paper_note/lynx/subgraph_write.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": {
      "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"ModelC\\n8TP$\\\\times$8EP\",\n",
    "    \"ModelC\\n2DP$\\\\times$8TP$\\\\times$8EP\",\n",
    "    \"ModelD\\n8TP$\\\\times$8EP\",\n",
    "    \"ModelD\\n2DP$\\\\times$8TP$\\\\times$8EP\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Lynx without Graph Rewrite\", \"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(13.333333333333332, 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, 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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7b08f5e510>"
      ]
     },
     "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, 0.95),\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 2100x700 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "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
}