{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8251ed25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "\n",
    "# copied from the counts folder\n",
    "datasets = {\n",
    "    \"ShanghaiTech A\": {\n",
    "        \"0\": 182715307,\n",
    "        \"1\": 25282990,\n",
    "        \"2\": 3723241,\n",
    "        \"3\": 1075878,\n",
    "        \"4\": 428683,\n",
    "        \"5\": 212379,\n",
    "        \"6\": 116019,\n",
    "        \"7\": 66336,\n",
    "        \"8\": 38808,\n",
    "        \"9\": 22851,\n",
    "        \"10\": 13845,\n",
    "        \"11\": 8410,\n",
    "        \"12\": 5248,\n",
    "        \"13\": 3355,\n",
    "        \"14\": 1997,\n",
    "        \"15\": 1309,\n",
    "        \"16\": 818,\n",
    "        \"17\": 451,\n",
    "        \"18\": 238,\n",
    "        \"19\": 113,\n",
    "        \"20\": 65,\n",
    "        \"21\": 31,\n",
    "        \"22\": 16,\n",
    "        \"23\": 8,\n",
    "        \"24\": 8,\n",
    "        \"25\": 4,\n",
    "        \"26\": 2,\n",
    "        \"27\": 1,\n",
    "        \"28\": 1\n",
    "    },\n",
    "    \"ShanghaiTech B\": {\n",
    "        \"0\": 294072360,\n",
    "        \"1\": 8559657,\n",
    "        \"2\": 922301,\n",
    "        \"3\": 225017,\n",
    "        \"4\": 75708,\n",
    "        \"5\": 29428,\n",
    "        \"6\": 12533,\n",
    "        \"7\": 6347,\n",
    "        \"8\": 3429,\n",
    "        \"9\": 1869,\n",
    "        \"10\": 913,\n",
    "        \"11\": 494,\n",
    "        \"12\": 338,\n",
    "        \"13\": 202,\n",
    "        \"14\": 192,\n",
    "        \"15\": 11,\n",
    "        \"16\": 1\n",
    "    },\n",
    "    \"UCF-QNRF\": {\n",
    "        \"0\": 8451989906,\n",
    "        \"1\": 189577152,\n",
    "        \"2\": 19349594,\n",
    "        \"3\": 4927861,\n",
    "        \"4\": 1576908,\n",
    "        \"5\": 556543,\n",
    "        \"6\": 226325,\n",
    "        \"7\": 104066,\n",
    "        \"8\": 51679,\n",
    "        \"9\": 24615,\n",
    "        \"10\": 11391,\n",
    "        \"11\": 4737,\n",
    "        \"12\": 2041,\n",
    "        \"13\": 921,\n",
    "        \"14\": 450,\n",
    "        \"15\": 236,\n",
    "        \"16\": 120,\n",
    "        \"17\": 54,\n",
    "        \"18\": 20,\n",
    "        \"19\": 4,\n",
    "        \"20\": 2\n",
    "    },\n",
    "    \"NWPU-Crowd\": {\n",
    "        \"0\": 28037670189,\n",
    "        \"1\": 262823214,\n",
    "        \"2\": 20627370,\n",
    "        \"3\": 4559000,\n",
    "        \"4\": 1377101,\n",
    "        \"5\": 488567,\n",
    "        \"6\": 205419,\n",
    "        \"7\": 94266,\n",
    "        \"8\": 44455,\n",
    "        \"9\": 22605,\n",
    "        \"10\": 11771,\n",
    "        \"11\": 6703,\n",
    "        \"12\": 3736,\n",
    "        \"13\": 2433,\n",
    "        \"14\": 1408,\n",
    "        \"15\": 680,\n",
    "        \"16\": 271,\n",
    "        \"17\": 89,\n",
    "        \"18\": 41,\n",
    "        \"19\": 10,\n",
    "        \"20\": 3,\n",
    "        \"21\": 2\n",
    "    }\n",
    "}\n",
    "data_by_value = defaultdict(dict)\n",
    "\n",
