{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2100x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 14 / 5), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"ModelA\\n8DP\",\n",
    "    \"ModelA\\n8TP\",\n",
    "    \"ModelA\\n2DP$\\\\times$4TP\",\n",
    "    \"ModelA\\n2DP$\\\\times$8TP\",\n",
    "    \"ModelA\\n4DP$\\\\times$2TP\",\n",
    "    \"ModelB\\n8TP\",\n",
    "    \"ModelB\\n4DP$\\\\times$2TP\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFU\n",
    "data_a = {\n",
    "    labels_name_a[0]: [39.3, 44.4],  # modela 8DP\n",
    "    labels_name_a[1]: [39.3, 43.6],  # modela 8TP\n",
    "    labels_name_a[2]: [33.8, 44.6],  # modela 2DP 4TP\n",
    "    labels_name_a[3]: [38.0, 42.6],  # modela 2DP 8TP\n",
    "    labels_name_a[4]: [36.9, 44.7],  # modela 4DP 2TP\n",
    "    labels_name_a[5]: [43.4, 49.4],  # modelb 8TP\n",
    "    labels_name_a[6]: [37.6, 38.0],  # modelb 4DP 2TP\n",
    "}\n",
    "\n",
    "data_b = [\n",
    "    random.uniform(550, 650),\n",
    "    random.uniform(550, 650),\n",
    "    random.uniform(550, 650),\n",
    "    random.uniform(550, 650),\n",
    "    random.uniform(550, 650),\n",
    "    random.uniform(670, 770),\n",
    "    random.uniform(670, 770),\n",
    "]\n",
    "data_c = [\n",
    "    random.uniform(190, 250),\n",
    "    random.uniform(190, 250),\n",
    "    random.uniform(190, 250),\n",
    "    random.uniform(190, 250),\n",
    "    random.uniform(190, 250),\n",
    "    random.uniform(250, 310),\n",
    "    random.uniform(250, 310),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"ILP\", \"Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time (s)')"
      ]
     },
     "execution_count": 57,
     "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, 100)\n",
    "ax.set_yticks([0, 50, 100])\n",
    "ax.set_yticklabels([\"0\", \"50\", \"100\"], 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(\"MFU (%)\", fontsize=g_label_fontsize)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(list(group_centers.values()), data_b, \"^--\", color=colors[0])\n",
    "ax2.plot(list(group_centers.values()), data_c, \"v--\", color=colors[1])\n",
    "ax2.set_ylim(0, 805)\n",
    "ax2.set_yticks([0, 200, 400, 600, 800])\n",
    "ax2.set_yticklabels([\"0\", \"200\", \"400\", \"600\", \"800\"], rotation=90, va=\"center\")\n",
    "ax2.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize)\n",
    "ax2.set_ylabel(\"Time (s)\", fontsize=g_label_fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f92b394ecf0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(\n",
