{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d90c5585-73a8-4416-a846-3068e6f43525",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT\\RTG_Test...\n",
      "Loaded 14 RTG experiments.\n",
      "[INFO] Saved Fig2_RTG_Analysis_Revised.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# Set style for academic paper\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.family'] = 'serif' # Times New Roman style often preferred in papers\n",
    "plt.rcParams['font.size'] = 11\n",
    "\n",
    "def save_plot(filename):\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "    print(f\"[INFO] Saved {filename}\")\n",
    "\n",
    "def load_rtg_data(rtg_dir):\n",
    "    data = []\n",
    "    # Find all json files in RTG_Test folder\n",
    "    files = glob.glob(os.path.join(rtg_dir, \"*.json\"))\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        # Expected format: 20_{RTG}_E_1_DT_S_100.json\n",
    "        match = re.search(r\"20_(\\d+)_E_\", filename)\n",
    "        if match:\n",
    "            rtg_val = int(match.group(1))\n",
    "            with open(f, 'r') as json_file:\n",
    "                content = json.load(json_file)\n",
    "                win_rate = content.get(\"win_rate\", 0)\n",
    "                avg_reward = content.get(\"avg_reward\", 0)\n",
    "                \n",
    "                data.append({\n",
    "                    \"RTG\": rtg_val,\n",
    "                    \"WinRate\": win_rate,\n",
    "                    \"AvgReward\": avg_reward\n",
    "                })\n",
    "                \n",
    "    # Sort by RTG ascending\n",
    "    data = sorted(data, key=lambda x: x[\"RTG\"])\n",
    "    return data\n",
    "\n",
    "def plot_rtg_analysis(data):\n",
    "    fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "    rtg_values = [f\"RTG {d['RTG']}\" for d in data]\n",
    "    rtg_rewards = [d['AvgReward'] for d in data]\n",
    "    rtg_wins = [d['WinRate'] for d in data]\n",
    "\n",
    "    x = np.arange(len(rtg_values))\n",
    "    width = 0.5\n",
    "    \n",
    "    # Dual Axis Plot\n",
    "    # Bar for Reward\n",
    "    color1 = 'tab:blue'\n",
    "    bars = ax1.bar(x, rtg_rewards, width, label='Avg. Reward', color=color1, alpha=0.7, edgecolor='black')\n",
    "    ax1.set_xlabel('Target Return (RTG)', fontweight='bold')\n",
    "    ax1.set_ylabel('Average Reward', color=color1, fontweight='bold')\n",
    "    ax1.tick_params(axis='y', labelcolor=color1)\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(rtg_values, rotation=45, ha='right')\n",
    "    \n",
    "    # Set y-axis limit for reward (dynamically or statically)\n",
    "    ax1.set_ylim(0, max(rtg_rewards) + 10)\n",
    "\n",
    "    # Line for Win Rate\n",
    "    ax2 = ax1.twinx()\n",
    "    color2 = 'tab:red'\n",
    "    ax2.plot(x, rtg_wins, color=color2, marker='o', linewidth=3, label='Win Rate (%)')\n",
    "    ax2.set_ylabel('Win Rate (%)', color=color2, fontweight='bold')\n",
    "    ax2.tick_params(axis='y', labelcolor=color2)\n",
    "    ax2.set_ylim(-5, 105)\n",
    "\n",
    "    # Annotations\n",
    "    for i, v in enumerate(rtg_rewards):\n",
    "        ax1.text(i, v + 1, f'{v:.1f}', ha='center', color='black', fontsize=9)\n",
    "    \n",
    "    for i, v in enumerate(rtg_wins):\n",
    "        ax2.text(i, v + 5, f'{int(v)}%', ha='center', color=color2, fontweight='bold', fontsize=9)\n",
    "\n",
    "    plt.title(\"Impact of Returns-To-Go (RTG) on Performance (Including 70~150)\", fontsize=14, pad=15)\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    output_filename = 'Fig2_RTG_Analysis_Revised.png'\n",
    "    save_plot(output_filename)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Path to the RTG_Test folder\n",
    "    rtg_dir = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT\\RTG_Test\"\n",
    "    print(f\"Loading data from {rtg_dir}...\")\n",
    "    \n",
    "    data = load_rtg_data(rtg_dir)\n",
    "    \n",
    "    if not data:\n",
    "        print(\"No RTG data found. Please check the directory path.\")\n",
    "    else:\n",
    "        print(f\"Loaded {len(data)} RTG experiments.\")\n",
    "        plot_rtg_analysis(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "753b7350-cabe-46c4-a54d-d7289773919b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT\\RTG_Test...\n",
      "Loaded 14 RTG experiments.\n",
      "[INFO] Saved Fig2_RTG_Analysis_Revised.png\n",
      "[INFO] Saved Fig3_RTG_Boxplot.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Set style for academic paper\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.family'] = 'serif' # Times New Roman style often preferred in papers\n",
    "plt.rcParams['font.size'] = 11\n",
    "\n",
    "def save_plot(fig, filename):\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "    print(f\"[INFO] Saved {filename}\")\n",
    "\n",
    "def load_rtg_data(rtg_dir):\n",
    "    data = []\n",
    "    # Find all json files in RTG_Test folder\n",
    "    files = glob.glob(os.path.join(rtg_dir, \"*.json\"))\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        # Expected format: 20_{RTG}_E_1_DT_S_100.json\n",
    "        match = re.search(r\"20_(\\d+)_E_\", filename)\n",
    "        if match:\n",
    "            rtg_val = int(match.group(1))\n",
    "            with open(f, 'r') as json_file:\n",
    "                content = json.load(json_file)\n",
    "                win_rate = content.get(\"win_rate\", 0)\n",
    "                avg_reward = content.get(\"avg_reward\", 0)\n",
    "                reward_std = content.get(\"reward_std\", 0)\n",
    "                all_rewards = content.get(\"all_rewards\", [])\n",
    "                \n",
    "                data.append({\n",
    "                    \"RTG\": rtg_val,\n",
    "                    \"WinRate\": win_rate,\n",
    "                    \"AvgReward\": avg_reward,\n",
    "                    \"RewardStd\": reward_std,\n",
    "                    \"AllRewards\": all_rewards\n",
    "                })\n",
    "                \n",
    "    # Sort by RTG ascending\n",
    "    data = sorted(data, key=lambda x: x[\"RTG\"])\n",
    "    return data\n",
    "\n",
    "def plot_rtg_analysis(data):\n",
    "    fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "    rtg_values = [str(d['RTG']) for d in data]\n",
    "    rtg_rewards = [d['AvgReward'] for d in data]\n",
    "    rtg_std = [d['RewardStd'] for d in data]\n",
    "    rtg_wins = [d['WinRate'] for d in data]\n",
    "\n",
    "    x = np.arange(len(rtg_values))\n",
    "    width = 0.5\n",
    "    \n",
    "    # Dual Axis Plot\n",
    "    # Bar for Reward with Error Bars (Standard Deviation)\n",
    "    color1 = 'tab:blue'\n",
    "    bars = ax1.bar(x, rtg_rewards, width, label='Avg. Reward', color=color1, alpha=0.7, \n",
    "                   edgecolor='black', yerr=rtg_std, capsize=5)\n",
    "    ax1.set_xlabel('Target Return (RTG)', fontweight='bold')\n",
    "    ax1.set_ylabel('Average Reward', color=color1, fontweight='bold')\n",
    "    ax1.tick_params(axis='y', labelcolor=color1)\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(rtg_values, rotation=45, ha='right')\n",
    "    \n",
    "    # Set y-axis limit for reward (dynamically based on max reward + std)\n",
    "    max_val = max([r + s for r, s in zip(rtg_rewards, rtg_std)]) if rtg_rewards else 60\n",
    "    ax1.set_ylim(0, max_val + 10)\n",
    "\n",
    "    # Line for Win Rate\n",
    "    ax2 = ax1.twinx()\n",
    "    color2 = 'tab:red'\n",
    "    ax2.plot(x, rtg_wins, color=color2, marker='o', linewidth=3, label='Win Rate (%)')\n",
    "    ax2.set_ylabel('Win Rate (%)', color=color2, fontweight='bold')\n",
    "    ax2.tick_params(axis='y', labelcolor=color2)\n",
    "    ax2.set_ylim(-5, 105)\n",
    "\n",
    "    # Annotations (Average values)\n",
    "    for i, (v, s) in enumerate(zip(rtg_rewards, rtg_std)):\n",
    "        # Displaying average slightly below the top of the bar to avoid overlap with error bars\n",
    "        ax1.text(i, v/2, f'{v:.1f}', ha='center', color='white', fontweight='bold', fontsize=9)\n",
    "    \n",
    "    for i, v in enumerate(rtg_wins):\n",
