqXr26cufOrYCAAAUEBChfvnyqUqWKPvzwQ23ZssWlNh92/PhxffzxxypUqJBsNpv++OOPGPMNw9Ds2bP1/PPPK126dEqbNq2qVaum33//PUHbTWny588vm83m8isux48fd6qdzp07x9rOH3/8odatW8vf31/VqlWzXO73339X+/btlT9/fvn7+ytDhgx6+umnNWzYMF2/ft21XwqSraioKP36669q0KCBvL294/z8SNKKFStUpUoVnTlzxnT+yZMntXHjRtOXu7o1/ffff/Xmm2+qdOnSCgoKUkBAgAoXLqyePXvq33//dcs2HnTkyBF99tlnatSokfLnz6+0adPK19dXmTNnVvny5dW5c2f9+OOPunXrltu3nVQuXLigunXravbs2abzw8LCLP+ue/bsSZSYRo8ebXlcbdu2baJsMzb+/v7asGGDKlWq5PK6iX0MDQsL09y5c/Xqq6/qqaeeUu7cue2f04wZM6pIkSJq3Lixhg8froMHD7oc/8P27dunHj16KCAgwHK/cebMGQ0ePFhPPfWUMmTIoMDAQBUoUEANGjTQ1KlTdePGjQTH4Yxbt25p7NixqlmzpoKDg+Xn56fs2bOrfv36mjt3rgzDSJI4klJsf+PixYvLy8u9aZ0DBw7ok08+UZ06dZQ/f377+Xju3Ln15JNPqn///lqzZk2q/F0768KFCxo1apTKli0rm82m6dOnOyyzfPly1a9fX5kyZVJAQICeeuopzZkzx6n2r1y5ohEjRljuYx527949vfHGG3rttdcsl9m2bZvlft9VSXGtAgBIOuRP44/cKblTcqfkTvHo8fF0AACQWLJmzaoqVapYJqJ++ukn1a5dO2mDSkEWLVpkOS84OFjly5d327aWLVumIUOGaOvWrabzL1++rMuXL2vr1q2aNGmSsmTJon79+ql///4KCAiIs/0XXnhBJ06ccDmuY8eOWXaJKN1/Kmzo0KFxtrN27dpYT9yirVq1SgMHDrTssv3kyZM6efKkNm3apE8++UTVq1fXl19+qYoVK8bZtnT/ZsT8+fM1Y8YMrV+/3jIhfvXqVbVu3drhQmDdunVav369pk+fro4dOzq1zZRu/fr1WrBggb766ivLG8+S5O3trWHDhqldu3bKmTNnnO3mz59fd+/e1YkTJ/T+++9rwYIFMeZXrVpVQ4cO1dNPP+2w7rVr1zRjxgxNnDhRBw4ciHU7u3btUu/evbVp06YY08PDw7V161Zt3bpVX3/9tebNm6fnn38+zpjd8T0aMmSIU9+bTp06md6oSIjvv/9eXbt2dWmdI0eOKF++fPafX3jhBa1bt87lbU+dOtXye+NMm506ddKUKVNM5124cEGTJ0/WpEmTdPLkSafiOXHihIYOHarp06fHenNs2rRpmjZtmuU8Z4o3rFy7dk39+vXTzJkzHWI4dOiQDh06pClTpuijjz5yyxO4mzdv1uDBgy2THNeuXdO1a9f0zz//aMaMGUqbNq26d++uDz/8UJkyZYq17b179zqdNIvtmOBKO2Z/t/DwcE2ZMkUffvihrly5Yrnu+fPnVbVqVdN5zz//fJw38VwVGRmpqVOnms4LDg5WyZIl3bq9xJAUx9CIiAgNHz5cI0eO1OXLl02XuXHjhm7cuKGDBw9qyZIleuedd1S/fn2NHj1aBQoUcOk9rVixQqNGjdKKFSssl4mKitKnn36qYcOGKTQ0NMa8o0eP6ujRo/rtt9/07rvv6quvvlKHDh1cisEVU6dO1VtvvaVr167FmH7x4kUtW7ZMy5Yt07hx47Rw4UJlyZIl0eJIarGdk9epU8dt29m2bZvee+89rVq1ynT+mTNndObMGW3btk0jR45UuXLl9MUXX8Q6jI+Pj2spp0GDBmnQoEH2n+Nz3Jbu78dWr15tOs/ZNh9+OjcsLEy//PKLZsyYoRUrVlg+vXv37l11795dP/74Y4zpf//9t9q0aaNDhw7pww8/NF138+bNGj9+vH766Senb4YsW7ZMb7/9tvbu3Rvrci1btrSc52yRTGJfqwAAPIP8afyROyV3Su6U3OnDMaf03CniRo8VAFK1unXrWs5btmxZEkaSsoSHh+u7776znN+7d2+3dNl95coVNW/eXPXr17dfGJQtW1Zffvmlli9frqVLl+rbb79V7dq1YzyNd+XKFX344YcqWbKkU9XHO3bs0NKlS9WgQYM4l3377bd15swZGYYR64WBdP8kJywsTAcPHlTjxo1jzMuaNaumTZumM2fOxHnSFRoaqm7duqlWrVr6+++/lTNnTr311luaP3++1qxZox9++EHt27eXn59fjPXWrl2rypUra8KECZZtR0VF6ffff1eHDh2UI0cOde3aVevWrbO8MLh8+bKqVq1qeePRMAz169cvzor01CJv3rx64403tHPnThUpUsRyuR49eujtt99Wrly5nP5upEmTRkWKFNFnn30WY3rVqlW1atUqVa9ePcZYnVu2bFGXLl2UK1cuvfHGG3FeGHz77bd66qmnHC4MHnbp0iXVr19fu3btinW5vXv3avfu3Ro6dGicN6y8vLw0adIkXb161eF79OGHH+q///4zvRmUPn16DR48WAcOHNCkSZNi3UZ8NGnSRDNmzFCVKlUUGRlp+UqbNq1GjRqlbdu2KXfu3DHamDRpktasWaNPP/1UadOmjbWd+vXr6/vvv9eff/6pRo0aWcY1ceJErVixQj169HBoIzAwUB999JE++OADh/X++OMPvfTSS8qTJ48++OADp4sqJGnw4MGaNm2ax5443rdvnypWrKjvv/8+1hgiIiI0cOBADR8+PN7bCgkJUa9evfTMM8/Y922FChXS0KFDtXTpUi1fvlyTJk1Ss2bNYjypdfv2bY0aNUpFihTRb7/9Fus2SpYsqfDwcF24cEErV65U9erV4xVryZIldffuXZ08eVILFiwwTRDEZtOmTerdu3esRRWesGHDBstCAXcmG90tKY+h58+fV+XKlTVw4ED77yo4OFgffPCBlixZolWrVmnGjBlq3bp1jHMiwzC0dOlSVaxYUdu2bYvzPYWGhmry5MkqWbKk6tatG2tRRWhoqBo1aqRBgwY5FFU87NKlS+rYsaN69erl9v1KWFiYOnXqpG7dujkUVTxs/fr1qlWrlstP6CVXa9eu1b59+0zneXt7q2fPngneRlRUlAYOHKhKlSpp1apVypQpk3r37q05c+Zo9erVmjt3rl555RWlTZs2xnr//POPateurQ8//NDyb75mzRq98sor8vX1jfV42bFjR61atcqhK/JGjRpp06ZN+v7771WhQoVY28ibN6++/PJLrVq1ShMnTrR8v40aNdLGjRs1duxYZcqUyaGdZs2aafny5fblN2/erJ49eypHjhxq1aqVli5dGmtRRb169RyKKh40dOhQXbhwwf7z7du3NXHiRJUtW1bPPPOMZs6c6dLnt379+nEWVSRUYl6rAACSB/KnriN3Su6U3Ol95E7/JzXkThE3eqwAkKrFVo169uxZXbp0ScHBwUkYUcrwxRdf6NChQ6bzsmXLpn79+iV4G8eOHVONGjV0/PhxSfdPCiZMmKA2bdo4LNu7d2/t2LFDbdu21X///WeffvToUT3//POaNWuWmjVrZrmtzJkzq379+qpfv76+/vprvfHGG6bLVahQQV988YVL78PPz0+FChXS6NGj9csvv0i6/3TekiVLnOpe/Pr166pTp462bt0qm82md955R4MGDXKoJm/Xrp0GDx6sZs2axeimPSIiQr169dKlS5ccnn6LiopSoUKFdPToUafeS3h4uBo1amSZwI927do13bhx45Ea0ztr1qyaP3++KlasaJrsLl68eLzbfnC4AR8fH82YMSPGheCqVas0YMAA7dy506n2DMPQG2+8odGjRzsdQ0hIiLp166atW7dadimeNm1alSpVSqVKldLLL7+s2rVrWw7TUKZMGXXv3t10nre3twoXLqxPPvkkxg29xx57TL///rtKlCjhdNyuypAhg9q1a6d27dppxIgRGjBggOly7733nmV32gUKFFCBAgX03HPPqW7duqpSpYru3LnjsFz9+vXt+4S4FCxYUAULFtQLL7ygAwcOaO3atZKk7Nmz688//3R4Cv3777/X559/rv379zvVvpnp06c7VLWbVdYPHjxYQ4YMifd2zGzZskV169Z1qSvFgQMHqnHjxrFepJu5cuWKateurR07dki6v88ePny4Xn31VXl7e8dYtnv37jpy5IjatWsXI/F06dIlNWzYUN98802s4w/7+voqW7ZsqlWrlmrUqBHv3k3SpEmjPHnyKE+ePKpTp46KFCkS61MfD6pWrZpD8sfsSYd8+fLZj79JYfHixZbzKlSokGRxuCIpj6GXLl1StWrVYpzjlC1bVitWrHAYl7ljx456/fXX1aBBA129etU+/fr162revLn27dunoKAgh+1euHBB48aN0/jx42MdBzpaZGSkWrduHWdR0cOiE5bjx493aT0rISEhaty4sWXvA2b++ecfffrpp/roo4/cEoOnhIWF6dVXX7Wc37VrVxUsWDDB22jZsqWWLFkiSerSpYtGjhypjBkzxliuVatWGjp0qFq3bu3wNOsnn3yic+fOafLkyQ7tP/fcc3ruuef06quvqnr16qZDSBUrVsyyR5tMmTLpqaee0lNPPaWWLVuqTp069mPkg/z9/bV+/XqHYkirNitVqqRKlSopMDBQnTp1ss8bM2ZMjP18tWrVXNqPt2/fPs7lIyMjderUKQUGBurdd9/VzJkzEzT01MP7/D/++MO0uC+up0mtJOa1CgAg+SB/6jpyp84jd/poIHeaOnKniBs9VgBI1cqWLRvrfGcPto+SOXPmWN5E8/Ly0g8//OCQbHXVmTNn9Nxzz9kvDPz9/bV8+XLTC4No5cuX119//eXQXXhYWJheeuklpyvo+/Xrp3bt2pnOe+KJJ5x7AybOnz9v//+rr77q1IXBvXv3VK9ePXvF+dSpU/XZZ59ZdtFXsGBB/fnnn6ZtDx482J4Uj+bl5aWJEyfq5MmTunv3rlavXh1rd/YDBgzQX3/9pQ8//FDXr1/XvHnz9NhjjzksV7Ro0UfqwiBayZIlLb8bcV1QxebBJ4ybNGmixx9/PMZ8f39/lS5dWpMmTdLEiRPjvLHcp08fjR49Wj4+Pmrfvr2WLVumS5cuKSwsTIcPH9bQoUMdKvglafv27fr111+dijlnzpxavny56edDut/1YlxPWz7YfWWGDBm0cuXKJL0weOutt9S0aVPTeVbv62FlypTRwIEDTec504WlmQdv9s+ePdu0a//z58/rqaee0qhRozRo0KA4h6lITjZv3qxatWrp+vXreuaZZzRt2jQdPnxYd+/e1ZEjR/T555+bHmMiIiI0bNgwl7Z169Yt1ahRw15UYbPZNHv2bPXt29ehqCJagQIFtH79etWoUSPGdMMw9Nprr8X6RNKDvL29Yx3r3llBQUGxJr9Sig0bNljOS64JgaQ8hnbv3j1G8tPLy0vz5s1zKKqIVqlSJdMnrk6ePGn5JNYXX3yhqVOnqly5ck4NvTJ06NBYC2JiM2HCBM2fPz9e6z4oJCRE9erV0+rVq5UjRw6999572r59u27cuKGLFy9qyZIlevLJJ03X/frrr3Xz5s0Ex+ApEREReumllywL6IoWLapRo0YleDvt2rWznz8OGTJEU6dOtTzPz549u1auXGl67JwyZYq+/fZby+0UL17c8mmqHDlyOBWrr6+vpk6dajoGfIECBZwqqnjYg8eCTp06ORTPffzxxzp8+LBCQ0P1999/x1qYMGrUKC1cuFAvv/yyrly5ot9//12FCxd2WC5LliwqXry4goKCdPjwYfXu3VtjxoyJ9TrIUxL7WgUAkHyQP3UNudP4IXea+pE7/Z/UkDuFOQorAKRqWbJkMX1