paper_note/lynx/or_tools.ipynb

{
 "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]: [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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"ILP\", \"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(0, 0.5, 'Time (s)')"
      ]
     },
     "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)\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f50c31998b0>"
      ]
     },
     "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, 0.98),\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x840 with 2 Axes>"
      ]
     },
     "execution_count": 9,
     "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",
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