    "        ax2.text(i, v + 5, f'{int(v)}%', ha='center', color=color2, fontweight='bold', fontsize=9)\n",
    "\n",
    "    #plt.title(\"Impact of Returns-To-Go (RTG) on Performance\", fontsize=14, pad=15)\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    output_filename = 'Fig2_RTG_Analysis_Revised.png'\n",
    "    save_plot(fig, output_filename)\n",
    "    plt.close(fig)\n",
    "\n",
    "def plot_rtg_boxplot(data):\n",
    "    # Flatten data for seaborn boxplot\n",
    "    box_data = []\n",
    "    for d in data:\n",
    "        for r in d['AllRewards']:\n",
    "            box_data.append({'Target Return (RTG)': str(d['RTG']), 'Reward': r})\n",
    "            \n",
    "    df = pd.DataFrame(box_data)\n",
    "    \n",
    "    if df.empty:\n",
    "        print(\"[WARNING] No 'all_rewards' data found for boxplot.\")\n",
    "        return\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    \n",
    "    # Create boxplot to show median, IQR, and outliers\n",
    "    sns.boxplot(data=df, x='Target Return (RTG)', y='Reward', ax=ax, hue='Target Return (RTG)', palette='Blues', showfliers=True, legend=False)\n",
    "    \n",
    "    #ax.set_title(\"Distribution of Rewards Across Target RTG Settings (IQR & Variance)\", fontsize=14, pad=15)\n",
    "    ax.set_ylabel(\"Cumulative Reward\", fontweight='bold')\n",
    "    ax.set_xlabel(\"Target Return (RTG)\", fontweight='bold')\n",
    "    ax.tick_params(axis='x', rotation=45)\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_horizontalalignment('right')\n",
    "    \n",
    "    # Draw a reference line indicating the typical maximum reward or threshold\n",
    "    # Assuming ~63-64 is the max possible reward when 20 enemies are killed\n",
    "    ax.axhline(y=63.5, color='r', linestyle='--', alpha=0.5, label='Estimated Max Reward (All Targets Cleared)')\n",
    "    ax.legend(loc='lower right')\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    output_filename = 'Fig3_RTG_Boxplot.png'\n",
    "    save_plot(fig, output_filename)\n",
    "    plt.close(fig)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Path to the RTG_Test folder\n",
    "    rtg_dir = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT\\RTG_Test\"\n",
    "    print(f\"Loading data from {rtg_dir}...\")\n",
    "    \n",
    "    data = load_rtg_data(rtg_dir)\n",
    "    \n",
    "    if not data:\n",
    "        print(\"No RTG data found. Please check the directory path.\")\n",
    "    else:\n",
    "        print(f\"Loaded {len(data)} RTG experiments.\")\n",
    "        plot_rtg_analysis(data)\n",
    "        plot_rtg_boxplot(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7096116f-7090-48e6-8319-573e4f6c24c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\EnemyCount10...\n",
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\EnemyCount15...\n",
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\EnemyCount20...\n",
      "\n",
      "=== Statistical Analysis Results (All Difficulties) ===\n",
      "| Difficulty   | Metric                         | PPO Mean ± SD     | DT_S_100 Mean ± SD   |   ANOVA p-value |   T-test p-value (PPO vs DT_S_100) | Significant? (p < 0.05)   |\n",
      "|:-------------|:-------------------------------|:------------------|:---------------------|----------------:|-----------------------------------:|:--------------------------|\n",
      "| Enemy 10     | Success Rate (Wins)            | 0.980 ± 0.141     | 1.000 ± 0.000        |      0.01015    |                          0.3222    | No                        |\n",
      "| Enemy 10     | Cumulative Reward              | 31.552 ± 0.877    | 32.696 ± 0.283       |      0.0002402  |                          2.741e-12 | Yes                       |\n",
      "| Enemy 10     | Control Stability (Smoothness) | 0.369 ± 0.043     | 0.147 ± 0.062        |      7.819e-143 |                          1.197e-35 | Yes                       |\n",
      "| Enemy 10     | Shooting Accuracy              | 3.758 ± 1.460     | 25.080 ± 8.475       |      8.66e-29   |                          1.854e-23 | Yes                       |\n",
      "| Enemy 10     | Time to Kill (Steps)           | 357.480 ± 168.475 | 214.160 ± 29.366     |      1.497e-21  |                          2.524e-07 | Yes                       |\n",
      "| Enemy 15     | Success Rate (Wins)            | 0.960 ± 0.198     | 1.000 ± 0.000        |      1.051e-07  |                          0.1594    | No                        |\n",
      "| Enemy 15     | Cumulative Reward              | 46.473 ± 1.209    | 48.233 ± 0.478       |      4.246e-06  |                          5.736e-14 | Yes                       |\n",
      "| Enemy 15     | Control Stability (Smoothness) | 0.377 ± 0.045     | 0.204 ± 0.162        |      5.525e-72  |                          1.311e-09 | Yes                       |\n",
      "| Enemy 15     | Shooting Accuracy              | 5.054 ± 1.847     | 22.936 ± 6.495       |      3.54e-36   |                          5.435e-26 | Yes                       |\n",
      "| Enemy 15     | Time to Kill (Steps)           | 390.200 ± 173.051 | 267.580 ± 57.441     |      5.059e-34  |                          1.295e-05 | Yes                       |\n",
      "| Enemy 20     | Success Rate (Wins)            | 0.960 ± 0.198     | 1.000 ± 0.000        |      1.249e-06  |                          0.1594    | No                        |\n",
      "| Enemy 20     | Cumulative Reward              | 61.373 ± 1.520    | 63.573 ± 0.377       |      1.821e-07  |                          7.099e-14 | Yes                       |\n",
      "| Enemy 20     | Control Stability (Smoothness) | 0.372 ± 0.033     | 0.202 ± 0.092        |      1.463e-110 |                          3.015e-18 | Yes                       |\n",
      "| Enemy 20     | Shooting Accuracy              | 5.541 ± 2.073     | 22.535 ± 4.873       |      3.345e-47  |                          6.242e-33 | Yes                       |\n",
      "| Enemy 20     | Time to Kill (Steps)           | 464.160 ± 174.620 | 309.000 ± 45.768     |      2.883e-37  |                          1.156e-07 | Yes                       |\n",
      "\n",
      "[INFO] Results saved to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Statistical_Analysis_Results_All.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n",
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n",
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "def parse_filename(filename):\n",
    "    # Matches PPO: 20_V12_PPO.json -> Model: PPO\n",
    "    if \"PPO\" in filename:\n",
    "        return \"PPO\"\n",
    "    \n",
    "    # Matches DT variants: 20_E_1_DT_S_100.json -> DT_S_100\n",
    "    dt_match = re.search(r\"DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return f\"DT_{dt_match.group(1)}_{dt_match.group(2)}\"\n",
    "    \n",
    "    return \"Unknown\"\n",
    "\n",
    "def load_data(folder_path):\n",
    "    files = glob.glob(os.path.join(folder_path, \"*.json\"))\n",
    "    data_dict = {}\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        model_name = parse_filename(filename)\n",
    "        \n",
    "        with open(f, 'r') as json_file:\n",
    "            try:\n",
    "                content = json.load(json_file)\n",
    "                # Ensure all required raw arrays exist\n",
    "                if all(k in content for k in [\"all_rewards\", \"all_wins\", \"all_steps\", \"all_smoothness\", \"all_accuracies\"]):\n",
    "                    data_dict[model_name] = {\n",
    "                        \"rewards\": np.array(content[\"all_rewards\"]),\n",
    "                        \"wins\": np.array(content[\"all_wins\"]),\n",
    "                        \"steps\": np.array(content[\"all_steps\"]),\n",
    "                        \"smoothness\": np.array(content[\"all_smoothness\"]),\n",
    "                        \"accuracies\": np.array(content[\"all_accuracies\"])\n",
    "                    }\n",
    "            except Exception as e:\n",
    "                print(f\"Error loading {f}: {e}\")\n",
    "                \n",
    "    return data_dict\n",
    "\n",
    "def run_statistical_tests(data_dict, baseline=\"PPO\", target=\"DT_S_100\"):\n",