qL1pc3UE9SqKiovT111+rQ4cOioqKcpjv4+OjKVOmxDqOsjOiE8WnT5+2Txs0aJAqV64c57oZM2bUsmXLlCFDBoc2W7VqpWPHjjkVw7Bhw0zHfHa2OtlM9BOdvr6+euedd5xa5/3339dff/0lSXrzzTfVuXPnONdJly6dpk+frjRp0sSYbhiGunTpotu3b8eYXrNmTeXJk0dp0qRRjRo11L9/f9N2161bpzFjxmjQoEH66KOPlCFDBrVo0UK7du1Sq1at7F205c+fX7Nnz3bq/aVG/fv3N306dPHixabfG2c8ePOpR48eDvOrVq2qGTNmqHv37urRo0esN6uGDBmicePGqUqVKtq7d69mzpypunXrKmvWrPLz81OBAgU0aNAg/fDDD6brO3vTWJJy586tuXPnmt6gvnLlSqxV37dv37Z34+fl5aXZs2c7daPP3YYNG2ba/aArF3uvvvpqjG4Hoz2YMHBWZGSk/vnnH0lSrVq1LIeTePvttzV9+nT169dPQ4cOdduT4Ylt9+7dql+/vnx9fbVw4UL9+eef6ty5swoUKKA0adLoiSee0DvvvKOVK1eaHrsXLFhg2juIle7du2v37t32n19++WWnihT8/Pz0888/O1yoS1KvXr3s++24mN1Qiw93teMpERERlk9oSPefjEqukuIY+ueffzoUMJQpUybOnghatmypfPnyOUy3Guf5008/1alTp7RixQrt2bMn1uF1/vzzT3366aeSpHLlymn8+PE6dOiQ7t69qzNnzuinn35S1apVY42vZ8+e8doPRgsPD1ezZs20fv169enTR4cPH9awYcNUvnx5pU+fXsHBwWrYsKHWr1+v5557zmH9W7duaeHChfHeviedP39eTZo0sSxsKVSokH777bdYr3GcMW7cOPs4xc2aNdPgwYPjXMfX11ffffedaZL4zTffjHVIqiZNmpgOb7R//36nh4/Jnz+/WrZs6TD94sWL8RqCJjpB6+vrazqGcdWqVVWgQAH5+/urYsWK+uSTT0zbOXjwoAYOHKhOnTrpu+++U+bMmVWzZk3t2LFDvXr1sl93BAcHa86cOQoMDJSXl5dWrFihzz//XH369NGsWbNi7YbdE5LiWgUAkDyQP3UOuVNyp+RO40bu9H9SQ+4UjiisAJDq5cqVy3JeXOM0PwqioqK0YsUKPffcc3rjjTdMxwzOmTOnlixZ4tTJa1xGjBihP//80/5zcHCwZfdyZnLnzm1a+Xr79m29/PLLTiVV8+bNa5qU3bFjR4yLFlfMmTNHktS8eXOnnnZfs2aNRowYIen+BawzyexoRYoUUevWrR2mX7lyJc4brE899ZTp9M8++0w1atRw+N0GBwdr7ty5On36tHbv3q2DBw/G+SRDaubr62vajfHZs2fj9WTv8ePHtWrVKkn3L7ycufguWbKk6ROTO3bs0NChQ9WnTx/98ccfsVZnt2zZ0vTmxtq1a126yKlatapljw1DhgzRkSNHTOd98MEH9u/akCFDVK9ePae36U7FihUz/Z07W30u3a8Yf/HFFx2mL1y40OWbPJs2bdKVK1ckyaVuQ5s0aWLZDWFy0rZtW2XNmlXbtm0z/Z1Fe/LJJ9WzZ0+H6Xfu3NHmzZud2tacOXM0d+5c+89+fn4uDWmSPn16jRw50mF6ZGSkunbtGudTBZISfNPT3e14yqFDhxQaGmo5P6FPciWlxDiGPvg5jZY+fXqn4jHbd1oVhj38RFe/fv2UNm1ah+Xu3bunV155RYZhaOjQodq2bZt69uypggULKk2aNMqZM6datmypdevWacSIEZZj4165ckVffvmlU+/DzDfffKPVq1dr6tSpGjNmjOX3IE2aNBo7dqxpHNHH15Ti2rVrGj16tEqUKGE5BEudOnW0ceNG08IvV+zfv9+eMPb19dVXX33l9LpZs2Y1HRYpPDw8zm6hX3/9dYdp58+f1/bt253efpcuXRymXb582WGIEmdEP7HYtGlT00Klh1ntA0aOHKknnnjCoceYoKAgjRs3TufOndM///yjkydPqmbNmpbtx3ZsTGqeulYBAHgO+VNr5E7/h9wpudO4kDuNKaXnTuEo+WdgASCB0qVLZznvxo0bSRiJZ0VFRenGjRs6deqUtm/frilTpqh37956/PHHVbdu3Rgn7NHSpUun/v37a//+/W55gur8+fMOXbl36NDBsvs2K3369DHt9mrNmjWaPn26U22YjVltGIa+//57l2KR7p9QRY+raHYz0MzgwYPtFzKtW7d2+iZKNLMTO0mxdsMsWT8ZbLPZNGnSJMsbJDlz5lSpUqVMu19+1LRp00Z58uRxmB7b079WpkyZYv8cvPzyy5a//4eZJf9v3bql9957T2PGjDF9quBhZk/u37lzx+mnF6INGjRIFSpUcJh+9+5dvfLKKw7T161bp2+++UaSVKNGDb3//vsubc/dzG7Q/PvvvzF6OoiLWeLk+PHjWr16tUuxRFfD58yZU3Xq1HF6vTRp0ih79uwubcsTcufO7fQNQbO/iyTt3bs3znXv3r3r8PRLw4YNlTNnTucC/X9NmzZV7dq1Habv37/fqWFJ3FXs4ux+IbmKq9eCh5+kSqhu3brJx8cnQa+PPvrItO3EOIaaJVGc/ZsXK1bMYdrVq1d17969ONf18fGx7PHiwIEDmjJligYNGmT5ObbZbOrfv7890Wlm6tSp8X46PTQ0VLNnz7bcFzyoVKlSpschZ/YXnhAeHq7Lly/ryJEjWr16tb788ks1b95cOXPmVL9+/XT16lWHdYoWLaq5c+dq+fLllkPEuGLYsGH2gqfatWvHOsSFGavz0KlTp+ru3buW67344oumxVSzZs1yets1atQwPQ+bMmWK021I94emiv7+de3a1al1rPYBYWFhmjRpksNTkdGyZs2qsmXLWs6P5urfITF56loFAOA55E/JnT6M3GlM5E6dR+40ppSeO0VMFFYASPViO/FMyWMvO2vGjBmy2Wzy9vZWxowZlTdvXlWsWFEvv/yyxo8f79Blr5+fn6pVq6axY8fqzJkzGjFihMsnrlZGjBihW7duxZjWpEkTl9vx8fGxrNT+8ssvnaq8rlKlisqUKeMwfdKkSS53SzZx4kRJ929wPP/883Euv2PHDm3cuNH+c4MGDVzaniTLG4QnTpzQ4cOHLdez+j68/vrrCRon8VHi6+urN99802H65s2btW7dOqfbiYyM1NSpUyXd/0w7m9SXzJ9mrlixolM3e6NZXWC6+uSBj4+Ppk+fbjr24OrVq2N0nXfz5k116tRJhmEoa9as+uGHHzze00LTpk1Nb/K4kiioUaOG6djurtzkCQ0NtT+53rFjR9NuAmMT2xigycVPP/3kdAFIsWLFTJMRp06dinPdKVOmOBzb4nOske4Pu2JmzJgxLg1L8iiLKwnr7qTTkCFDtHnzZi1atEgDBw5U5syZFRkZGeerdevWWrhwof7++2/LRF9iHEPN9ufBwcFOrWv1VKGziW+zbd+9e1dTp051qqBBkt544w1Vq1bNMo4ff/zRqXYe1rdvX7Vo0cLp5UuXLu0wzZn9RVKqXr26bDab/P39FRwcrIIFC6pmzZp65513tHDhQoeeXbJly6b27dvr999/1/79+9WqVSu3xHHu3Dn99NNP9p/deR4aGhpqesMhWpo0afTSSy85TJ81a5YiIyOd2raXl5c6dOjgMH3BggW6fv26U21Ist9QyJUrl9PddVvtA1566SVVqVLF6W1bSS7Hck9eqwAAPOdRzp+SOzVH7jQmcqfOI3caU0rPnSIm/hoAUr3YTvScqUx8lBQrVkxXrlzR2rVr9eqrr8Zare6qO3fuONxk9Pb2VsWKFePVXosWLUyf+jpw4ICWLVvmVBu9evVymHbixAmXhgG4dOmSfv75Z0kyrTA1M27cuBg/x2d8tNi6hl+/fr3lPKsbWMltTOfkrnv37qYV7NFj3zlj6dKlOnv2rKT7T9M70w1iNLOLPFeHC7B6KjJ6KApXlCxZ0nKYhbfeest+k++1117TiRMnJEnTpk1z6T0nFnfd5Gnfvr3D9J9//tn0yWMzP//8s/335OwNzQe5+vSKJ7iSaPLy8lKOHDkcpsf1+TQMQ2PGjHGYXqlSJae3/aDq1aub3ry+du2aZsyYEa82HzVx9VjwcNIwofLkyaMnn3xSDRs21EcffaTdu3fHmfwaOXKkfvjhBzVu3FjlypWz7BEgMY6h9evXd5j2wgsvOLWu1X4/PDzcqfXN3k+rVq3UsWNHp9aX7j+19d1331kWg7nac080VxPTVt9TV4dkSi4GDRqk8+fPa+bMmbEOHREfU6ZMifEZScrzUMm8l6cLFy5o5cqVTm/f7DMaGhrqdM8X8S1mTOzz6ORyLPfktQoAwHPInzqH3Cm5UziH3GlMKTl3ipgorACQ6sXWHW1gYGASRuIZwcHB2rhxozZs2GB/md1ElO53bx6f7tycsXDhQoenyPLmzRvvv0GGDBnUuHFj03lm45Wbad++vWni3pWxf6dPn67w8HAFBASoU6dOTq3z8E2GvHnzymazufSKLcl+5swZy3muPgUPc0FBQabji69YsUI7d+50qo3vvvvO/v8ePXq4tH13POGdJUsW0+kPPzHrrLfffltPPvmkw/QLFy5o4MCBmj9/vn3/8tprr6lhw4bx2k5iMLvJc+7cOfsYjs6Ivuh5UFhYmNNPa0ffpK9SpYoKFy7s9Haj+fv7u7xOcmc2RERcT0pt3LhRBw8ejDHNx8dHBQsWjFcMXl5eateunek8Z481j7q49lfxHSrCWTly5NDIkSMt52fPnl19+/Z1qq3EOIa2a9dOffv2lb+/v/z8/NS9e3d169bNqXWthhVwZigQyXzIkbiGKjBTsGBBy/FeY+u9wJ3M9heGYbi9cCchOnXqFON8fNWqVZa9k4wZM8aeQHS3h89Dn3vuOZfPQ2Pbp8Z2HirdL3QrWrSow3RXrkHMjrmS8z1FLVq0KF7FjIl9Hp1cjuWevFYBAHjOo5w/JXdqjdwp4ovcqaOUnDvF/1BYASDVi61L2JQwJn1C+fn5qUqVKnr22Wftr0mTJlmOcT9gwAD9999/bo9j8eLFDtPMus53hVXX0852KRYUFGR6Qr9ixQodPXo0zvUNw7Cf4L300kumwwk87Ny5czp+/LhT8cXX5cuXLec5Ow4d4vbaa6+ZXtw6U3l9+vRp+9MB+fLlU506dVzatjsu8qyeinT2htzDvL29NWPGDNObAhMmTNDLL78s6X537fEZUzExWd3kmTlzplPrnzp1SvPmzTOd58xNnjNnzuj333+XFL/eKiSlym4BzZ4kiC3ZJ5kfax577LEE/X6sjjVbtmyJ98X0oyRt2rSxzr906VKix2D1N5Skp556yul9amIcQ728vDR69GjdunVLt2/f1qRJk5x6IvDw4cOWT+d7opeG7t27m04/e/asy92kxofVk0dx7TOSUu7cuWOcj7/wwguWx4hr166pc+fObv9bRkRE6O+//3Zrmw+L7Tw0mtn59+LFi50uhPnqq69Mp+/YscOpJG30MCDPPvusChUq5NQ2pcQ/j04Ox3JPX6sAADznUc6fkju1Ru4UCUHuNKaUnDvF/9CHE4BUzTCMWCtQ8+XLl4TRJB/p06fXjz/+qOeee04REREx5oWEhKh9+/bavHmzW7v6W7NmjcO0hHaXV65cOdPpJ06c0KlTp5QnT5442+jdu7dDt/GGYWj8+PFxnsCsXbtWhw4dkmTeNZ6ZzZs3m7bjzt+1Wff50bg4cJ+sWbOqW7duDp+f+fPn6/Dhw7E+zTl16lT7MBPdunXzSCLd6gIjITdxihUrpo8++kjvvPNOjOlRUVG6ceOGAgICNHv27GTzROaDOnXqpPfeey/GtJ9//lm3b9+O88bw6NGjFRERIV9fX4eLq127dmn79u2