    "colors = {\n",
    "    \"ShanghaiTech A\": \"#4477AA\",\n",
    "    \"ShanghaiTech B\": \"#EE6677\",\n",
    "    \"UCF-QNRF\": \"#228833\",\n",
    "    \"NWPU-Crowd\": \"#AA3377\"\n",
    "}\n",
    "\n",
    "\n",
    "for name, data in datasets.items():\n",
    "    total = sum(data.values())\n",
    "    counts = {k: 0 for k in range(4)}\n",
    "    counts[\"≥4\"] = 0\n",
    "    for k, v in data.items():\n",
    "        if int(k) < 4:\n",
    "            counts[int(k)] += v\n",
    "        else:\n",
    "            counts[\"≥4\"] += v\n",
    "    for k in counts:\n",
    "        data_by_value[k][name] = counts[k] / total * 100  # 转为百分比\n",
    "\n",
    "# 准备绘图数据\n",
    "x_labels = [0, 1, 2, 3, \"≥4\"]\n",
    "datasets_order = list(datasets.keys())\n",
    "bar_width = 0.2\n",
    "x = list(range(len(x_labels)))\n",
    "\n",
    "# 创建图形\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 8), dpi=200)\n",
    "\n",
    "# 绘图\n",
    "for i, dataset in enumerate(datasets_order):\n",
    "    y_vals = [data_by_value[label][dataset] for label in x_labels]\n",
    "    x_positions = [val + i * bar_width for val in x]\n",
    "    bars = ax.bar(x_positions, y_vals, width=bar_width, label=dataset, edgecolor='black', color=colors[dataset])\n",
    "    \n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        text = f'{height:.1f}' if height > 1.0 else f'{height:.2f}'\n",
    "        ax.annotate(\n",
    "            text,\n",
    "            xy=(bar.get_x() + bar.get_width() / 2, height),\n",
    "            xytext=(0, 2),\n",
    "            textcoords=\"offset points\",\n",
    "            ha='center',\n",
    "            va='bottom',\n",
    "            fontsize=18,\n",
    "            weight=\"medium\",\n",
    "            color=colors[dataset],\n",
    "        )\n",
    "\n",
    "# 设置坐标轴、标题等\n",
    "ax.set_xlabel('Blockwise Count Value', fontsize=25)\n",
    "ax.set_ylabel('Percentage of Blocks', fontsize=25)\n",
    "ax.set_yscale('log')\n",
    "\n",
    "x = list(range(len(x_labels)))  # [0, 1, 2, 3, 4]\n",
    "x_positions = [x_val + i * bar_width for x_val in x]\n",
    "# 设置紧凑型横轴\n",
    "ax.set_xticks([r + bar_width * 1.5 for r in x])   # 保持标签居中\n",
    "ax.set_xticklabels(x_labels)\n",
    "\n",
    "# 缩小左右空隙\n",
    "left_margin = x[0] - bar_width * 0.75             # 起始位置\n",
    "right_margin = x[-1] + bar_width * (len(datasets_order) - 0.25)  # 结束位置\n",
    "ax.set_xlim(left_margin, right_margin)\n",
    "\n",
    "# ax.set_xticks([r + bar_width * 1.5 for r in x])\n",
    "# ax.set_xticklabels(x_labels)\n",
    "\n",
    "ax.tick_params(axis=\"both\", which=\"major\", labelsize=22.5)\n",
    "ax.tick_params(axis=\"both\", which=\"minor\", labelsize=22.5)\n",
    "\n",
    "ax.legend(title='Dataset', title_fontsize=25, fontsize=22.5)\n",
    "ax.grid(axis='y', linestyle='--', alpha=0.8)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"count_distribution.png\", dpi=300, bbox_inches='tight', pad_inches=0.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d594a601",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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