    "    metrics = {\n",
    "        \"Success Rate (Wins)\": \"wins\",\n",
    "        \"Cumulative Reward\": \"rewards\",\n",
    "        \"Control Stability (Smoothness)\": \"smoothness\",\n",
    "        \"Shooting Accuracy\": \"accuracies\",\n",
    "        \"Time to Kill (Steps)\": \"steps\"\n",
    "    }\n",
    "    \n",
    "    results = []\n",
    "    \n",
    "    for metric_name, key in metrics.items():\n",
    "        arrays = [v[key] for k, v in data_dict.items()]\n",
    "        model_names = list(data_dict.keys())\n",
    "        \n",
    "        # 1. One-Way ANOVA across all models\n",
    "        f_stat, anova_p = stats.f_oneway(*arrays) if len(arrays) > 1 else (np.nan, np.nan)\n",
    "        \n",
    "        # 2. Independent T-Test (Baseline vs Target)\n",
    "        t_p_val = np.nan\n",
    "        t_stat = np.nan\n",
    "        if baseline in data_dict and target in data_dict:\n",
    "            base_data = data_dict[baseline][key]\n",
    "            target_data = data_dict[target][key]\n",
    "            \n",
    "            # For success rate (binary 0 or 1), T-test is acceptable for large N,\n",
    "            # but formally proportions Z-test is better. We will use Welch's t-test for all continuous/binary.\n",
    "            t_stat, t_p_val = stats.ttest_ind(base_data, target_data, equal_var=False)\n",
    "            \n",
    "            base_mean = np.mean(base_data)\n",
    "            target_mean = np.mean(target_data)\n",
    "            base_std = np.std(base_data, ddof=1)\n",
    "            target_std = np.std(target_data, ddof=1)\n",
    "        else:\n",
    "            base_mean = target_mean = base_std = target_std = np.nan\n",
    "            \n",
    "        results.append({\n",
    "            \"Metric\": metric_name,\n",
    "            f\"{baseline} Mean ± SD\": f\"{base_mean:.3f} ± {base_std:.3f}\",\n",
    "            f\"{target} Mean ± SD\": f\"{target_mean:.3f} ± {target_std:.3f}\",\n",
    "            \"ANOVA p-value\": f\"{anova_p:.3e}\",\n",
    "            f\"T-test p-value ({baseline} vs {target})\": f\"{t_p_val:.3e}\",\n",
    "            \"Significant? (p < 0.05)\": \"Yes\" if t_p_val < 0.05 else \"No\"\n",
    "        })\n",
    "        \n",
    "    return pd.DataFrame(results)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    base_folder = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    difficulties = [\"EnemyCount10\", \"EnemyCount15\", \"EnemyCount20\"]\n",
    "    \n",
    "    all_results = []\n",
    "    \n",
    "    for diff in difficulties:\n",
    "        folder_path = os.path.join(base_folder, diff)\n",
    "        print(f\"Loading data from {folder_path}...\")\n",
    "        \n",
    "        data_dict = load_data(folder_path)\n",
    "        \n",
    "        if len(data_dict) < 2:\n",
    "            print(f\"Not enough models found in {diff}. Skipping.\")\n",
    "            continue\n",
    "            \n",
    "        # Run tests comparing PPO vs the best DT model (assuming DT_S_100)\n",
    "        df_results = run_statistical_tests(data_dict, baseline=\"PPO\", target=\"DT_S_100\")\n",
    "        df_results.insert(0, \"Difficulty\", diff.replace(\"EnemyCount\", \"Enemy \"))\n",
    "        \n",
    "        all_results.append(df_results)\n",
    "        \n",
    "    if all_results:\n",
    "        final_df = pd.concat(all_results, ignore_index=True)\n",
    "        print(\"\\n=== Statistical Analysis Results (All Difficulties) ===\")\n",
    "        print(final_df.to_markdown(index=False))\n",
    "        \n",
    "        output_csv = os.path.join(base_folder, \"Statistical_Analysis_Results_All.csv\")\n",
    "        final_df.to_csv(output_csv, index=False)\n",
    "        print(f\"\\n[INFO] Results saved to {output_csv}\")\n",
    "    else:\n",
    "        print(\"No results to display.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ac289b7-9ae3-4720-9fb4-8d993471bc6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[WARN] test_log_ppo.json not found.\n",
      "[WARN] test_log_dt1.json not found.\n",
      "[WARN] test_log_dt1_trained.json not found.\n",
      "[WARN] test_log_dt1_trained_rtg55.json not found.\n",
      "No data found!\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "def load_log(filename):\n",
    "    if not os.path.exists(filename):\n",
    "        print(f\"[WARN] {filename} not found.\")\n",
    "        return None\n",
    "    with open(filename, 'r') as f:\n",
    "        return json.load(f)\n",
    "\n",
    "def plot_benchmark():\n",
    "    files = {\n",
    "        \"PPO (Expert)\": \"test_log_ppo.json\",\n",
    "        \"DT (Initial)\": \"test_log_dt1.json\",\n",
    "        \"DT (Trained)\": \"test_log_dt1_trained.json\",\n",
    "        \"DT (RTG 55)\": \"test_log_dt1_trained_rtg55.json\"\n",
    "    }\n",
    "\n",
    "    data = {}\n",
    "    for name, path in files.items():\n",
    "        log = load_log(path)\n",
    "        if log:\n",
    "            data[name] = log\n",
    "\n",
    "    if not data:\n",
    "        print(\"No data found!\")\n",
    "        return\n",
    "\n",
    "    # 5 Metrics\n",
    "    metrics = [\"win_rate\", \"avg_reward\", \"avg_steps\", \"accuracy\", \"smoothness\"]\n",
    "    titles = [\n",
    "        \"Win Rate (%) (Higher is Better)\", \n",
    "        \"Avg Reward (Higher is Better)\", \n",
    "        \"Time to Kill (Steps) (Lower is Better)\", \n",
    "        \"Accuracy (%) (Higher is Better)\", \n",
    "        \"Smoothness (Lower is Better)\"\n",
    "    ]\n",
    "    colors = ['#FF9999', '#66B2FF', '#99FF99']\n",
    "\n",
    "    names = list(data.keys())\n",
    "    x = np.arange(len(names))\n",
    "    width = 0.6\n",
    "\n",
    "    # 1 Row, 5 Columns\n",
    "    fig, axes = plt.subplots(1, 5, figsize=(25, 5))\n",
    "\n",
    "    for i, metric in enumerate(metrics):\n",
    "        values = [data[name].get(metric, 0) for name in names]\n",
    "        \n",
    "        # Color logic: Green for best, Red for worst? Or just consistent agent colors?\n",
    "        # Let's keep consistent colors for bars.\n",
    "        bar_colors = colors[:len(names)]\n",
    "        \n",
    "        bars = axes[i].bar(x, values, width, color=bar_colors, edgecolor='black', alpha=0.9)\n",
    "        \n",
    "        axes[i].set_title(titles[i], fontsize=12, fontweight='bold')\n",
    "        axes[i].set_xticks(x)\n",
    "        axes[i].set_xticklabels(names, fontsize=10, rotation=15)\n",
    "        axes[i].grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "        # Add value labels\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            axes[i].text(bar.get_x() + bar.get_width()/2., height,\n",
    "                        f'{height:.2f}',\n",
    "                        ha='center', va='bottom', fontsize=11, fontweight='bold')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    output_file = \"benchmark_comparison_5_metrics.png\"\n",
    "    plt.savefig(output_file, dpi=300)\n",
    "    print(f\"[INFO] Plot saved to {output_file}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    plot_benchmark()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9010f84-f520-4be7-bf9d-efcec6fd5244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "Loaded 62 records.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:124: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=subset, x=\"TargetRTG\", y=\"Avg Reward\", ax=ax, palette=\"Blues_d\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved RTG Reward plot to c:\\Users\\Develope\\DT\\FINAL_RESULT\\RTG_Controllability_Reward.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:147: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=subset, x=\"TargetRTG\", y=\"Win Rate\", ax=ax2, palette=\"Oranges_d\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved RTG WinRate plot to c:\\Users\\Develope\\DT\\FINAL_RESULT\\RTG_Controllability_WinRate.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved metric group plot to c:\\Users\\Develope\\DT\\FINAL_RESULT\\MetricGroup_Win_Rate.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved metric group plot to c:\\Users\\Develope\\DT\\FINAL_RESULT\\MetricGroup_Avg_Steps.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved metric group plot to c:\\Users\\Develope\\DT\\FINAL_RESULT\\MetricGroup_Smoothness.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved metric group plot to c:\\Users\\Develope\\DT\\FINAL_RESULT\\MetricGroup_Accuracy.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_11320\\3604442403.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved metric group plot to c:\\Users\\Develope\\DT\\FINAL_RESULT\\MetricGroup_Reward_Std.png\n",