qUKGC5fozZ85UVFSUgoKC1KpVq3jFnxqfqDDbLz58zHpYUh5rwsLC9Pfff6tq1aoJaj+1Mxui4UGJkZB8WIYMGRQUFKQ7d+44zHPmXCFaYh5DnXma5u7du5o3b54mT56sDRs2JFos8VGrVi15e3ubDqF07ty5BCeE42J1HhXXPsPTGjVqpN69ezt0dSxJq1at0ujRo9WvXz+3bW/37t0O34NZs2a59D2IizPJ8g4dOuj999+P0eV49Oc7rnGT9+7dax82xOy4O2XKFNMhoaIlpJgxsc+jk8Ox3NPXKgAAzyB/6ojc6f+QO0V8kTt1lJJzp7iPwgoAqdqpU6diHWfa7AnlR0XlypU1aNAgDRo0yGHetm3bNGTIEH3yySdu2daJEyd07do1h+kJPUEoXbq0vLy8TMeBPH36tFMXB0WLFlX16tW1du3aGNOnTZumjz/+ONYusSdNmiRJKl++vJ566imnYj516pTDtJIlSypr1qxOrZ+c5M+f37IrZmel1LHPo/Xv31/jx4+PcZEdFRWlL7/80v75eFhUVJSmTp0q6f4JurNdvj8oOV/k9e/fXwsXLtSWLVsc5rVq1UrFixf3QFRxM7vJExISogULFsTaVeWtW7c0efJkSdLQoUM1fPhwh/3dlClTYi2siB4GpEWLFm4dnzWlc7Ww4t69e/r3338dpif0WJMjRw7lyJFD58+fd5iXFE/ip3RPPPGEbDab5f4+KQorpPuJUbPCCrMhJJKbQ4cOady4cZoxY4Z9/5IjRw5VrlzZPlaxpwUEBKh48eLas2ePw7yrV68m+vZTamGFdL/3hXXr1pnuv9577z3VrFkzXmNKmzE7Dy1UqFC8x+2Or1y5cqlWrVpasWJFjOkzZ86Ms7Aiemif0qVLq0qVKg7dUP/4448aPny45Tm8O4oZU7PUdK0CAHAe+VNz5E7vI3caf+ROyZ2aSam5U9zn+X4GASAR7dixw3JeYGCg25KUKdXAgQP17LPPms77/PPPtWnTJrdsJ7oy+WEJ7Z45MDDQ8gLAlS5me/fu7TDtypUrsY43eOnSJfvNjFdeecXpbZldJEWP8YyUJ1++fKbjbs6YMcNybPQVK1bYL6oaNGignDlzJmqMSS26WzuzC+tZs2Zp165dHogqbtE3eR4W19ipkydP1o0bN5Q+fXr16dNHbdu2dVhm1qxZlvu7LVu26MCBA5LiPwzIoyS2hMKJEydMk4HuGAqgSJEiptPpzjxu6dOnj/UplNjO1dzJqkcIPz+/JNl+fGzcuFFNmjRRkSJF9PXXX+vatWt6/vnn9dNPP+nkyZPq27evp0OMoXz58qbTzc59kkpKSEKmSZPG8omk0NBQtW/fPtYbHa5ITuehnTt3dpi2bt06nTx50nKd8+fP24fAefPNN02LMK5duxZrwVF0MWPLli3j7JHqUZScPiMAgKRD/tQaudP7yJ0ivsidOkqpuVPcR2EFgFTtr7/+spxXpUqVZNHdqid5e3vrxx9/NO2yNzIyUh06dHB6rOPYWI3TePv27QS3bfWk6c2bN51uo2nTpqYnaGbdMkebPn26wsPDlS5dOtMbqVbMLg6SYnx5JJ6Hu26TpPDwcPsTlQ+LHltSknr06JFocXlSkSJF9NFHHzlMv3fvnjp16hTvsQgTm9lNnj/++MOyV4LIyEiNHj1akvTyyy8rXbp0pjd5bty4oQULFpi2ET3Oe4ECBfTcc8/FL3BISv7HmkdZlSpVLOft3btXFy5cSPQYkvPTKg/bvHmzatSooapVq+qXX36Rl5eX2rdvr127dumPP/5Qy5YtnRo6JKlZJbtSQnGDp5UqVUpffvml6bxdu3bp/fffd8t2ktN5aNOmTR2uQQzD0A8//GC5ztixYxUWFqbHHntMbdq0UcWKFVW6dGmH5aZMmWK6PsWMcUtOnxEAQNIhf2qN3Ol95E6REOROHaXU3CkorACQyi1dutRyXpMmTZIwkuQrb968mjBhgum8o0eP6vXXX0/wNqyesnPHDan06dObTnelO30fHx/Tk7StW7eaVu0bhmHvqqx9+/YuPe0WEhLiMO2ff/5xen0kP6VKlVL9+vUdpk+cONHhYvD8+fNasmSJJClPnjyqV69eksToCWbdwUv3bxC5q6tMdzO7yRMVFWV5k2f+/Pk6ceKEfHx87PvK8uXLq0yZMg7Lmt3kCQsL05w5cyTdL+pISTd+k6Pkfqx5lDVo0MBynmEYWrVqVRJGk3xdvXpVnTp10jPPPGPvZrdGjRras2ePZs6caXoDOTmxStgGBAQkcSQp02uvvWZ5XjBy5Ej98ccfCd5GcjoPTZMmjemTazNnzjRdPiQkxH7N0qdPH3tvM2YFEmvWrNGxY8ccpkcXMxYsWJBiRgvJ6TMCAEg65E9jR+6U3CkShtypuZSYOwWFFQBSsQMHDmjv3r2m83x9fdW8efMkjij5eumll9SpUyfTedOmTUvw+N2ZMmUynX78+PEEtStZJ/GzZMniUjs9evQwHZ/722+/dZi2Zs0aHT58WJLUs2dPl7ZjdjGzdetWl9pA8mNWeX379m2NHTs2xrTp06fbxxTs1q2bvLxS56nYzJkz7TdGzJ5sGTZsWLK8KHb1Jk90ZX3z5s2VN29e+3SzXivWrVunI0eOxJi2ePFiXb9+XV5eXpb7YDjP6lhz8+ZNXb16NUFtu+tY86iqV69erEm7n376KQmjSZ42bNigEiVK2Icf8vb21tdff63Vq1erWLFiHo7OOWZP8UlS9uzZkzaQFMpms2natGnKli2bw7yoqCh16tQpwV0gJ7fzULOeog4cOKC///7bYfqMGTN05coVBQYGxjj/bt++vcOQPoZhaNq0aTGmhYWF2buqNtsu7ktunxEAQOIjf+occqfkTpEw5E5jSqm5U1BYASAVmzhxouW85s2bK0eOHEkYTfI3duxYFShQwHRejx49dO7cuXi3bXWifvPmTcsu