{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NpBjWANDY7J2f2rp1a9x4441Vr/v06RPXXnttDicCaHjFdA2orKyMMWPGxMaNG6N169Zx3333RbNmzRrsfACFrFj2/zfeeCNj/bDDDov58+fHhAkTonXr1jvsMXjw4HjiiSeiefPmae+tWbMmxowZUx+jAuSFYtn/d6Rly5bx0EMPxbnnnrvDz912220xdOjQWL16dSNNBkBd7ArXsH8nWJHHUqlUxnpd06qVlZUZ60m3VqmLYlgDQGOzd37q9ttvj7Kysoj4NKV6xx135O2sAPWlmK4B//Vf/xV//vOfIyLilltuiX333bfBzgVQ6Ipl/1+wYEHG+siRI6Nnz5417vP5z38+vve972V875lnnolnnnkmq/kA8k2x7P87M2PGjPjDH/4QzZo12+GjQWbOnBmHH354vPXWW404HQDZ2FWuYf9KsCKPtWnTpkH6lpeXZ6zXNUGUSTGsAaCx2Ts/fYbyv96dYuLEifGFL3whhxMBNI5iuQa8+uqrVY/9OPnkk3f612kAu7pi2f8XLVqUsd67d+9a97ryyiujY8eOGd/78Y9/XOt+APmoWPb/HfnpT38aw4cPjw0bNsTDDz8cM2fOjP/8z/9M/Pw777wThx9+eMyePbsRpwSgtnaFa9i/E6zIY23bts1YT/qCqqmkZ9DUNUGUSTGsAaCx2TsjLr/88li/fn1ERHz5y1+O7373uzmeCKBxFMM14JNPPolvfOMbsW3btvjsZz8bv/71r+v9HADFphj2/23btsVHH32U8b3Pfvazte7XunXruOiiizK+N3369KrvFwAKWTHs/0m2b98eF1xwQYwbNy4qKyvjrrvuipEjR0aTJk3iRz/6UfzmN7/J+NiniIj169fHcccdF7NmzWq0eQGonWK+hiURrMhjDfUFuWnTpoz19u3b16lvJsWwBoDGtqvvnY899ljcc889ERGx1157xb333hslJSU5ngqgcRTDNeCKK66It956K0pKSuI3v/lN4l8bA/BPxbD/J4UqIiK6deuWVc+kOx6lUql44YUXsuoJkE+KYf/PZPv27XHaaafFr371q4iIGDt2bHzjG9+o9pkzzzwz/vSnPyXOtHHjxjjuuOPib3/7W0OPC0AWivUatiOCFXmsQ4cOGet1/YLcuHFjxnqvXr3q1DeTYlgDQGPblffOZcuWxTnnnBMRER07downn3wyOnXqlOOpABpPoV8D/vCHP8Rtt90WEZ/ewn3YsGH12h+gWBX6/h/x6R2LkmT7b/pevXpF//79M773l7/8JaueAPmkGPb/f5dKpeKss86Khx56KCIi9t577/jRj36U8bNHH310vPjii9GjR4+M73/00UcxYsSI2LBhQ4PNC0B2ivEatjOCFXmsT58+GetJSZ2a+uCDDzLWe/bsWae+mRTDGgAa2666d27bti1OO+20WLduXTRv3jymTp2a+L8FQLEq9GvAv/5l8U9/+tMoKSnJ6r+zzz47Y/+kzwMUukLf/yMidtttt8T32rVrl3Xf4447LmN95cqVWfcEyBfFsP//uxtuuCHuu+++qtc//OEPo7S0NPHzBx54YLz44ovRu3fvjO+/++67MW7cuPoeE4A6SrqGbd++vU7hinz+PYZgRR7r27dvxvqaNWuioqIi675r1qzJWE/6h0tdFMMaABrbrrp3XnLJJTFz5sxo0qRJ3H333TFkyJBcjwTQ6Ar9GrB69ep67Qewqyj0/T8ionXr1onvNW/ePOu+/fr1y1hft25d1j0B8kUx7P//6i9/+UuMHz++6vVnP/vZGD169E6P23vvveO5555L/KXZnXfeGa+99lq9zQlA3e25556JAeoVK1Zk3Teff48hWJHHOnbsmPEZlJWVlfH+++9n3XfVqlUZ64MGDcq6Z5JiWANAY9sV985f/vKXVbeOnzRpUnz961/P8UQAubErXgMAKI79v02bNonhii1btmTdN+mXjlu3bs26J0C+KIb9/19dcsklUVlZWfX65JNPjmbNmtXo2N69e8ef/vSn6Ny5c8b3f/rTn9bLjADUnwMOOCBjffny5Vn3zOefYQlW5LkBAwZkrC9btiyrfuXl5bF27dq0+u677x6f+9znsuq5M8WwBoDGtivtnY8//niMHTs2IiImTpwYF154YU7nAci1XekaAMA/FcP+v/fee2esr1+/PuueXbt2zVjv1KlT1j0B8kkx7P8REc8//3y88sor1WrDhg2rVY8+ffrEww8/nDGMMXXq1Ni8eXOdZgSgftX3NSwi4r333kurNWnSJA477LCse9YXwYo8d/zxx2esL1myJKt+ZWVlGeuDBw/Oql9NFMMaABrbrrJ3zp49O0477bTYvn17jB07ttrtIgF2VbvKNQCA6oph/z/wwAMz1rNdQ0RE27ZtM9aT/qIZoNAUw/4fEXHfffel1ZKuCzsyZMiQmDBhQlp9y5Yt8dJLL2UzGgANpL6vYZs3b874mNm+ffsmfl/QmAQr8tzIkSMz1l9//fWs+i1atChjfcSIEVn1q4liWANAY9sV9s65c+fGCSecEJ988kmcddZZcfPNN+dsFoB8UsjXgFQqVS//TZ48uVb9AYpBIe///3DkkUdmrM+dOzfrnkmPF8nml3UA+agY9v+IiBdffDGtlukxJzUxbty46NWrV1p98eLFWfUDoGEMHz48WrRokVbP9hq2ePHijD/nyZffAQtW5Lm99tor47MkX3vttaz6vfnmm2m1Zs2axcknn5xVv5oohjUANLZi3zvffvvtOO6442LDhg3x1a9+Ne68884oKSnJySwA+abYrwEAZFYM+//w4cMz1v/yl79k3XPTpk0Z60cddVTWPQHySTHs/xGZb/ue6ZdtNdG8efP45je/mVavy6OlAKh/rVq1yvjYp/q8hkVEfP3rX8+qX30TrCgAY8aMSavNmTMnq7/MypQQGj58eIM/l7IY1gDQ2Ip173z33XfjmGOOiRUrVsSxxx4b999/fzRt2rTR5wDIZ8V6DQBgxwp9//8//+f/xMCBA9Pq06dPz7rnBx98kFbr0aNH7Lfffln3BMg3hb7/R0TGWTPt4TU1ZMiQtFr79u2z7gdAw8h0DSsrK8sqDJfpGrb//vtH//79s5qtvglWFIDzzz8/2rVrV6324YcfxuzZs2vda+bMmWm1yy67LNvRaqwY1gDQ2Ipx73zvvffimGOOiaVLl8ZRRx0VU6dOjdLS0jr1/PGPf1ynH9QC5KNivAYAsHPFsP+fd955abX33nsv8a/PdmbBggU1OgdAISuG/T/TYz9WrlyZdb/PfvazabXu3btn3Q+AhjF69OjYe++90+pPPfVUrXv9+c9/Tqvl08+wBCsKwGc+85k4//zz0+pTp06tVZ/XXnstVq1aVa128MEHx7HHHlun+WqiGNYA0NiKbe9899134+ijj4533nknDjvssHjiiSeiVatWdep52223xdVXX+2vroGiU2zXAABqphj2/7PPPjs6d+6cVr/77ruz6vfKK69Ue92sWbP49re/nVUvgHxVDPv/YYcdllabNWtW1v3Ky8urvW7SpEkcccQRWfcDoGE0bdo0rrrqqrR6ba9ha9asiVdffbVarXv37nHWWWfVab76JFhRIC677LJo3bp1tVptvyAfeeSRtNp1111Xp7lqoxjWANDYimXvLCsriy9+8YuxZMmSOOSQQ+Kpp56Ktm3bZt2vsrIyfvazn8WFF14Yffv2jYMOOqgepwXID8VyDQCgdgp9/2/VqlVce+21afUpU6bERx99VOt+Dz/8cLXXF1xwQfTo0SPr+QDyVaHv/1/5ylfSao8//njW/RYuXFjt9ZFHHhldunTJuh9AIauoqEirVVZW5mCSzM4+++y0Ow09+eSTaSG5HZk2bVramr7//e/X+Y7X9UmwokB07949xo8fX622ePHimDFjRo2OLy8vjzvuuKNa7ZRTTokTTjihxjNMmzYtBg4cGKWlpdGrV6+44YYbavX/tPmwBoBCkw97Z133/7feeiuOOuqoKCsri8GDB8fTTz9d42diplKpqKioiE2bNsXy5cvj5ZdfjptuuikGDBgQV155ZVRWVsYZZ5xR41kACkkxXAMAqL1i2P8vuOCCGDRoULXaunXr4mc/+1mNe0REvPTSS/HGG29Uve7Ro0dcf/31teoBUCgKff/PdCv4P/zhD7Fo0aIan/9fPfDAA9Vejxs3Lqs+AMVg27ZtabWtW7fWW/8XX3wxvvCFL0SLFi2ie/fuceWVV9YqFNGiRYu0f+tv2rQpfv/739e4xy9+8Ytqrw899NCMd3PKqRQFY+vWralDDz00FRFV/33pS1+q0bHXX399teO6du2aWr58eY3P/cADD1Q7/h//XXzxxQWzBoBCVcj7/1//+tdUly5dMvaoj/9KSkpSS5curfF6AApNIV8D6mLy5MkZzw2wqyiG/X/hwoWptm3bVutRWlqamjt3bo2O37JlS6p///7V/u3/6KOP1moGgEJT6Pv/ww8/nHb8kCFDUhUVFTXukUqlUrNmzUqVlJRU9Tj22GNrdTxAsdl///3T9tfjjz++XnrPnDkztdtuu6X1HzFiRK17ffWrX63WY7/99ktt27Ztp8fde++91Y5r3bp16s0338xmOQ3KT6YKzJIlS1IdO3as9sU1ZcqUHR4zY8aMVGlpadXnW7RokZo1a1atzpvp/2EjItWkSZPUypUrC2INAIWsEPf/WbNmpTp06NBgoYqISH3xi1+s1XoAClEhXgPqSrACoDj2/6lTp6aaNm1arU+fPn1Sq1ev3uFx27dvT5155pnVjrvmmmtqdW6AQlXo+/+FF16Y1uM//uM/Utu3b6/R8e+8806qR48eVcfutddeDf79B0C++8xnPpO2t/bv379eeh9//PGJP39/+eWXa9Vrw4YNqb333rtajwkTJuzwmHnz5lW77pWUlKQeeeSRuiypwfjJVAGaNWtWqk2bNlVfYKWlpYlfYL/73e+qfbZt27apJ598stbnzJRU+sd/L730UkGsAaDQFdL+/8c//jHVqlWrBg1VRETqzjvvrPWaAApRIV0D6oNgBcCnimH/nzx5crW/Oo6IVL9+/VJvvfVWxs+vWbMm7S/dxo4dW+vzAhSyQt7/t23blhozZkxaj+OPPz71zjvvJB5XWVmZevDBB1OdOnWqOqZ79+6phQsX1notAMXkySefTAy+/fWvf61z/3333Tdx///tb39b636LFi1KdevWrVpQ4he/+EXGzz777LOprl27Vn12t912S02ePLmOK2o4JalUKhUUnJdffjlGjRoV77//flVt1KhRMXr06OjRo0eUlZXF3XffHc8//3zV+3369IkHHngg+vfvX+vz9e3bN+bNm5dWb9KkSaxYsSK6du2a92sAKAaFsP9PnTo1vv71r2d87lt9atGiRaxatSratWvXoOcByBeFcA2oL1OmTImzzz47re7bV2BXVAz7/7Rp0+LMM8+Mjz76qKrWvHnzOP300+PYY4+N7t27x7p16+LFF1+Me+65JzZs2FD1mZtuuikuvvjiWp8ToNAV8v6fSqXiO9/5Ttx4441RWVlZVS8tLY3jjz8+vvzlL8eee+4ZpaWlsW7dunj99dfjsccei/nz51d9dujQoXH//fc36PcdAPlq06ZNsXz58vjTn/4U3/ve96r9O/pfdenSJW644YYYMmRIfPazn41WrVrV+lwnnnhi/O///m/G915++eU49NBDa91z0aJFMXLkyFiwYEFVbejQoXHGGWfEXnvtFe+//3488MAD8eijj1a936NHj7j33ntj6NChtT5fo8ltroO6WLlyZer000/f6V/ztmvXLjVhwoRUeXl51ud68MEHM/au6/OVG3MNAMUi3/f/s846q8HvVBERqVNPPTXrdQEUqny/BtQXd6wAqK4Y9v8FCxakRowYUaN/65eUlKRGjhzpr5SBXV6h7/9vvPFG6rjjjqvVz3v233//1JQpU1IVFRVZnxegUP3mN7+p88/Nn3/++Vqd86WXXsp416IRI0bUaS0bN25MXXLJJWmPBvz3/1q0aJG69NJLUx9++GGdztcY3LGiCMyfPz8mT54czz33XLz99tuxadOm6Ny5cwwcODBOPPHEGDNmTL38Ne+0adNiwoQJMW/evOjWrVucf/75cfXVV0eTJk0KZg0AxaQY9n8AsuMaALBrKob9/4033oiHH344pk+fHkuXLo21a9dG06ZNY/fdd48DDjggjj766Bg9enTss88+dT4XQLEo9P1/8eLF8eSTT8af/vSnKCsrizVr1sT69eujdevW0alTp9hzzz1jyJAhccwxx8SQIUOipKSkzmsBKEQffPBBLFu2rE499tprr2jdunWtjnnxxRfj6quvjldffTU6dOgQZ5xxRlx//fXRokWLOs0SEbF06dK46667Yvr06bFgwYLYuHFjdOjQIQ444IA4/vjj45vf/GZ06dKlzudpDIIVAAAAAAAAAAAJ/JkRAAAAAAAAAEACwQoAAAAAAAAAgASCFQAAAAAAAAAACQQrAAAAAAAAAAASCFYAAAAAAAAAACQQrAAAAAAAAAAASCBYAQAAAAAAAACQQLACAAAAAAAAACCBYAUAAAAAAAAAQALBCgAAAAAAAACABIIVAAAAAAAAAAAJBCsAAAAAAAAAABIIVgAAAAAAAAAAJBCsAAAAAAAAAABIIFgBAAAAAAAAAJBAsAIAAAAAAAAAIIFgBQAAAAAAAABAAsEKAAAAAAAAAIAEghUAAAAAAAAAAAkEKwAAAAAAAAAAEghWAAAAAAAAAAAkEKwAAAAAAAAAAEggWAEAAAAAAAAAkECwAgAAAAAAAAAggWAFAAAAAAAAAEACwQoAAAAAAAAAgASCFQAAAAAAAAAACQQrAAAAAAAAAAASCFYAAAAAAAAAACQQrAAAAAAAAAAASCBYAQAAAAAAAACQQLACAAAAAAAAACCBYAUAAAAAAAAAQALBCgAAAAAAAACABIIVAAAAAAAAAAAJBCsAAAAAAAAAABIIVgAAAAAAAAAAJBCsAAAAAAAAAABIIFgBAAAAAAAAAJBAsAIAAAAAAAAAIIFgBQAAAAAAAABAgv8P23iyDsigTPsAAAAASUVORK5CYII=",
      "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": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"DLRover-Lynx without Graph Rewrite\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(13.333333333333346, 0.5, 'Time (s)')"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcdf3c1ad20>"
      ]
     },
     "execution_count": 17,
     "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, 0.95),\n",
    "    fontsize=g_label_fontsize - 2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "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
}