      "Saved summary CSV to c:\\Users\\Develope\\DT\\FINAL_RESULT\\benchmark_summary.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pfn5+DooG7iAgICBblW9SUpKCg4NznHflyhUFBATYTPP399fFixet1cP//H9XYI/3KD09XSVLlnTa9l2tj4sC/YzCQD8DuBlyCaB4sdd5u0SJEnbdBteYgOvhWAZwM+QRQPHjjNf55BGAa+J4hruhtK+YMRqNqly5ss0/vkBgTyaTSbGxsdbXFy9eVHJysipVqqR77rnHZl5cXJyqV68uT0/Pm7axZ88e1alTx/7BFxF7vEeJiYmqXLmy07bvan1cFOhnFAb6GcDNkEsAxYu9ztuBgYF23QbXmIDr4VgGcDPkEUDx44zX+eQRgGvieIa7oYDKiZhMJv3000+aNWuWWrdurXr16qlHjx7avXt3tmUXLlyozp07q169emrSpIkGDx6sY8eOZVtu9erVeuqpp9SgQQM1btxYw4YNs5l/9epVjR07Vk2bNlWzZs30xhtvKDU11W77iKLXunVr7dixQ3v27JHZbNZHH32kLl26yGg0qkOHDlq5cqWOHz+ua9eu6aOPPlK3bt2ytdGpUyd9+umnSk1N1YULFxQdHa0nnnjCAXtjH/Z4j44cOaJGjRo5bfuu1sdFgX5GYaCfARQUuQRQ9Ox13q5atapdt8E1JuB6OJYBFBR5BOAYznidTx4BuCaOZ7gbhvBzMp999pkqVaqkKVOmSJI+/vhj9e/fXz/99JPKlCkjSZo8ebLmzZunF198UU2bNtWFCxc0ffp09erVSytWrFC5cuUkSV988YUmT56sZ599Vm+++aauXr2qLVu22Gxv3Lhxatu2rb788kvFxsZq3LhxKleunF544YWi3XHYTenSpTV+/HgNHTpUV69eVYsWLTR27FhJUvXq1fXSSy+pd+/eslgs6tixo3r16iVJWrZsmeLj4/Xvf/9bTz75pA4ePKg2bdrI19dXQ4YMkclkcuRuFSp7vEcmk0kVK1Z02vZdrY+LAv2MwkA/A7gd5BJA0bLXeXvr1q123QbXmIDr4VgGcDvII4Ci54zX+eQRgGvieIbbsSBHe/bssezZsyfX+ampqZb9+/dbUlNTC22bS5Yssdxzzz2WunXr2vxbv369xWKxWO655x5Lnz59bNZJS0uzPPTQQ5b33nvPYrFYLMePH7fUqlXL8s0339gsd/XqVUvLli0tEydOtFgsFsu5c+csdevWtcydOzfHWI4fP2655557LG+99ZbN9JEjR1o6d+5cGLtrwx7vp7Pbs2eP5amnnrK0aNHCEhERYYmPj7dYLBbLjh07LB07drS0atXKEhUVZTl37lyO6y9btszy8MMPW1q0aGEZN26cxWw2F2X4AOBW+M4G8u5W19nOzhF5hMXivrkEeYRjcN4DrnP2Y8HZ43cV9AOQd+6cS5BHdC6M3bVBLgEAsCeu84HiIz95BEP4FTMlS5bU8uXLbf41adLEOv+xxx6zWd7b21stW7bUgQMHJElbtmyRj49PtuVKlCihRx55RDt27JAkbdy4UQaDwVoFmps2bdrYvK5du7bi4+MLvH/Im+TkZA0ZMkSjRo3Sxo0bFR4ervHjxysjI0MvvviiRo0apfXr16tBgwYaP358tvXj4uI0c+ZMRUdHa+3atTp48KCWLFnigD0BANfHdzaA4oJcAkWB8x5wnbMfC84ev6ugHwAUB+QRAAAULq7zAedV7AqoTpw4of79++v777+3mb5nzx716NFD9evXV5s2bbRo0SKb+cnJyXr99dd1//3367777tNrr72m5OTkogy9UBiNRtWoUcPmn5+fn3V+2bJls63j7++vCxcuSJISEhJUvnx5GQyGbMvdcccdunTpkiQpPj5eFStWlIeHx03jCQwMtHnt6+ura9eu5Xe3kE87d+5UaGio6tatK0nq27evdu7caU0UsxLY9u3bKy4uLtv6S5cuVUREhIKDg+Xt7a3IyEitXr266HYAANwI39lA8UEuQS4B++O8B1zn7MeCs8fvKugHoHggjyCPAACgMHGdDzivYlNA9ffff+v1119Xp06dFBMTYzPv9OnTioyMVLdu3fTrr7/qzTff1Ntvv63NmzdblxkxYoQSEhK0evVqrV69WseOHdPo0aOLejfsLjU1Ndu0U6dOWccQDwgI0NmzZ3Nc99y5cwoKCpJ0/Vcl58+ft1+guC0ZGRnKzMy0vjYajfL19dXVq1dVuXJlrVmzRmazWYsWLVLbtm2zrb9v3z7Vq1fP+jo0NFSHDh0qktgBwN3wnQ04HrlE3pBLoDBw3gOuc/ZjwdnjdxX0A+BY5BF5Qx4BAED+cJ0POK9iU0D122+/KT09XQsWLLBeeGf56quv1KxZMz311FPy8fHRgw8+qO7du2vu3LmSpMOHD2v9+vV6++23VbZsWVWoUEFvvvmmVq9ebf0VhKv45ZdfbF4nJSVpw4YNCgsLkyQ98MADSklJyVaFmp6erp9++kkPPvigJKlp06ZKTk7Wjz/+WDSBI18aNWqk2NhY7dixQxaLRStXrtTJkyeVlJSk119/XUOGDFHDhg31ww8/aODAgdnWP3/+vDUxlaSgoCBdvHixKHcBANwG39mA45FL5A25BAoD5z3gOmc/Fpw9fldBPwCORR6RN+QRAADkD9f5gPPydHQAWbp06aIuXbrkOG/r1q3ZxsVu1qyZVqxYIen6GNv16tVTcHCwdX6tWrVUunRp7d69W61bty5QTBaLRSkpKTnOS0tLk9lsVmZmpk0F6e0wm80ym836+++/baYbjUZVrFhRkrR582ZNmDBBHTt2VGJiot577z1VqFBBXbp0UWZmpqpVq6YePXro//7v/3T27Fndf//9unDhgj755BP5+voqIiJCmZmZqlGjhrp27arhw4fr9OnTatKkiZKSkrR+/Xq99tprMpvNkpRt//45vTBlZmbKbDYrNTXVug135u3trYkTJ2rixIk6f/68wsPDddddd8lisWjo0KGaNWuW6tWrp0WLFmnw4MGaPn26zfqZmZlKTU21fn7T09NlMBiUkpKS46OU7cFisRTJduyhKN4jg8Fg1/fI3u1Lzt3HRcEVPkcS/ZwXfGcD+WOxWAr9s13ccomiziMk980lyCOKnj3Pe67A3ufuorj+4xozb5z9WHD2+F2Fs+cSrnAsw7kUdi5R3PIIKfdcgjyCexJwba7wt2TyCOB/nDnf4p4EXFF+8ohiU0B1M3///beqVq1qMy0kJERJSUm6evVqjvMlqVKlSjp9+nSBt3vt2jUdOHAg1/menp5KS0srcPs5be/KlSt6+OGHbaYHBgZq3bp1kqQhQ4Zo+/bt6tOnj4xGo1q2bKmhQ4fKYDDo6tWrkqRXXnlFFStW1Pz58/Xuu++qdOnSatWqld555x15eHhYlxsxYoQqVaqkOXPmaNKkSSpbtqwef/xxXb161bpf6enp1uWzYrRYLDbTCkNaWpoyMjL0119/FWq7zszHx0cjRoywvl61apW+//571apVS97e3jp48KDq16+vr776SmvXrlWlSpWsy3p6emr37t26fPmyJCkxMVH+/v76888/FVonVJ4e9j30MzIztH/ffqccm97Ly0uhoXXk6elh1+1YzGYZjPZ7CKDZYpbRYL/2nbmPi4KXl5fq1AmVh52PNfq5+OA7G8gfb2/vItuWI3KJos4jsrbpjrkEeYRj2OO8d7NjxlkURS5h7zxC4hozP5z9WHD2+F2Fs+YSrnQsw7kUVS5RHO9JkEdwTwKuiXsSecO1B5yNM+ZbXl5e3JOAy8prHuEUBVQpKSny9fW1mZb1Oj09XSkpKfLx8cm2nq+vr9LT0wu8XS8vL9WsWTPHeWlpaTp16pRKlCiR47YLonv37urevftNlwkMDNS77757y7YGDhyY4yP/bvTcc8/pueeeyzb9rrvu0v79+wsUY0F5enqqatWqKlGihF3ad2a7du1S1apVVbFiRZnNZtWuXds6z9fXV3feeafNZ7V+/fq6cuWKdbmff/5Z9evX19133y1PD099vf8rnU2Jt0usd/iVV6/QCN19991OWT1sMBjk6emh6Yv36+Q5+1RyN7g7WD3a3qXT385UesKpQm/fr3p9lWv5pN362dn7uCgYDAZ5eHjarY8l+rk44zsbuLk///yzSLfniFyiqPMIyb1zCfIIxyqs894/13NW9s4l7J1HSFxj3g5nPxacPX5X4Sy5hCsfyyjeijKXKG73JMgjuCcB18U9iVvj2gPOzlnyLYPBwD0JuKT85BFOUUDl5eWVLenI+jWCj4+PvLy8cqxQTEtLk5+fX4G3azAYcl3faDTKaDTKw8NDHh72rQr/p6LeXlHx8PCQ0WiUr69voSaAzuzIkSOqXr26EhISNHXqVD3//PO688471a9fP/Xt21fVqlXTt99+q/T0dNWuXVteXl7WdZ944gmNGTNGnTt3lo+Pj+bOnatBgwZZk/yzKfE6mXzSrvHf+AcGZ3PyXIqOnk62S9uVyl7/XklPOKW0s8cKvX3v4OuP17Z3Pzt7HxcFe/WxRD8XN3xnA3lXVI+BzuKIXKI45hGSa+YS5BGOYY/z3u3k7sWNvXIJe+cREteY+eXsx4Kzx+8qnDmXcJVjGc6jKHOJ4nZPgjyi8JFLoLjhnsStce0BZ+LM+Rb3JOBq8pNHOEUBVYUKFXTmzBmbaSdPnlSFChXk7e2tChUqaMeOHdnWO336tKpUqVJUYQKFauzYsTp8+LBKliypAQMGqHXr1pKk0aNHa/DgwUpOTlblypU1Y8YMeXl5afPmzVq7dq1GjRqlZs2aqWvXrurQoYO8vLzUt29ftWrVysF7BACui+9soPgilwAKH+c94DpnPxacPX5XQT8AxRN5BAAAuB1c5wPOySkKqBo3bqzNmzerY8eO1mlbt25V8+bNrfM/+eQTXblyRSVLlpQkHTx4UCkpKbr33nsdETJw27744oscp4eHhys8PDzb9LCwMIWFhVlfR0VFKSoqym7xAQD+h+9soPgilwAKH+c94DpnPxacPX5XQT8AxRN5BAAAuB1c5wPOyejoAPKiT58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",
      "text/plain": [
       "<Figure size 2400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qSfEKdOJeHzlyJNHlE8PDwzVkyBAZjUa1adNGPj4+FrMpubq6qm3btvroo4/M29LyM5cUz/pdUbduXW3evFldunTRCy+8YN5esWJFjRw5UpJ08uTJeJ+B33//XXPmzJEkTZw4UcOGDbMoHCtQoIAGDhyoN998U9Kj6+vo6Kjr16/rzJkzCca6adMmSVLjxo3NM7IBAAAAAICMjQIqAAAAAAAyubiiqFWrVsWb8UZ6VFwTFhamWrVqqXTp0s8cb/HixeaZWOKWBntSjx49JEl79+5NeeCS7O3t1bVr12T3++WXX/THH3/IxcVFU6ZMibes3PMIDg6WpOcufFi0aJEkqX///ipZsmSCbRwdHc1FUMePH9e1a9cSbFe2bFk1aNAg3vZmzZqZC4cqV66sRo0axWtTp04dOTo6ymQy6dKlS4nG+9FHH6lAgQIJ7uvWrZuKFCmimJgY7dixw2Kfp6dnotc/blnCiIgIc2Hak4xGo3r06GEu+kqquGX+npxlKjF+fn46d+6cHB0dNXbs2HhL+8UpU6aMOnbsKOnRZyoxPXv2TPB6eXh4mJeJO3v2bJJiS66NGzfKaDSqfPnyevHFFy32vf7663rhhRdkNBrNhTxPWrt2re7du6dChQpp9OjRSbr2afmZS4pn/a6oVq2aXF1dE9zn6ekpe3t7SdLly5ct9s2bN08mk0lNmzZN0nJ7bm5uatiwoSRp27ZtCbbZsmWLJJbvAwAAAAAgM6GACgAAAACATM7T01OlSpXSvXv3tHXrVot9JpNJS5culZS02aekR0t7SVKTJk0SbRNXEBQcHJzoLDdJUbFiReXPnz/Z/eIKt5o2baq8efOm+PgJiSusiYyMTPEY169f1/Xr1yVJ3t7eT21btmxZ82xXv/76a4JtKleunOB2Jycn82w5ccVKT3J0dFTBggUl/a847El2dnZq2rRpojEaDAbz7Ffnzp1LtF2c8PBwXbx4Ub///rt524MHDxI9dv369Z855pPirsnOnTsTLWR53KFDhyQ9+rw8PkNRQuKuxZkzZxIsSpSkevXqJdo/bhm7wMDAZ8aVEnEzYyW0PJzBYDAv65fYMn5xn5+2bdsqW7ZsSTpmWn7mkiIlvysiIyP1559/au/evXJ2dpZk+T6MiYkxzxLWpUuXJI8b95lOaBm/8+fP66+//lLBggVVu3btZMULAAAAAACsJ+FH7QAAAAAAQKbStWtXjRkzRsuWLbMo2Nm3b5+uXr2qIkWKmGdNeZqwsDDzEm3vvPNOko4dGhqa6MxFz/L4UlnJ8ccff0iSXnvttRT1fxo3NzeFh4c/V/FL3Cw3BQsWlLu7+zPbly5dWv/880+iszQVLlw40b4uLi6S9NSioLg2iRUD5c6dW7ly5XpqjEWKFJGUcFHQ5cuXtXHjRv3++++6fPlyvGX+pEfFfAnJly9fimYzatasmVavXq3Dhw/r448/1rx589S1a1c1b948wfHifiZly5Z95thxBVDR0dG6c+eO+dwf97T3fNy1TOx6P4/z58/rjz/+kJ2dnblQ6knNmzfXwoUL9ccff+j8+fPmGbHixM1ElpzPT1p+5pIiKb8r/P395evrq19//VVXrlzRrVu3ZDQaLdo8/j68fv26wsPDJSXvvOrVqyd3d3fdvHlTp0+ftlgKMm7WL29v7yTPjgYAAAAAAKyPAioAAAAAALIAb29vffvtt/r999915swZVaxYUZK0ZMkSSY9mV0nKzfzHZ2cpVKhQkvokd+m1xyWluCghQUFBkh4VO6W2l156Sf7+/jpz5kySi8ieFBoaKknKmTNnktrnyJFDUuKzNMUt0/c0cUuUJeRZP6PElrNLaIzY2FiL7V9//bUWLFggo9EoOzs7lShRQvXq1VPRokVVpEgRff31108dN6XvAYPBIB8fH/3444/y8fHR2bNnNXToUE2ZMkW9e/dWt27dLK7J/fv3JSXtZxL385BkLrB5UlKud2JFY89jw4YN5rETWtYxofZPFlDFzUSWnM9PWn7mkuJZ75PFixdrypQpio6OlvSo4K9mzZoqWrSoihYtqgULFsSbgS3utYuLS5Jn4pIefR6bN2+uJUuWaNu2beYCKpPJZJ4FkOX7AAAAAADIXCigAgAAAAAgC3BxcVG7du20YMECLVu2TF999ZUuX76sQ4cOKXv27GrXrl2Sxolb5kqSVq5cmeKZpZIqpcVXcTMMRUREpGY4kqQaNWro4MGD2rt3r2JiYpJUXPSk7NmzS0q8+OZJce2eNQuUNcUV0OTOndu8bdWqVZo3b55cXFz06aefqmXLlvHO4VkFVM9TgOfg4KBevXqpa9eu2rRpk5YuXaqLFy9q0qRJ2rdvn+bPn28udIp7byflZ/J4m4z0M4mNjdWWLVuS1WfLli