9p1l1R1h8eLFXWrnscceU9OmTR2mz5kzx6E7vuiK68qVK7v89GjmzJkdpm3YsMGlNpKL48ePyzCMBL1Si+eee06VKlVymP7NN9/YK+0Nw9DkyZMl3f/cduvWLUljTCqHDh2yj73ZqlUr067NIyIi1KlTJ7eNGe9OZjdb9u3bp+3bt8eYtnHjRvuF/ZtvvhljXrt27Uxv8kydOjXGtOgnZ1944QXLMU/hvNiSQvv27UtQ2+461jyqgoKC1K5dO8v5S5cutRxb9VEwb948vfDCCzp//rwkycvLS3PnznXLk29Jyeq4/mDhGWKXPXt2+7HhYSdPnjQd29oVZueh27ZtS/DY3fFVqVIlFS1a1GF6dIFRtKioKI0aNUqS1KlTpxjvI2vWrGrUqJFDG9OnT1dUVJT958WLF+vatWsUM8YhNV2rAACcQ/7UeeROyZ26itzp/5A7/Z+Unjt91FFYASBVunTpkv0g/DAvLy+9++67SRxR/L366qvavHlzom8nbdq0mjVrlmnl8eXLl02fwHZWoUKFLLu4//fff+PdriTduXPHYVq+fPksK71j8+qrrzpMCwkJiZHgvnTpkhYtWiRJ6tWrl8vbyJo1q8O0I0eOuKV7Z+n+iVl0VS8SbvPmzaafCzNmldeXL1+2jwu4Zs0ae48F9erVU+7cud0XaDIRHh6u1q1b6/bt28qbN68mTpyofv36qUqVKg7L7tmzR0OHDvVAlLGzusnzcK8VX331laT7480+9dRTMeZlyZLFtLvU6dOnKzIyUpJ07tw5rVy5UpJ5DxdwXXBwsOW+PzGONf7+/immJ4HkoH///pYJvcjISMvzttTujz/+UNu2bWOMn/rWW2+lyCcDzXpTyJIli3LlyuWBaFKuevXqqW/fvqbzZs2aZR9CKj7MzkNv375t78khoU6ePGm6v4yNWZHDnDlzYnwnlixZokOHDslms+mNN95wWN7sOHrq1Cn7cVb6XzFjrVq1UuU5mLtwrQIAj5bUkj8ldxo7cqcxcT7iXuROXZMacqePOgorAKRKQ4cO1e3bt03ntWvXTmXKlEniiOJnz549Gj9+fJI9yfzUU0/po48+Mp23fPly067dnJEpUyaVKFHCdN6aNWvi1WY0s4uDOnXqxKutatWqmVZrT5gwwV4hPG3aNIWHhytLlixq2bKly9soW7as6fTYnhBwRbdu3Sz/hnDdiBEjnK4MbtKkiekN+a+++kr37t2zXyRIMh2XMjV49913tWPHDnl7e+vHH39UxowZ5eXlpWnTpikgIMBh+S+//FLbtm3zQKSxM7vJM3v2bPuF9+HDh/XLL79Iun+z2IxZQuXs2bNavny5pPuFGpGRkcqYMaPpEx9wnc1mM70QlRLnWPP8888rTZo0luuk1u4q46tgwYKxdr//9ddfOzzllNrdu3dPnTp1ipHUCwgI0HvvvefBqOLP7O9n9kQS4vbll1+qVKlSpvN69+4d76cWE/s89IMPPnA5ed6hQweH/eXly5e1bNky+8/RxYyNGjVSoUKFHNqoU6eOaQHPlClTJMUsZuzSpYtL8T1quFYBgEdLasifkjuNG7nTmDgfcS9yp65JLbnTRxnZPgCpzsaNGzV+/HjTeVmzZtWIESOSOKL4+/LLL/X0008naXXmO++8o+eff9503oABA3TgwIF4tVutWjXT6UuXLo1Xe9Giu81+UOPGjePdnlky+L///tPq1atlGIb9BK9Tp06x3lCzUr58eQUGBjpMnzdvnrZs2eJ6wA/YuXOnNmzYoJIlSyaoHdx38OBBLV68WC1atHBqeZvNprffftth+qlTp/T111/bx9vMnTu36tev79ZYk4PffvtNX3/9tSTp/fff17PPPmufV6hQIQ0bNsxhnYiICHXu3FlhYWFJFaZTzG7yXLx4UStWrJAkjRo1SlFRUSpQoIBpzxSSVLt2bdN9d/RNnhkzZkiS2rRpE699CcxZHWtWrlyZoCdS4nOs8fX1NZ3uridjHuziPqX47LPPTLt1laRr167ps88+S+KIPGvevHk6efJkjGmVK1dWxowZPROQEva5MjtHrFevXkLCeWT5+/tr9uzZpseHa9euqVOnTvHqFjh79uymhQl//fWXFixYEK9Yo50/f17z5s1z+Tw0V65cqlWrlsP06J6i/v77b3vXzw8PvRXN29tbHTt2dJj+yy+/6PLly/rhhx8UGRmpTJkyUcwYB65VAODRkVryp+RO40bu9H84H3EvcqeuSU2500cZhRUAUpXz58+rbdu2lknhiRMnKlu2bEkcVfzs379fs2bNSvKuoL28vPTDDz+Ydgd39+5dtWvXLl5je1k9pfrvv//q0KFDLrcXHc/DNyRy5cqlunXrxqs9SerYsaPSpk3rMH3cuHFas2aNDh8+LJvNpldeeSVe7fv6+qpy5coO0yMjI9W1a9cEnSS9++678vb2try4g2uGDBmi9OnTq0aNGk6v065dO9MnJt99913796Zr166W3eGnVOfOnVPnzp1lGIaeeeYZDRo0yGGZ119/XVWrVnWY/u+//2rw4MFJEabTrG7yfP/997p69aq9i8vXX3/dslcCqzHcf/31Vy1dulT79u2TlPyHAYkeuiSlaNeunWlBw/Xr17V27dp4t/twYiwgIEBt27aNdR2zRJBk/rRQfDzYTb6rPPV3DQ4OjvUJrtGjR2vPnj1JGJFnRXeP+6DHHnsswe0mZBzehJyHPPwUjY+PT4oc0iS5KFGihOUNjTVr1th7cXDVc889Zzq9d+/eunLlSrzalO6fN4WGhuqFF15weV2z64QlS5bo+vXrGjlypCSpQoUKsZ7jmvVEER4erpkzZ9qLGdu2bSt/f3+X40sNnN3vc60CAI+G1JI/JXcaN3KnMXE+4l7kTp2X2nKnjzIKKwAkiaR4qvLmzZtq3LixTp06ZTr//fffV7NmzeLVdkIS1PHVr18/RUVFOX1xYBVjfGLPnTt3jK63HrRjxw7T8dDiUqFCBT399NOm80aPHu1ye9L9C6iHP1s9evRI0IlX+vTp1b59e4fpv/zyi72buOrVq6tw4cLx3oZVF8T79u1T586d43XDa/bs2VqxYoUaNmyo7Nmzu7x+cn/yOam/g3/++afmzJmjxo0bWz51bsbPz8907O/o36+Xl5defvllt8WZHERFRal9+/a6dOmSMmTIoFmzZpl+B202m6ZNm2Z6s3nEiBEJfurA3cwSGr/88ou++OILhYSEKFOmTHEWRXTp0sVhjNTorv8lqWTJkqpYsaLbYk4os7/brVu3PBBJ/OXIkcPyWB/fY83Vq1cdnvBp3bp1nOPRZsiQwXT/cfbsWZe2b9U1b2hoqFPrJ7e/a+vWrS279AwLC1ObNm2cfm/JRXyPocePH3eY5o7hUBJSOHPz5s14rXf06FGH4SmaNm2qHDlyxDuWlCi285X4nMu8+uqratSokem8gQMH6u+//3a5Tavz0IsXL6p58+YKCQlxuc0NGzZo8uTJKlOmjCpUqODy+k2bNlWGDBliTAsLC9Pw4cM1f/58Sda9VUQrVKiQaRLy008/tY9LnljDgCSn82ir6yBX9vvJ8VoFAB4VKTl/Su70f8idkjv1BHKnyVdqzZ0+qiisAJAkErurosuXL6tmzZqWycXu3bvr448/jnf7scWfkCdGrUydOlUrV65U+fLl9fjjjzu1jlUldHwqpCWpefPm6tatm+m8B7vmcsUHH3xgOn3atGm6evWqy+093BVelixZ1K9fP5fbeVjv3r0dpkVGRmr9+vWSpJ49eyao/VatWll2UThnzhx17NjRpc/V1q1b7VXgffr0iXVZqwsPqxt3yYXVdzAxvn937txR9+7dZRhGvJ56eOWVVyxvuNatW9ct4356ImFh5bPPPrOP9zlhwgTly5fPctkCBQqYdvUfGRmpzp07J6ubqWY3eUJDQzV8+HBJ9xMRQUFBsbZRoEAB0yeDo58IducNHnd8JszGcjx48KDpsufPn1f37t1NuxT19Ocz+gmUh/3222/av3+/y+09fKzx8/PThx9+6NS6Zvv6nTt3Or3tP/74Q++++67pPGf322Z/1xs3bujixYumyw8bNkwLFy50Osb4GDt2rOWYvv/++6+6du2aLJNW7j6G3rhxw2Ha33//neD3Ht9zP0kOxRHOmjZtWoyfbTab5Xnfwzy9z3Cn2H738f27TJkyxbRA5d69e2rdurXL59BVqlSxTNivW7dODRs2dOkzffjwYbVu3VqRkZF67bXXXIolWpo0afTSSy85TP/ss88UERGh3Llzq1WrVnG2Y1bwGH3MLV26dLyKPh6U2OfRiXUsl6yP5wsWLHA4N/PktQoAPOpScv6U3GlM5E7JnSY1cqcxJafrzNSaO31UUVgBIElcu3Yt0dretm2bKlasaHlR0K9fP02cONHhqWFXxPaEjzueLHzQn3/+aT/Bc+XExKpr8YR0OT569GjL6uIOHTpo165dLrXXsGFDvfjiiw7TQ0JC9Mknn7jUlmEYmjdvXoxpgwcPVvr06V1qx0ypUqVijHH2oBw5ciR4bGZfX1/LG2WSNGvWLD355JP6559/4mzrxx9/VM2aNXXr1i01bNhQNWvWjHV5q5Nss6dmkxOr76C7v3+RkZHq2LGj9u/fr3Tp0ql27dout5E2bVrTC0xJlk9pu+ru3bsO0yIiItzStisJhXXr1tm7omvXrp1at24d5zp9+vQx7dLxwIEDsX4vkprVTR7DMOTr6+v0zSOrXi18fX3VoUOHBMX4ILO/m6sXz2Y37tatW6fLly/HmBYVFaW+fftq8+bNputYfYbc8Rl15vNZtmxZ9e3b12G6YRh6//33Xd7mTz/9FOPnPn36OJ24K168uMO0RYsWOfU+5syZo7p168rHx8d0vtUTZg+z6jFgwYIFDtN+/fVXvf/++8qbN69TbceXr6+vFixYoEqVKpnOnz17tj1Jk5y4+xiaOXNmh2kXL17UnDlz4lz3v//+s9xnPnzud+bMGad7Stm3b58uXLjg1LIPbm/q1KkxpnXp0kVlypRxan2z74MnjmnuENt5d3zPyYODgzVjxgzTa5mjR4+qRYsWLu/vYysOW7t2rUqVKqXVq1fH2c7KlStVtWpVnT17VmXKlLHsvtoZZutG7wNee+01y33hg1q2bKl06dKZznNHMWNin0dbfV5d+fu6ss8/ceKEXnnlFYcnDD15rQIAj7qUnD8ld+qI3Gn8kTt1HbnTmMidIrFQWAEgSZw4ccLtbd64cUNvvfWWKlWqZNp+2rRp9eOPP2rUqFEJKqqQYn96z+rpn/j47bffVK9ePfuBv0WLFk6va5Uwv3v3brwvzIKCgjRr1izT7rzu3Lmj+vXr6/Dhwy61OWbMGNOK1DFjxujPP/90up0ff/wxxjjstWvXdusTUFYnd926dXOpe7PY2o9tPMNdu3bpySefVL169TRr1iwdOHBAd+7cUUhIiP777z999913evrpp9W+fXvdunVLadOm1ddffx3nds+cOWM63ZnkuSdZfQfd+f27ffu2WrRoYX9Su2HDhvEeh7tv374OTwzmzJlTDRs2THCckkyfNHdX1/7OPgFx7tw5tWnTRpGRkcqePbu++eYbp9bz8vLS+PHjTXsU+Oabb/T777+7FG9isrpB1KpVK9PxIM20aNHCNGnRoEEDBQcHJyS8GMz+bq7u+4sWLeowLSQkRHXq1NHWrVt1584dbd26VQ0bNtS8efMsb8xZXbQnJFkVzdn39NFHH+mJJ55wmP7zzz9r7ty5Tm9v48aN+vXXX+0/lypVSp9++qnT65t1S3/x4kW9+eablkUDt27dUp8+fdSmTRs9+eSTGjZsmOlyixYtcioGs7+rJA0YMEBTp07VtWvXdOrUKX3yySdq3ry56tatmyRD1AQFBWn16tWWCbepU6eqQYMGunTpUqLH