    "    ncol=3,\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    bbox_to_anchor=(0.5, 0.98),\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LMxMnTvTpOSxatKjGf1dvyv8/2rRp49KHP/74w9hsNpf1HXfcccbhcPi9TlSN3HpGbsuQ28hFdj0ju2XIbmQit56R2zLkNjKRW8/IbRlyG9nIr2d1Lb/+vCbl5s+f79ZOw4YNTV5eXrXbQuXIrWfk1vdly5FbAJGEwgoAHnk6+M3OzjZJSUkuBzH169c3Bw8erFbbQ4YMMZLMRRdd5DK9pgdahw8fNmeddZaRZGJiYsyDDz5ocnJyXOZZunSp6dSpk0v7Q4cONfn5+T73+8Ybb3Qu27VrV/Pjjz+6/P7XX381ffv2dXsevj6XX375xbRq1crYbDZz3333mcOHD7v8ft68eaZZs2Yu7Z577rmmpKSk0nYDfdKpqKjING3a1Egyjz76qNvvhw4d6vb8P//8c7/WCd+QW3fktgy5jWxk1x3ZLUN2Ixe5dUduy5DbyEVu3ZHbMuQ28pFfd3UxvzV9Tcrl5+ebbt26ubXzyCOPVKsd+IbcuiO35BZA9KOwAoBHng5+jTHmlltucTuQefzxx31ud82aNc7lVq1a5fK7mhxoFRcXmwEDBjiXefHFF73Om5OTY7p06eKyjiFDhpjCwsIq13PnnXc6l+nRo4c5dOiQ1/4MGjSo2s9ly5YtpnHjxsayLPPmm29WOl955XT57e6776607UCfdHrrrbeMJFOvXj2zd+9et99/9NFHbs9/5MiRfq0TviG3rsjtEeQ2spFdV2T3CLIbucitK3J7BLmNXOTWFbk9gtxGPvLrqq7mtyavSbnDhw+bsWPHurUxePBgU1xc7HM78B25dUVuyS2A2oHCCgAeeTv43bx5s4mJiXE5mGnRooVPB5DGGHPJJZcYSWbQoEFuv6vJgdY999zjnL9v375Vzr9+/XoTFxfnsp6qhpx77733nPMmJyebP/74o9L5t2/f7raOyp5LSUmJ6dOnj5FkLr300iqfw7Rp01zajomJMf/73/+8zh/ok06nnXaakWTGjx/v8felpaWmdevWLuu02+1m8+bNfq0XVSO3R5BbV+Q2spHdI8iuK7IbucjtEeTWFbmNXOT2CHLritxGPvJ7RF3Ob01ek/3795vp06ebVq1auS1/4YUXun2jH4FDbo8gt+QWQO1BYQUAj7wd/BpjzF//+le3g5pXXnmlyja3bNniPHD2NGxmdQ+0fvvtNxMbG+uc/7333vPpuf3jH/9wOyGyevVqj/NmZ2ebjIwM57x33HGHT+sYN26cz8/lzTffdM539BBwnmRmZrr9rW677Tav8wfypNOKFSuc7RxdFV7R0dcBlGTuueeeGq8XviG3ZcitK3Ib+chuGbLriuxGNnJbhty6IreRjdyWIbeuyG10IL9l6np+j16PZVmmVatWpnfv3mbo0KFm9OjR5oILLjDnnnuu6devnznmmGOMZVluy/Xq1ct8/PHHPq8XNUNuy5BbcgugdqGwAoBHlR38VhxyrfzWoUMH43A4Km3ztttuM5LMSSed5PH31T34veyyy1wOYL0NoXa0rKwst8rocePGeZz3rrvucpnv999/92kdL7zwgs/P5dRTTzWSTKNGjXxqu6CgwO1vdcIJJ3idP5Annf72t78ZSaZ3796Vzrd79263yur09HSfq89RM+S2DLl1RW4jH9ktQ3Zdkd3IRm7LkFtX5Daykdsy5NYVuY0O5LdMXc/v0eupya1Ro0bm+uuvN/PmzTMlJSU+rxvVR27LkFtyC6B2iREAVFOXLl00ZMgQff75585p69ev19y5czV69GiPyxw4cEAvvfSSJOnOO+/0uw/79u3TO++843zcrl07JScn+7Rsenq6zj77bH3yySfOae+9954mT56shg0bOqfl5ubqxRdfdD5u1qyZjjvuOJ/W4et8f/zxh1asWCFJKi0t1UUXXeTTckdbt26dSktLZbfba7S8L/bu3atZs2ZJkm666aZK523SpInOO+88vfXWW85pu3fv1uzZs3XxxRcHrY/wjtxWjdyS20hEdqtGdslupCG3VSO35DbSkNuqkVtyG6nIb9VqY34lqUePHvriiy+Umpoqy7IkSSUlJcrNzdWOHTv022+/6dtvv9UHH3ygzMxMSVJ2dramTZumadOmqVWrVnr44Yd16aWXBrWfcEduq0ZuyS2AyERhBYAamTBhgsvBryQ9+uijXg9+n3/+eR06dEitW7fWhRde6Pf6P/nkExUVFTkf/+Uvf6nW8kcf/JaUlGjx4sU699xzXdaxf/9+5+NTTjnF5/bLDwyrsmTJEuf9nJwc50md6jLGaN++fWrSpEmNlvfFSy+9pIKCAjVr1kxjx46tcv4bbrjB5aSTJE2dOpWTTmFEbitHbsltpCK7lSO7ZDcSkdvKkVtyG4nIbeXILbmNZOS3crUxv5KUkJCgtLQ0l2kxMTFKS0tTWlqaOnbsqHPPPVePP/645s6dqzvuuEP/+9//nPNu2bJFl112mRYuXKjXXnvN578TAoPcVo7cklsAkckW7g4AiE4DBw7Uqaee6jJt2bJl+vrrr93mLSoq0jPPPCNJuvXWWxUT439N1+LFi10eJyUlVWv5rl27uk1bvny