0aMmSIxfvY0dFR0dHRyfr8pOQz9/jP9smZoB73tH0JjfWkgwcPasKECbK3t9dHH32k9u3bK1++fBZtVq5cGa+AKq4w8eHDhzIajcmaMap169ZasmSJtm/frs8//1ySdOLECf3777+qVq2aSpQokeSxAAAAAACA9TGPNAAAAAAAWUTcLFNbtmxRcHCwVq5cKZPJpJYtW1oUvTxNrly55OrqKunR8lYZVcGCBSX9bymy1NS8eXM5ODgoICDAvBxXchUtWlTSoyXFQkJCntk+bnm5kiVLpuh4zyssLOyZsyWdP39e0v+Wt5OkpUuXSpI+/PBDde3aNV6xUdxMXGkte/bsat++vXx9fTVx4kTZ29vryJEj5tmapP/9TC5cuPDM8eJ+Hq6urvEKcazp4MGDunv3rpydnbV3714dOXIk0X++vr6ys7PT3bt3dfDgQYtx4pbDS87nJyWfuccLMsPCwhJtd/fu3SSPmZBly5ZJkjp37qx+/fol+DNL6L0Yd05Go1F//vlnso5Zvnx5vfLKK/L399dvv/0mSdq8ebMkqW3btskaCwAAAAAAWB8FVAAAAAAAZBFFixZVw4YNFRkZqXXr1mnjxo2SpK5duyZ5DDs7O7322muSpN27d6cojriZYuKW0koLr7/+uiRp69atioqKStWxixQpopYtW0qSJk2apH///TfZY3h4eChPnjwymUzy9fV9atu//vpLV69elaOjo/m80tvDhw/166+/Jro/ICBAfn5+kqSaNWuat1+5ckWSzEtGPunEiROpGGXStG3bVm+//bYkmWOWpFq1akl6VIQUGBj41DF27Nhh7vO0pfrSW9x7qX79+ipUqJDc3d0T/VemTBlVqVJFkiwKyaT/fX7ifkckRUo+c7lz5zbP8pRY4VVMTMxT33tJ8az34cWLF81LOD4uX758KlWqlCQ983OaEG9vb0nS9u3bFR0drZ9//lkuLi5q2rRpsscCAAAAAADWRQEVAAAAAABZyLvvvitJmjlzpoKCglSjRg298soryRqjTZs2kh7N6nLx4sVE2wUEBCggICDe9rjl6xLal1ratGmj7NmzKyAgQFOnTk20XWxsbIrGHzp0qAoXLqzg4GB169YtSbNxPV6gYTAY1KVLF0mPfhbXrl1LsE9MTIwmTJggSWrVqpXc3d1TFG9qmDJlSoKFMSaTSRMnTlRMTIzKli2rypUrm/fFLet248aNeP2ioqI0c+bMNIv3aTMaxc189Piyb/Xr11fx4sUVGRmpL774ItH3xl9//WWe0ah79+6pF/BzCgsLMxc1vvXWW0nqE1fgs2fPHov3Z6dOnWRnZ6fTp09r0aJFifZ//Bql5DPn4OCgcuXKSZLWrl2bYPuffvpJd+7cSdL5JCbufZjQ59RkMumbb75JtG/nzp0lSYsXL9bp06cTbZfQ+6VFixayt7fX9u3bdeDAAQUHB6tZs2bKkSNHck8BAAAAAABYGQVUAAAAAABkIbVq1dIrr7yi8PBwSf8rqEqON998U1WqVNHDhw/13nvvac2aNebxpEdFCt9//71atGiRYMGCh4eHJOnXX3/VsWPHJEkPHjxQcHBwCs4oYXnz5tWQIUMkSYsWLdKnn35qXnZNelTMtGrVKnOb5MqdO7dmz56t/Pnz69q1a3r77bf1zTffxFvmKywsTEeOHNHIkSPVs2dPi329evWSh4eHQkND1blzZ23YsEEPHz407z979qw++OADHT58WEWKFElxrKnB0dFRly9fVo8ePSyWuLtx44Y+/fRTbd++Xfb29ho9erRFvxo1akiSpk6dajHb1KVLl9SjRw8ZjcY0i/mrr77Sl19+qQsXLpiXHzQajdq1a5d56cXGjRub29vb25uX99u9e7c++OADnTlzxrw/IiJCvr6+evfdd/Xw4UO9++67qlatWqrGvGLFClWpUkWff/55svtu375dDx8+lIuLixo0aJCkPm+++aayZcumyMhIbdu2zbzdw8PD/H796quvNH78ePn7+5v3BwYGav78+Zo0aZJ5W0o/c61bt5b0aEa7adOm6cGDB5IeXe/58+fr66+/TnaR55Pi3ofz58/Xrl27zO+H69eva+DAgbp48aLc3NwS7NuhQwdVqVJF0dHR6t69u3766SeLYrPLly9rzJgxCS7n+cILL6h27dq6deuWfHx8JLF8HwAAAAAAmZWDtQMAAAAAAACpq2vXrho1apSKFCkiLy+vZPc3GAyaOXOmPvjgA124cEEjRozQl19+qTx58ujhw4fm4oJcuXIlOGOSl5eXihQpops3b6pbt25yc3PTgwcPNH/+fHOhQ2ro3LmzwsPD9c0332jz5s3avHmzcuXKpWzZsikwMFBGo/G5lsQrU6aMVq5cqVGjRungwYOaM2eO5syZIycnJ7m5uSkqKkohISHmYo0KFSpY9Hd2dtb8+fPVt29fnTlzRp9//rlGjhxpvh5xRWmvvPKKfvjhB+XOnTvlF+M5OTs7a9KkSfr444/l7e2t3Llzy97eXvfu3ZMkZcuWTVOmTDEv7xjn008/1YkTJ3Tnzh117dpVOXLkkKOjo4KDg5U/f34tXrxYb775ZprEHBsbqzVr1mjFihVycXG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",
      "text/plain": [
       "<Figure size 2400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import re\n",
    "\n",
    "# Set style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic' # For Korean text support on Windows\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def parse_filename(filename, filepath):\n",
    "    \"\"\"\n",
    "    Parses filenames to extract model metadata.\n",
    "    Expected formats: \n",
    "    - E_{Epoch}_DT_{Type}_{DataSize}.json\n",
    "    - {EnemyCount}_{Version}_PPO.json\n",
    "    - {EnemyCount}_{RTG}_E_{Epoch}_DT_{Type}_{DataSize}.json (RTG Test)\n",
    "    \"\"\"\n",
    "    parent_dir = os.path.basename(os.path.dirname(filepath))\n",
    "    \n",
    "    # RTG Test Files\n",
    "    # e.g. 20_0_E_1_DT_S_100.json -> Enemy: 20, RTG: 0\n",
    "    rtg_match = re.search(r\"(\\d+)_(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if \"RTG_Test\" in parent_dir and rtg_match:\n",
    "         return {\n",
    "            \"Model\": \"DT_RTG\",\n",
    "            \"EnemyCount\": int(rtg_match.group(1)),\n",
    "            \"TargetRTG\": int(rtg_match.group(2)),\n",
    "            \"Epoch\": int(rtg_match.group(3)),\n",
    "            \"Type\": rtg_match.group(4),\n",
    "            \"DataSize\": int(rtg_match.group(5)),\n",
    "            \"Name\": f\"RTG_{rtg_match.group(2)}\"\n",
    "        }\n",
    "\n",
    "    enemy_count = 10 # Default\n",
    "    if \"EmemyCount15\" in parent_dir:\n",
    "        enemy_count = 15\n",
    "    elif \"EnemyCount10\" in parent_dir:\n",
    "        enemy_count = 10\n",
    "    elif \"EnemyCount20\" in parent_dir:\n",
    "        enemy_count = 20\n",
    "    \n",
    "    # DT Models\n",
    "    dt_match = re.search(r\"E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match.group(1)),\n",
    "            \"Type\": dt_match.group(2), \n",
    "            \"DataSize\": int(dt_match.group(3)),\n",
    "            \"EnemyCount\": enemy_count,\n",
    "            \"Name\": f\"DT_{dt_match.group(2)}_{dt_match.group(3)}\"\n",
    "        }\n",
    "    \n",
    "    # DT Models with Prefix (e.g. 20_E_1_DT...)\n",
    "    dt_match_prefix = re.search(r\"(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match_prefix:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match_prefix.group(2)),\n",
    "            \"Type\": dt_match_prefix.group(3),\n",
    "            \"DataSize\": int(dt_match_prefix.group(4)),\n",
    "            \"EnemyCount\": int(dt_match_prefix.group(1)),\n",
    "            \"Name\": f\"DT_{dt_match_prefix.group(3)}_{dt_match_prefix.group(4)}\"\n",
    "        }\n",
    "\n",
    "    # PPO Models\n",
    "    ppo_match = re.search(r\"(\\d+)_V(\\d+)_PPO\", filename)\n",
    "    if ppo_match:\n",
    "        return {\n",
    "            \"Model\": \"PPO\",\n",
    "            \"Epoch\": \"PPO\",\n",
    "            \"Type\": \"PPO\",\n",
    "            \"DataSize\": 0,\n",
    "            \"EnemyCount\": int(ppo_match.group(1)),\n",
    "            \"Name\": \"PPO\"\n",
    "        }\n",
    "        \n",
    "    return None\n",
    "\n",
    "def load_data(root_dir):\n",
    "    data = []\n",
    "    files = glob.glob(os.path.join(root_dir, \"**\", \"*.json\"), recursive=True)\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        meta = parse_filename(filename, f)\n",
    "        \n",
    "        if meta:\n",
    "            try:\n",
    "                with open(f, 'r') as json_file:\n",
    "                    content = json.load(json_file)\n",
    "                    \n",
    "                    # Extract metrics\n",
    "                    meta[\"Win Rate\"] = content.get(\"win_rate\", 0)\n",
    "                    meta[\"Avg Reward\"] = content.get(\"avg_reward\", 0) # New for RTG\n",
    "                    meta[\"Avg Steps\"] = content.get(\"avg_steps\", 0)\n",
    "                    meta[\"Smoothness\"] = content.get(\"smoothness\", 0)\n",
    "                    meta[\"Accuracy\"] = content.get(\"accuracy\", 0)\n",
    "                    meta[\"Reward Std\"] = content.get(\"reward_std\", 0)\n",
    "                    \n",
    "                    data.append(meta)\n",
    "            except Exception as e:\n",
    "                print(f\"Error reading {f}: {e}\")\n",
    "                \n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "def plot_rtg_test(df, output_dir):\n",
    "    \"\"\"\n",
    "    Plots Avg Reward vs Target RTG for RTG Test data.\n",
    "    \"\"\"\n",
    "    subset = df[df[\"Model\"] == \"DT_RTG\"].sort_values(by=\"TargetRTG\")\n",
    "    if subset.empty:\n",
    "        print(\"No RTG Test data found.\")\n",
    "        return\n",
    "\n",
    "    # Use a bar plot similar to MetricGroup_Win_Rate\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    fig.suptitle(\"RTG Controllability Test (Avg Reward vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Avg Reward\", ax=ax, palette=\"Blues_d\")\n",
    "    \n",
    "    ax.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax.set_ylabel(\"Actual Average Reward\", fontsize=12)\n",
    "    \n",
    "    # Add values on top\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, fmt='%.2f', padding=3, fontsize=10)\n",
    "        \n",
    "    # Draw reference line (y=x) to show ideal controllability\n",
    "    # Since x is categorical in barplot, we can't easily draw y=x line directly.\n",
    "    # Instead, we can plot points or a line if x was numeric.\n",
    "    # For now, let's just show the bars.\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    save_path = os.path.join(output_dir, \"RTG_Controllability_Reward.png\")\n",
    "    plt.savefig(save_path)\n",
    "    print(f\"Saved RTG Reward plot to {save_path}\")\n",
    "\n",
    "    # Plot 2: Win Rate vs Target RTG\n",
    "    fig2, ax2 = plt.subplots(figsize=(10, 6))\n",
    "    fig2.suptitle(\"RTG Controllability Test (Win Rate vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Win Rate\", ax=ax2, palette=\"Oranges_d\")\n",
    "    \n",
    "    ax2.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax2.set_ylabel(\"Win Rate (%)\", fontsize=12)\n",
    "    \n",
    "    for container in ax2.containers:\n",
    "        ax2.bar_label(container, fmt='%.1f', padding=3, fontsize=10)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    save_path2 = os.path.join(output_dir, \"RTG_Controllability_WinRate.png\")\n",
    "    plt.savefig(save_path2)\n",
    "    print(f\"Saved RTG WinRate plot to {save_path2}\")\n",
    "\n",
    "def plot_by_metric(df, output_dir):\n",
    "    # Filter out RTG Test data for general metrics\n",
    "    df = df[df[\"Model\"] != \"DT_RTG\"]\n",
    "    \n",
    "    metrics = [\"Win Rate\", \"Avg Steps\", \"Smoothness\", \"Accuracy\", \"Reward Std\"]\n",
    "    enemy_counts = [10, 15, 20]\n",
    "    \n",
    "    # Sort data for consistency\n",
    "    df = df.sort_values(by=[\"Model\", \"Type\", \"DataSize\", \"Epoch\"])\n",
    "\n",
    "    for metric in metrics:\n",
    "        fig, axes = plt.subplots(1, 3, figsize=(24, 8))\n",
    "        fig.suptitle(f\"Metric Comparison: {metric}\", fontsize=20)\n",
    "        \n",
    "        for i, count in enumerate(enemy_counts):\n",
    "            ax = axes[i]\n",
    "            subset = df[df[\"EnemyCount\"] == count]\n",
    "            \n",
    "            if subset.empty:\n",
    "                ax.text(0.5, 0.5, \"No Data\", ha='center', va='center')\n",
    "                ax.set_title(f\"Enemy Count: {count}\")\n",
    "                continue\n",
    "                \n",
    "            sns.barplot(data=subset, x=\"Name\", y=metric, hue=\"Epoch\", ax=ax, palette=\"muted\", errorbar=None)\n",
    "            \n",
    "            ax.set_title(f\"Enemy Count: {count}\", fontsize=14)\n",
    "            ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
    "            ax.set_xlabel(\"\")\n",
    "            \n",
    "            if i > 0:\n",
    "                ax.set_ylabel(\"\")\n",
    "            \n",
    "            for container in ax.containers:\n",
    "                fmt_str = '%.1f' if metric == \"Win Rate\" else '%.2f'\n",
    "                if metric == \"Smoothness\": fmt_str = '%.3f'\n",
    "                ax.bar_label(container, fmt=fmt_str, padding=3, fontsize=9)\n",
    "\n",
    "        plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "        \n",
    "        safe_metric_name = metric.replace(\" \", \"_\")\n",
    "        save_path = os.path.join(output_dir, f\"MetricGroup_{safe_metric_name}.png\")\n",
    "        plt.savefig(save_path)\n",
    "        print(f\"Saved metric group plot to {save_path}\")\n",
    "\n",
    "def main():\n",
    "    root_dir = r\"c:\\Users\\Develope\\DT\\FINAL_RESULT\"\n",
    "    \n",
    "    print(\"Loading data...\")\n",
    "    df = load_data(root_dir)\n",
    "    \n",
    "    if df.empty:\n",
    "        print(\"No data found!\")\n",
    "        return\n",
    "        \n",
    "    print(f\"Loaded {len(df)} records.\")\n",
    "    \n",
    "    # Plot RTG Test Results\n",
    "    plot_rtg_test(df, root_dir)\n",
    "    \n",
    "    # Plot other metrics (excluding RTG test data)\n",
    "    plot_by_metric(df, root_dir)\n",
    "    \n",
    "    # Save CSV\n",
    "    csv_path = os.path.join(root_dir, \"benchmark_summary.csv\")\n",
    "    df.to_csv(csv_path, index=False)\n",
    "    print(f\"Saved summary CSV to {csv_path}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e1cf45-95a1-4a4c-89d1-3e2093c86b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import re\n",
    "\n",
    "# Set style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic' # For Korean text support on Windows\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def parse_filename(filename, filepath):\n",
    "    \"\"\"\n",
    "    Parses filenames to extract model metadata.\n",
    "    Expected formats: \n",
    "    - E_{Epoch}_DT_{Type}_{DataSize}.json\n",
    "    - {EnemyCount}_{Version}_PPO.json\n",
    "    - {EnemyCount}_{RTG}_E_{Epoch}_DT_{Type}_{DataSize}.json (RTG Test)\n",
    "    \"\"\"\n",
    "    parent_dir = os.path.basename(os.path.dirname(filepath))\n",
    "    \n",
    "    # RTG Test Files\n",
    "    rtg_match = re.search(r\"(\\d+)_(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if \"RTG_Test\" in parent_dir and rtg_match:\n",
    "         return {\n",
    "            \"Model\": \"DT_RTG\",\n",
    "            \"EnemyCount\": int(rtg_match.group(1)),\n",
    "            \"TargetRTG\": int(rtg_match.group(2)),\n",
    "            \"Epoch\": int(rtg_match.group(3)),\n",
    "            \"Type\": rtg_match.group(4),\n",
    "            \"DataSize\": int(rtg_match.group(5)),\n",
    "            \"Name\": f\"RTG_{rtg_match.group(2)}\"\n",
    "        }\n",
    "\n",
    "    enemy_count = 10 # Default\n",
    "    if \"EmemyCount15\" in parent_dir or \"EnemyCount15\" in parent_dir:\n",
    "        enemy_count = 15\n",
    "    elif \"EnemyCount10\" in parent_dir:\n",
    "        enemy_count = 10\n",
    "    elif \"EnemyCount20\" in parent_dir:\n",
    "        enemy_count = 20\n",
    "    \n",
    "    # DT Models\n",
    "    dt_match = re.search(r\"E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match.group(1)),\n",
    "            \"Type\": dt_match.group(2), \n",
    "            \"DataSize\": int(dt_match.group(3)),\n",
    "            \"EnemyCount\": enemy_count,\n",
    "            \"Name\": f\"DT_{dt_match.group(2)}_{dt_match.group(3)}\"\n",
    "        }\n",
    "    \n",
    "    # DT Models with Prefix\n",
    "    dt_match_prefix = re.search(r\"(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match_prefix:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match_prefix.group(2)),\n",
    "            \"Type\": dt_match_prefix.group(3),\n",
    "            \"DataSize\": int(dt_match_prefix.group(4)),\n",
    "            \"EnemyCount\": int(dt_match_prefix.group(1)),\n",
    "            \"Name\": f\"DT_{dt_match_prefix.group(3)}_{dt_match_prefix.group(4)}\"\n",
    "        }\n",
    "\n",
    "    # PPO Models\n",
    "    ppo_match = re.search(r\"(\\d+)_V(\\d+)_PPO\", filename)\n",
    "    if ppo_match:\n",
    "        return {\n",
    "            \"Model\": \"PPO\",\n",
    "            \"Epoch\": \"PPO\",\n",
    "            \"Type\": \"PPO\",\n",
    "            \"DataSize\": 0,\n",
    "            \"EnemyCount\": int(ppo_match.group(1)),\n",
    "            \"Name\": \"PPO\"\n",
    "        }\n",
    "        \n",
    "    return None\n",
    "\n",
    "def load_data(root_dir):\n",
    "    data = []\n",
    "    files = glob.glob(os.path.join(root_dir, \"**\", \"*.json\"), recursive=True)\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        meta = parse_filename(filename, f)\n",
    "        \n",
    "        if meta:\n",
    "            try:\n",
    "                with open(f, 'r') as json_file:\n",
    "                    content = json.load(json_file)\n",
    "                    \n",
    "                    # 새로운 원본 데이터 배열이 있는 경우 에피소드 단위로 50개의 행(Row) 생성\n",
    "                    if \"all_wins\" in content and len(content[\"all_wins\"]) > 0:\n",
    "                        for i in range(len(content[\"all_wins\"])):\n",
    "                            row = meta.copy()\n",
    "                            row[\"Win Rate\"] = content[\"all_wins\"][i] * 100.0\n",
    "                            row[\"Avg Reward\"] = content[\"all_rewards\"][i]\n",
    "                            row[\"Avg Steps\"] = content[\"all_steps\"][i]\n",
    "                            row[\"Smoothness\"] = content[\"all_smoothness\"][i]\n",
