paper_note/lynx/subgraph_write.ipynb

{
 "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": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 14 / 6), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"ModelC\\n1DP$\\\\times$8TP$\\\\times$8EP\",\n",
    "    \"ModelC\\n2DP$\\\\times$8TP$\\\\times$8EP\",\n",
    "    \"ModelD\\n1DP$\\\\times$8TP$\\\\times$8EP\",\n",
    "    \"ModelD\\n2DP$\\\\times$8TP$\\\\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",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Lynx without Graph Rewrite\", \"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(13.333333333333332, 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, 3500)\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 0x7f0222abb9e0>"
      ]
     },
     "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,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6PProoxs9dtq0afGvf/0rIiIuueSSCtfy1VdfFZ7gRkScf/75Gz2mW7ducdFFFxXZdsMNN8Qnn3xS4v7z5s2L8847r/D5kCFDYuutt97gHO3bt49BgwZttJYCw4cPj7fffjsiolT3GDz00EOLPF+9enXcddddpZ5vQ+bOnVtsW9OmTTMyNgAl01v1VgAyR1/dtPtqVdl8881L3L5w4cIqrgQg+/RWvTUT9FbILMEKgBqipJPgG2+8MZIk2eBxt912W6xevTp69OgRBx98cIXruPbaa2PVqlUREVGnTp1iJ5jpXHDBBUVS1mvWrInrr7++xH1vuOGGmD17duHzs88+u1Rz9OvXr1T7RUTceuutEbH25HKXXXbZ6P7t2rUrtu0///lPqefbkEWLFhXb1qhRo4yMDUB6euvG6a0AlJa+unE1ta9WlebNm5e4fdmyZVVbCEA1obdunN66YXorZFbdje8CQHWw8847xyGHHBIvvvhi4baJEyfGyJEj45hjjinxmIULF8bf/va3iIj43e9+V+Ea5s2bV5h2jojo2rVr2suxra9169Zx6KGHxqhRowq3PfHEE3HXXXdFy5YtC7ctXbo07rvvvsLn7dq1i2233bZUc5R2v2+//TY++uijiFh7Qn/88ceX6rj1ff7557FmzZqoU6dOuY4vkJNTPOe4/qX+AMg8vXXj9FYASktf3bia2leryuLFi0vc3rBhwyquBKB60Fs3Tm/dML0VMsv/WQKoQS655JIiJ9IREddff33aE+m//vWvsXjx4ujUqVOxS8eVx6hRo4rcx2+rrbYq0/Hrn0ivXr063nzzzTj66KOLzDF//vzC57vttlupx0+lUqXab8yYMYVfL1iwIIYPH17qOdaVJEnMmzcvtthii3IdX6BFixbFtkkNA1QNvXXD9FYAykJf3bCa2leryo8//lji9ppSP0Bl0Fs3TG/dML0VMsutQABqkP333z923333Itvef//9eOONN4rtu3Llyrj99tsjIuK3v/1tRv6V5ptvvlnkeePGjct0/K677lps24cffljk+SuvvFLkeWWc5I0dO7bw6z333DOSJCn3IxP1rZvQLrBkyZIKjwvAxumtmaG3AhChr2ZKdeurVWXevHklbu/SpUsVVwJQfeitmaG3FqW3QvkIVgDUMCVdwq2ke9M9+uijMXPmzGjRokWceeaZGZn7iy++KPI8Pz+/TMfvvPPOxbbNmTOnyPP333+/yPPNNtusTHOUxty5cwu/XrBgQcbHL6uSLlk3bdq0LFQCUDvprRWntwJQQF+tuOrWV6vK+PHjS9xeln+5DLAp0lsrTm8tSm+F8hGsAKhhBg0aFFtvvXWRbS+88EJ8+umnhc+TJImbbropIiJ+9atflTlJnM76lw4r67/8bNKkSdSrV2+DY8yePbvI83UvNZcp6yZ1Z82aFUmSZHyOsvjpT39abNuyZcti5syZWagGoPbRWytObwWggL5acdWtr1aV9f9VdMTaf7XcvXv3LFQDUH3orRWnt/6P3grlJ1gBUMPUqVMnLrzwwmLbb7jhhsKvn3vuufjyyy+jYcOGcf7552d07nWtm/QtrfUTx82aNSvyfP3Lky1cuLDMc5TFwoULY9KkSZU6x8bstddeJW5fP60NQOXQWzNLbwWo3fTVzKoOfbUqJElS4mXtBwwYEDk5/hc2ULvprZmlt+qtUF7+5ADUQKeffnq0atWqyLbhw4fHlClTIiLixhtvjIiIX/ziF9G6deuMzbv+/eO++uqrWLNmTZnGaNiwYZHnXbt2LfI8Nze3yPPvvvuuTOOXRosWLYo8f/XVVzM+R1lsueWWsffeexfbXlKiGIDKobdWjN4KwLr01Yqpbn21Kvz73/+O77//vtj2s88+OwvVAFQ/emvF6K3/o7dC+QlWANRAjRo1ivPOO6/IttWrV8fNN98cH3zwQbz55puRk5MTF198cUbn3W677Yo8z8vLi4kTJ5ZpjKVLlxZ5vsceexR5vvnmmxd5/sknn8Tq1avLNEeBdJd0a9u2bZHnf/3rX8s1/rBhw2LFihXlOnZ9v/zlL4tte/LJJ2vNZekAsk1vLR29FYDS0FdLpyb11cq27r+6LnDAAQdEr169slANQPWjt5aO3vo/eitknmAFQA113nnnFUvz/uMf/4jLL788IiKOOuqo2HbbbTM6Z9++fYttGz16dKmPX7NmTSxevLjw+WabbVbsUt077rhjkefLly+Pl19+uYyVrpWfn1/i9j333LPI888++ywef/zxMo398ccfx+233x4NGjQoV23rO/bYY6NDhw5Ftk2bNi1efPHFjIy/rh9//LHEE2uA2k5v3Ti9tWR6K0Bx+urG1aS+WplGjhwZY8aMKbKtbt26cdNNN2WpIoDqSW/dOL11Lb0VKodgBUANtfnmm8fpp59eZNuyZcsKTzovueSSjM/Zv3//aNKkSZFtI0aMKPXxU6ZMKZI2PvHEE6NevXpF9unXr1+x4+69996yFfr/W7VqVYnb+/btG6lUqsi2c845J6ZNm1aqcZMkiYsvvjgOOOCAYuOUV4MGDeL2228vtn3o0KEZGb9AkiRxyimnxMqVKzM6LsCmQG/dOL21OL0VoGT66sbVpL5aWaZMmRKnnXZase1XXnll7LLLLlVfEEA1prdunN6qt0JlEqwAqEYKErWlvUT1RRddFHXq1Cm2fZ999onevXuXer51bWjuZs2aFTspGzNmTHz00UelqDZi7NixhV/XqVMnzj///GL7HHfcccVOrkeOHFmmJHSB9S8zV6Bt27bRv3//ItvmzZsX/fr1K7wv4YZcc8018eqrr8ZZZ51V5po2ZODAgcXqeu+998r9QaIk11xzTUyfPj1+//vfZ2xMgOpMb9Vb9VaAzNFXa3dfjUj/L4HL44cffoiBAwfGggULimwfMGBAXHHFFRmbB6A601v1Vr0Vag7BCoBqpOD+bMuXLy/V/p07d45jjz222Pbf/e53ZZpvXRub+49//GO0atWqyLarrrqqVPM99dRThV+fccYZxe7RFxGx5ZZbxkknnVRs+ymnnBJffvnlBsf/9ttvizyfPn162g8Gl156abFt3333Xey6665x7733lvivTufNmxdDhgyJq666Kvbdd9846KCD0tayZs2aIs9Le4L8wAMPROfOnYtsu/DCC+ODDz4o1fEb8tBDD8Udd9wRTz31VLEPKwCbKr1Vb9VbATJHX9VXS7v2G/PNN99Enz594uOPPy6y/fDDD4/hw4dHTo7/bQ3UDnqr3qq3Qg2SAFBt/PznP08iItl7771Lfcy4ceOSiCh8dO/ePcnPzy/VsV9//XWRYyMiad++/UaPe+KJJ5JUKlXkuPvvv3+Dx4wfPz6pU6dOEhHJNttskyxcuDDtvnPmzEnatWtXrLbWrVsnzz33XLH916xZk9x6661J3bp1ix3zxBNPJEmSJIsWLSo2529/+9ti+xc8WrZsmQwePDj53e9+l1x88cXJEUcckTRq1CiJiCQ3NzeZMGHCBl/vk08+WWS8Hj16bHD/dU2YMCFp3bp1keObN2+evPnmm6UeY31/+9vfkqZNmyavv/56uccAqIn01rX0Vr0VIBP01bVqc1+dNGlSsVr69u1bqmOTJElWrlyZ3HrrrUmLFi2KjJFKpZILL7wwWblyZanHAtgU6K1r6a16K9QEghUA1cTKlSuTjh07JhGRNG7cOFm8eHGpjz3wwAMLT5j+8Y9/lPq4v/71ryWeRE6aNGmjx956661Fjqlfv37y2GOPlbjv5MmTky5duhSeqE+cOHGj47/55ptJbm5uifXttddeyfXXX588+OCDydChQ5Ptt98+ycnJSX7xi18U2zcnJyfp2LFj0qJFi2T06NFF5li5cmVyyCGHpD2ZLumRSqWS//u//9to/ZdffnmR4xo3bpwsW7Zso8cVmDhxYrLNNtsUGaNOnTrJH//4x2Tp0qWlHmfp0qXJueeemzRv3jx5++23S30cwKZAby1Kb9VbASpCXy2qtvbVu+66q9icO+ywQ/Lxxx9v8C9uJkyYkFx33XXFenFEJLvttlvyxhtvbHRugE2N3lqU3qq3QnUnWAFQDUyaNCnp379/kZOfgw8+eKMp2AL/+c9/Ck9S8/LyNrr/8uXLk0cffTRp2rRpiSeLO++8czJmzJhkzZo1Gxzn3nvvTZo0aVLk2MMPPzwZPnx4Mnbs2OSNN95ILr/88sJ59tprr2Tq1Kmlek1JkiTvvPNO0qpVq42e3Obm5ib/+te/ktdee63Y9zp27JjceOONyYIFC0qcY+XKlSWegJf0qFu3bvLII49stO6333472WyzzYod//Of/zxtHSVZuHBh8otf/KJYGrxt27bJVVddlXz55Zdpj/3uu++S6667Lmnbtm2y1157Jd99912p5wXYFOitJdNb9VaA8tBXS1ab+urKlSuTxx57LGncuHHa+evVq5d069YtOfDAA5NBgwYlhx9+eNKzZ8+kbdu2xfZt1KhRcvTRRyejR48u9b+yBtiU6K0l01v1VqjOBCsAsmTChAlJmzZt0p7MFjyaNGmStGnTJnnhhRc2ON6uu+6aXH/99Rvc59RTT0222GKLEi+RVtKjYcOGSZs2bZL77rsv7ZiTJ09OTjnllA2+jp49eyaPPvroRk/MSzJ37tzkrLPOKrzs2rqPnJyc5Mgjj0y+/fbbJEmSIifSvXv3ToYPH56sXr26VPOMHj062WOPPUqsP5VKJQcffHDy2WefbXCMa665JmnWrNkGf6Y5OTlJmzZtkhEjRpT6Z/D+++8nRx11VOGl89Z9tGzZMtl7772TAQMGJAMHDkz233//ZMstt0wi1l4G8J///Ge5fu4ANZHeWjp6q94KUBr6auls6n118ODBSZcuXdL+C+INPerUqZM0btw4adu2bbLbbrslxxxzTHL11Vcnzz//fJmuFgWwqdBbS0dv1VuhukolSZIEAFTQ8uXLY8yYMTFt2rSYNWtW1K9fP7baaqv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      "text/plain": [
       "<Figure size 2100x700 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",
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