4ix3H0NLly5tOr1v376WvavcuXNHH3/8scqWLWvZDeiD58WnT59W9erVLXsoeVhUVJRGjBjh1LLRPvzwwxjnoblz53apDbN9hjv2F1Li3pwwE1sBi9Xnxxm1a9c27S5Xul8I0alTJ5e6Q27QoEGsY14fP35cNWvWVNWqVTV58mTt3btXN27cUGhoqI4dO6YffvhBtWrVUp06dXT+/Hn5+Pho3LhxCepSunLlyipSpIjD9LRp0zqdXA0KCjLt2cLX11ft2rWLd2zREvs82uoczJXPca5cuUyLS3766ScNGDBAp06d0tWrV/XDDz/omWeekbe3t+lnwVPXKgDwqEvJ+VNyp+ROo5E79QxypzGRO0ViobACQKILCQnRX3/9ZTovNDTU5SfJ9u/fr3fffVf58uXTV199ZZpEbNasmXbv3q22bdvGK+aHbd682XLetGnTEtxl84kTJ9S9e3c1bNjQfoAvWbKk02PRhYaG2scONuNKt+MPq1ChgmVF9NmzZ1W9enWX2s+VK5fmzJnjcGIQERGhFi1aOHWxceDAgRjJ5cKFC2vmzJkJLqB5UPPmzR3G2/Py8nJb1azNZtOMGTOUM2dOy2UiIyO1fPlytWvXTsWKFVPatGkVFBSkokWLqkePHtq6dat92RkzZqhAgQKxbtMwDI0dO9Z03q+//qoVK1bE780kshs3bljeYDp8+LC9m8H4ioyM1E8//aRSpUrFuFEZn67somXLls3hyciuXbsm6IbDg/bt2+cw7eTJky493W3WBb10/ynouISFhal58+Y6d+6cJGno0KGmT15bKVOmjOnv1zAMtW/f3uknqxOb1U2e/v37O91GYGCgac8X7hwGJPpG18MOHTrkUjv169c3nb5jxw49/fTTSps2rZ5++mktW7ZMNWvWtOyW3eqi3dW/682bNx2mHT9+3Kknd9OmTauff/7ZdLiWl19+2alxKc+fPx/j75QtWzbNnz9fadKkiXPdaC+99JK8vBwvub799lu98MILWrZsma5fv647d+5o+/bteuedd1SgQAF9++23euqpp7R06VLTsTUladSoUfr888/1yy+/6PPPP9eAAQNMlytXrpzpE8x37txRt27dlDlzZuXNm1cffvihfH19k/TGV2BgoBYsWKAhQ4aYPo2+bNkylS1bVrNmzYp37xWRkZGaOXOmQ88rrkqMY6hVwujKlSuqUqWKxowZowsXLigkJET//POPPvjgAxUoUECDBg2SzWazPCeZNGmSrl+/rrVr1+qZZ57RvXv3nO5lRZJGjhypJUuWOLXskiVLYiSH0qRJowULFlh26/qwU6dOmXZl6o79heT6fjChtm/fbjkvIefj0v3uY8uWLWs6b/bs2WrTpo1CQkKcbm/UqFEqVapUrMts3LhR3bt3V6lSpZQxY0YFBAToiSeeUIcOHbRq1Sr7cl9++aWeeeYZp7dtpVOnTg7TunbtqowZMzrdhllPUQ0bNnRLMaNVInT37t2WY6y7wuz8TnLtc2yz2SxvQIwYMUJ58+ZVlixZ1KFDB509e1ZfffWVaSGGJ65VAOBRl9Lzp+ROyZ1K5E49hdypI3KnSCw2I7n1r5qK1WneRdeumX8RkfplypRBKxZMi3vBVCYqKko9evTQlClTLJfx8/NTqVKlVKJECRUpUkR58+ZVcHCwAgICFB4erps3b+rYsWPat2+f1q9fb3ny6O3trUaNGql///6WXZHFx3///adKlSrF2m1WlSpV9NJLLylXrlxKmzat/P39HU4CDMPQvXv3FBYWpmvXrunixYs6fPiw/v77b23bts3hAmPw4MEaMmSIUzF+8sknsXbp26pVK5ee0H2YYRiqVauWZWVuQECAhgwZor59+zp9w+mnn35Su3btHLrgiu7uuF69eqbrrVmzxp4IlKQiRYpo9erVTj897ooPPvggxpPJDRo0iPHksjvs27dPNWrUcLnb7Qd9/vnneueddyzn37hxQzt37tQ333xjryg24+vrq759+6p169YqWLCgS0nsxNSrVy9NmDDBcn5QUJB69uypMmXKKHPmzAoMDJS3t7fDzczIyEiFh4fr9u3bunr1qk6ePKn9+/frjz/+cHgiOjAwUJcuXbK8oemMY8eOqVChQoqMjJSXl5eOHj0a6xh6zlq9erVlt4Vz5851agxy6f4NmoEDBzpMz5w5sw4ePKgsWbKYrhcZGanWrVtr/vz59mm//PKLGjVq5NR249q+dD8psXbtWsvuvJPSw3FWq1ZNa9eudamNv/76K0YXftmzZ9fp06ed6tbcGRMmTFCvXr1M561bt07PPfec023VqFEjzvdXuHBhbdiwQdmyZXOYFxYWplKlSpneBGrbtq1+/PFHp+LYsmWL5TARc+bMMS1WMbN+/Xo1aNDAYSzUoKAgjRs3Th07djRd759//lHbtm114MABSfe7Mv39999VsmRJp7b7oJdffjnW8yAzVatW1dKlS5UuXTpNnz49zkKcrFmzqnv37pa9W8yYMSPOLvp9fHw0e/Zsl574cqedO3eqW7du2rFjh+n8smXL6vXXX1eLFi2UNm3aONs7ffq05s6dqwkTJpieO/r5+enll1/W+++/H2uiLjGPoVF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",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"subgraph_rewrite.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
    "fig"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".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": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}