    "                            row[\"Accuracy\"] = content[\"all_accuracies\"][i]\n",
    "                            row[\"Reward Std\"] = content.get(\"reward_std\", 0) # 스칼라 폴백\n",
    "                            data.append(row)\n",
    "                    else:\n",
    "                        # 예전 스칼라 데이터 포맷\n",
    "                        meta[\"Win Rate\"] = content.get(\"win_rate\", 0)\n",
    "                        meta[\"Avg Reward\"] = content.get(\"avg_reward\", 0)\n",
    "                        meta[\"Avg Steps\"] = content.get(\"avg_steps\", 0)\n",
    "                        meta[\"Smoothness\"] = content.get(\"smoothness\", 0)\n",
    "                        meta[\"Accuracy\"] = content.get(\"accuracy\", 0)\n",
    "                        meta[\"Reward Std\"] = content.get(\"reward_std\", 0)\n",
    "                        data.append(meta)\n",
    "            except Exception as e:\n",
    "                print(f\"Error reading {f}: {e}\")\n",
    "                \n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "def plot_rtg_test(df, output_dir):\n",
    "    \"\"\"\n",
    "    Plots Avg Reward vs Target RTG for RTG Test data.\n",
    "    \"\"\"\n",
    "    subset = df[df[\"Model\"] == \"DT_RTG\"].sort_values(by=\"TargetRTG\")\n",
    "    if subset.empty:\n",
    "        print(\"No RTG Test data found.\")\n",
    "        return\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    fig.suptitle(\"RTG Controllability Test (Avg Reward vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Avg Reward\", ax=ax, palette=\"Blues_d\", errorbar=None)\n",
    "    \n",
    "    ax.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax.set_ylabel(\"Actual Average Reward\", fontsize=12)\n",
    "    \n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, fmt='%.2f', padding=3, fontsize=10)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    save_path = os.path.join(output_dir, \"RTG_Controllability_Reward.png\")\n",
    "    plt.savefig(save_path, dpi=300)\n",
    "    plt.close(fig)\n",
    "\n",
    "    fig2, ax2 = plt.subplots(figsize=(10, 6))\n",
    "    fig2.suptitle(\"RTG Controllability Test (Win Rate vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Win Rate\", ax=ax2, palette=\"Oranges_d\", errorbar=None)\n",
    "    \n",
    "    ax2.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax2.set_ylabel(\"Win Rate (%)\", fontsize=12)\n",
    "    \n",
    "    for container in ax2.containers:\n",
    "        ax2.bar_label(container, fmt='%.1f', padding=3, fontsize=10)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    save_path2 = os.path.join(output_dir, \"RTG_Controllability_WinRate.png\")\n",
    "    plt.savefig(save_path2, dpi=300)\n",
    "    plt.close(fig2)\n",
    "\n",
    "def plot_by_metric_split(df, output_dir):\n",
    "    # Filter out RTG Test data for general metrics\n",
    "    df = df[df[\"Model\"] != \"DT_RTG\"]\n",
    "    \n",
    "    metrics = [\"Win Rate\", \"Avg Steps\", \"Smoothness\", \"Accuracy\", \"Avg Reward\"]\n",
    "    enemy_counts = [10, 15, 20]\n",
    "    \n",
    "    # Sort data for consistency\n",
    "    df = df.sort_values(by=[\"Model\", \"Type\", \"DataSize\", \"Epoch\"])\n",
    "\n",
    "    for metric in metrics:\n",
    "        for count in enemy_counts:\n",
    "            subset = df[df[\"EnemyCount\"] == count]\n",
    "            \n",
    "            if subset.empty:\n",
    "                continue\n",
    "                \n",
    "            fig, ax = plt.subplots(figsize=(10, 7)) # Standalone large figure\n",
    "            #fig.suptitle(f\"{metric} (Enemy Count: {count})\", fontsize=18, fontweight='bold')\n",
    "            \n",
    "            # Use errorbar='ci' (95% Confidence Interval) automatically computed from the 50 episodes raw data\n",
    "            # capsize=0.1 adds horizontal caps to the error bars\n",
    "            sns.barplot(data=subset, x=\"Name\", y=metric, hue=\"Epoch\", ax=ax, palette=\"muted\", capsize=0.1, errorbar=('ci', 95))\n",
    "            \n",
    "            ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n",
    "            ax.set_xlabel(\"Model Name\", fontsize=14, fontweight='bold')\n",
    "            ax.set_ylabel(metric, fontsize=14, fontweight='bold')\n",
    "            \n",
    "            # Add text labels on top of bars\n",
    "            for container in ax.containers:\n",
    "                fmt_str = '%.1f' if metric == \"Win Rate\" else '%.2f'\n",
    "                if metric == \"Smoothness\": fmt_str = '%.3f'\n",
    "                ax.bar_label(container, fmt=fmt_str, padding=3, fontsize=11)\n",
    "\n",
    "            plt.tight_layout()\n",
    "            \n",
    "            safe_metric_name = metric.replace(\" \", \"_\")\n",
    "            save_path = os.path.join(output_dir, f\"Metric_{safe_metric_name}_Enemy_{count}.png\")\n",
    "            plt.savefig(save_path, dpi=300) # Save as high-resolution PNG\n",
    "            plt.close(fig)\n",
    "            print(f\"Saved independent metric plot to {save_path}\")\n",
    "\n",
    "def main():\n",
    "    # Use the new revision data folder\n",
    "    root_dir = r\"c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    \n",
    "    print(f\"Loading data from {root_dir}...\")\n",
    "    df = load_data(root_dir)\n",
    "    \n",
    "    if df.empty:\n",
    "        print(\"No data found!\")\n",
    "        return\n",
    "        \n",
    "    print(f\"Loaded {len(df)} episode records across all files.\")\n",
    "    \n",
    "    # Plot RTG Test Results (if any exist in this folder)\n",
    "    plot_rtg_test(df, root_dir)\n",
    "    \n",
    "    # Plot split, high-resolution metrics with error bars\n",
    "    plot_by_metric_split(df, root_dir)\n",
    "    \n",
    "    # Save CSV\n",
    "    csv_path = os.path.join(root_dir, \"benchmark_summary.csv\")\n",
    "    df.to_csv(csv_path, index=False)\n",
    "    print(f\"Saved summary CSV to {csv_path}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb561931-1a70-4767-98e5-bfdef4d016cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision...\n",
      "Loaded 2414 episode records across all files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:133: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=subset, x=\"TargetRTG\", y=\"Avg Reward\", ax=ax, palette=\"Blues_d\", errorbar=None)\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:149: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=subset, x=\"TargetRTG\", y=\"Success Rate\", ax=ax2, palette=\"Oranges_d\", errorbar=None)\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Success_Rate_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Success_Rate_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Success_Rate_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Avg_Steps_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Avg_Steps_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Avg_Steps_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Control_Stability_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Control_Stability_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Control_Stability_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Accuracy_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Accuracy_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Accuracy_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Reward_Std_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Reward_Std_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_42424\\560926483.py:186: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Metric_Reward_Std_Enemy_20.png\n",
      "Saved summary CSV to c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\benchmark_summary.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import re\n",
    "\n",
    "# Set style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic' # For Korean text support on Windows\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def parse_filename(filename, filepath):\n",
    "    \"\"\"\n",
    "    Parses filenames to extract model metadata.\n",
    "    Expected formats: \n",
    "    - E_{Epoch}_DT_{Type}_{DataSize}.json\n",
