{
 "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": {
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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": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFU\n",
    "data_a = {\n",
    "    labels_name_a[0]: [43.0, 44.4],  # modela 8DP\n",
    "    labels_name_a[1]: [42.2, 43.6],  # modela 8TP\n",
    "    labels_name_a[2]: [43.1, 44.6],  # modela 2DP 4TP\n",
    "    labels_name_a[3]: [41.0, 42.6],  # modela 2DP 8TP\n",
    "    labels_name_a[4]: [44.7, 44.7],  # modela 4DP 2TP\n",
    "    labels_name_a[5]: [48.1, 49.4],  # modelb 8TP\n",
    "    labels_name_a[6]: [38.0, 38.0],  # modelb 4DP 2TP\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"DLRover-Lynx without Local Search\", \"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, 'MFU (%)')"
      ]
     },
     "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, 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5552dc7350>"
      ]
     },
     "execution_count": 8,
     "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, 0.9),\n",
    "    fontsize=g_label_fontsize - 2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2100x840 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "fig"
   ]
  }
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