5y+OFCxe6PA7GgeXKlSud93v27ClTdommGt2CeeBbWlqqadOmSZKuvfZaxcbGVrlM79691aVLF5dpS5Ys0dq1a4PSR1SN3AYGuUWokd3AILsIJXIbGOQWoURuA4PcIhzIb2BES36ry7IsjR49WmvWrNHgwYPdfj9jxgzde++9YehZ3UZuA4PcAkBoMWIFgBq788473SqEH330UfXr189l2htvvKGdO3eqQYMGuuqqqwKy7l9++cXlscPhqNbyR58MkcqG8axo2bJlLo+PrqYNhL179zrv5+TkBLz9QJk7d662bt0qqezEka/DylWs/C43ZcoUvfDCCwHtH3xHbv1HbhEOZNd/ZBehRm79R24RauTWf+QW4UJ+/Rct+a2p5ORkffDBB+ratas2btzo8rvHHntMl1xyiTp27Bim3tVN5NZ/5JbcAggtCisA1NjYsWN1zDHHuAzHNX/+fK1du9Z5cGmM0RNPPCGpbNjM6lb/epOdne3y+PDhw9VaPjk5WbGxsSouLvbaRlZWlstjTydQ/LVv3z7n/V27dskYE5FDmD333HPO+//973/9auvNN9/U448/rtTUVH+7hRogt/4jtwgHsus/sotQI7f+I7cINXLrP3KLcCG//ouW/PojOTlZjzzyiM477zyX6SUlJZo2bZqeffbZMPWsbiK3/iO35BZAaHEpEAA1Zrfbddttt7lNf+yxx5z3P/30U/3666+qV6+ebr755oCuu6KK1bm+OrpK+OiTIBUPTCXpwIED1V5HdRw4cEC//fZbUNdREz///LNzGL6ffvqp2sPIXX/99S7t5ebmasaMGeF4KhC5DTRyi1Ahu4FFdhEK5DawyC1CgdwGFrlFKJHfwIrU/AbCqFGj1KhRI7fp/hZZofrIbWCRWwAIPgorAPhl/Pjxaty4scu0WbNmadOmTZKkxx9/XJJ02WWXKT09PWDrPfqab7///rtKS0ur1Ua9evVcHrdr187lcWJiosvjitXTgdKgQQOXx19++WXA1+Gv8m/yDBo0SCeccEK1l7/pppvcpk2dOtXvfqHmyK1/yC3Chez6h+wiHMitf8gtwoHc+ofcIpzIr3+iIb+BEBMTo+7du7tNL788EEKL3PqH3JJbAKFFYQUAvyQkJLidVCgpKdGkSZP0ww8/aPHixbLZbLrjjjsCut4OHTq4PC4sLNT69eur1UZubq7L46MPzo6ugl2zZo1KSkqqtY5yxhiP05s2bery+Pnnn69R+//85z9VUFBQo2Urs3//fr3xxhuSpFtuuaVGbXTq1EkDBw50mbZ+/fpae6AfDcitb8gtuY00ZNc3ZJfsRhJy6xtyS24jCbn1Dbklt5GI/PomWvMbSA0bNgx3F/AncusbcktuAUQGCisA+O2mm25yq8B99dVXNXHiREllQ3Udd9xxAV1nv3793KZ99tlnPi9fWlqqQ4cOOR+npaWpd+/eLvMc/c2V/Px8LViwoJo9LeNwODxO79mzp8vjn376Se+++2612v7xxx/1zDPPKD4+vkZ9q8yrr76qvLw8tWvXTsOGDatxO3//+9/dpvGNnvAit1Ujt+Q2EpHdqpFdshtpyG3VyC25jTTktmrkltxGKvJbtWjNbyDt3r3bbdoxxxwThp5AIre+ILfkFkBkoLACgN8aNWqk8ePHu0zLy8tzHihOmDAh4OscPny4kpOTXabNmTPH5+U3bdrkUiF88cUXKzY21mWe/v37uy33wgsvVK+jfyouLvY4vV+/frIsy2Xa9ddf7/MwZsYY3XHHHRo0aJBbO/5yOBzOE0N///vfZbPVfJcxYsQItW7d2mXahx9+qO3bt/vVR9Qcua0auSW3kYjsVo3skt1IQ26rRm7JbaQht1Ujt+Q2UpHfqkVjfgOpoKBAy5Ytc5t+9tlnh6E3kMitL8gtuQUQGSisAOBReRWst2HGjnb77bfLbre7Te/bt6969erl8/oqqmzdqampuuKKK1ymLVmyRCtWrPCht9LKlSud9+12u26++Wa3eS644AK3A+K5c+dWq3q53NFDw5Vr2rSphg8f7jJt37596t+/v/NagpV56KGH9OWXX+rqq6+udp+q8sEHH+h///ufEhISdPnll/vVlt1u15VXXukyraSkRJMnT/arXbgit+SW3EYnskt2yW70IbfkltxGH3JLbslt9CK/5Nfbt/Vr4tVXX3UZaUCSYmNjdd111wVsHSC3ErkltwBqJQMAHtx5551Gkjn//PN9Xuaiiy4yklxuH330kU/L5ubmui3bvXv3SpfZs2ePady4scsy55xzjk/ru+CCC5zLXHPNNV7nu+KKK9z6lZ6ebn755ZdK23/55ZddlmnQoIFxOBwe512yZInbOiSZ+vXrm+eff94UFha6LZOdnW2uvfZaI8mcccYZlfZlwYIFLu327du30vmNMcbhcJiuXbs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",
      "text/plain": [
       "<Figure size 2100x840 with 2 Axes>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"or_tool.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
}