{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 337,
   "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": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 339,
   "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": 340,
   "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": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"OR-Tools\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "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": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time (s)')"
      ]
     },
     "execution_count": 343,
     "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": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f08d74480e0>"
      ]
     },
     "execution_count": 344,
     "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": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x1050 with 2 Axes>"
      ]
     },
     "execution_count": 345,
     "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
}