    "    - {EnemyCount}_{Version}_PPO.json\n",
    "    - {EnemyCount}_{RTG}_E_{Epoch}_DT_{Type}_{DataSize}.json (RTG Test)\n",
    "    \"\"\"\n",
    "    parent_dir = os.path.basename(os.path.dirname(filepath))\n",
    "    \n",
    "    # RTG Test Files\n",
    "    rtg_match = re.search(r\"(\\d+)_(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if \"RTG_Test\" in parent_dir and rtg_match:\n",
    "         return {\n",
    "            \"Model\": \"DT_RTG\",\n",
    "            \"EnemyCount\": int(rtg_match.group(1)),\n",
    "            \"TargetRTG\": int(rtg_match.group(2)),\n",
    "            \"Epoch\": int(rtg_match.group(3)),\n",
    "            \"Type\": rtg_match.group(4),\n",
    "            \"DataSize\": int(rtg_match.group(5)),\n",
    "            \"Name\": f\"RTG_{rtg_match.group(2)}\"\n",
    "        }\n",
    "\n",
    "    enemy_count = 10 # Default\n",
    "    if \"EmemyCount15\" in parent_dir or \"EnemyCount15\" in parent_dir:\n",
    "        enemy_count = 15\n",
    "    elif \"EnemyCount10\" in parent_dir:\n",
    "        enemy_count = 10\n",
    "    elif \"EnemyCount20\" in parent_dir:\n",
    "        enemy_count = 20\n",
    "    \n",
    "    # DT Models\n",
    "    dt_match = re.search(r\"E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match.group(1)),\n",
    "            \"Type\": dt_match.group(2), \n",
    "            \"DataSize\": int(dt_match.group(3)),\n",
    "            \"EnemyCount\": enemy_count,\n",
    "            \"Name\": f\"DT_{dt_match.group(2)}_{dt_match.group(3)}\"\n",
    "        }\n",
    "    \n",
    "    # DT Models with Prefix\n",
    "    dt_match_prefix = re.search(r\"(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match_prefix:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match_prefix.group(2)),\n",
    "            \"Type\": dt_match_prefix.group(3),\n",
    "            \"DataSize\": int(dt_match_prefix.group(4)),\n",
    "            \"EnemyCount\": int(dt_match_prefix.group(1)),\n",
    "            \"Name\": f\"DT_{dt_match_prefix.group(3)}_{dt_match_prefix.group(4)}\"\n",
    "        }\n",
    "\n",
    "    # PPO Models\n",
    "    ppo_match = re.search(r\"(\\d+)_V(\\d+)_PPO\", filename)\n",
    "    if ppo_match:\n",
    "        return {\n",
    "            \"Model\": \"PPO\",\n",
    "            \"Epoch\": \"PPO\",\n",
    "            \"Type\": \"PPO\",\n",
    "            \"DataSize\": 0,\n",
    "            \"EnemyCount\": int(ppo_match.group(1)),\n",
    "            \"Name\": \"PPO\"\n",
    "        }\n",
    "        \n",
    "    return None\n",
    "\n",
    "def load_data(root_dir):\n",
    "    data = []\n",
    "    files = glob.glob(os.path.join(root_dir, \"**\", \"*.json\"), recursive=True)\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        meta = parse_filename(filename, f)\n",
    "        \n",
    "        if meta:\n",
    "            try:\n",
    "                with open(f, 'r') as json_file:\n",
    "                    content = json.load(json_file)\n",
    "                    \n",
    "                    # 새로운 원본 데이터 배열이 있는 경우 에피소드 단위로 50개의 행(Row) 생성\n",
    "                    if \"all_wins\" in content and len(content[\"all_wins\"]) > 0:\n",
    "                        for i in range(len(content[\"all_wins\"])):\n",
    "                            row = meta.copy()\n",
    "                            row[\"Success Rate\"] = content[\"all_wins\"][i] * 100.0\n",
    "                            row[\"Avg Reward\"] = content[\"all_rewards\"][i]\n",
    "                            row[\"Avg Steps\"] = content[\"all_steps\"][i]\n",
    "                            row[\"Control Stability\"] = content[\"all_smoothness\"][i]\n",
    "                            row[\"Accuracy\"] = content[\"all_accuracies\"][i]\n",
    "                            row[\"Reward Std\"] = content.get(\"reward_std\", 0) # 스칼라 폴백\n",
    "                            data.append(row)\n",
    "                    else:\n",
    "                        # 예전 스칼라 데이터 포맷\n",
    "                        meta[\"Success Rate\"] = content.get(\"win_rate\", 0)\n",
    "                        meta[\"Avg Reward\"] = content.get(\"avg_reward\", 0)\n",
    "                        meta[\"Avg Steps\"] = content.get(\"avg_steps\", 0)\n",
    "                        meta[\"Control Stability\"] = content.get(\"smoothness\", 0)\n",
    "                        meta[\"Accuracy\"] = content.get(\"accuracy\", 0)\n",
    "                        meta[\"Reward Std\"] = content.get(\"reward_std\", 0)\n",
    "                        data.append(meta)\n",
    "            except Exception as e:\n",
    "                print(f\"Error reading {f}: {e}\")\n",
    "                \n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "def plot_rtg_test(df, output_dir):\n",
    "    \"\"\"\n",
    "    Plots Avg Reward vs Target RTG for RTG Test data.\n",
    "    \"\"\"\n",
    "    subset = df[df[\"Model\"] == \"DT_RTG\"].sort_values(by=\"TargetRTG\")\n",
    "    if subset.empty:\n",
    "        print(\"No RTG Test data found.\")\n",
    "        return\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    fig.suptitle(\"RTG Controllability Test (Avg Reward vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Avg Reward\", ax=ax, palette=\"Blues_d\", errorbar=None)\n",
    "    \n",
    "    ax.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax.set_ylabel(\"Actual Average Reward\", fontsize=12)\n",
    "    \n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, fmt='%.2f', padding=3, fontsize=10)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    save_path = os.path.join(output_dir, \"RTG_Controllability_Reward.png\")\n",
    "    plt.savefig(save_path, dpi=300)\n",
    "    plt.close(fig)\n",
    "\n",
    "    fig2, ax2 = plt.subplots(figsize=(10, 6))\n",
    "    fig2.suptitle(\"RTG Controllability Test (Success Rate vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Success Rate\", ax=ax2, palette=\"Oranges_d\", errorbar=None)\n",
    "    \n",
    "    ax2.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax2.set_ylabel(\"Success Rate (%)\", fontsize=12)\n",
    "    \n",
    "    for container in ax2.containers:\n",
    "        ax2.bar_label(container, fmt='%.1f', padding=3, fontsize=10)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    save_path2 = os.path.join(output_dir, \"RTG_Controllability_SuccessRate.png\")\n",
    "    plt.savefig(save_path2, dpi=300)\n",
    "    plt.close(fig2)\n",
    "\n",
    "def plot_by_metric_split(df, output_dir):\n",
    "    # Filter out RTG Test data for general metrics\n",
    "    df = df[df[\"Model\"] != \"DT_RTG\"]\n",
    "    \n",
    "    metrics = [\"Success Rate\", \"Avg Steps\", \"Control Stability\", \"Accuracy\", \"Reward Std\"]\n",
    "    enemy_counts = [10, 15, 20]\n",
    "    \n",
    "    # Sort data for consistency\n",
    "    df = df.sort_values(by=[\"Model\", \"Type\", \"DataSize\", \"Epoch\"])\n",
    "\n",
    "    for metric in metrics:\n",
    "        for count in enemy_counts:\n",
    "            subset = df[df[\"EnemyCount\"] == count]\n",
    "            \n",
    "            if subset.empty:\n",
    "                continue\n",
    "                \n",
    "            fig, ax = plt.subplots(figsize=(10, 7)) # Standalone large figure\n",
    "            fig.suptitle(f\"{metric} (Enemy Count: {count})\", fontsize=18, fontweight='bold')\n",
    "            \n",
    "            # Use errorbar='ci' (95% Confidence Interval) automatically computed from the 50 episodes raw data\n",
    "            # capsize=0.1 adds horizontal caps to the error bars\n",
    "            sns.barplot(data=subset, x=\"Name\", y=metric, hue=\"Epoch\", ax=ax, palette=\"muted\", capsize=0.1, errorbar=('ci', 95))\n",
    "            \n",
    "            ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n",
    "            ax.set_xlabel(\"Model Name\", fontsize=14, fontweight='bold')\n",
    "            ax.set_ylabel(metric, fontsize=14, fontweight='bold')\n",
    "            \n",
    "            # Add text labels on top of bars\n",
    "            for container in ax.containers:\n",
    "                fmt_str = '%.1f' if metric == \"Success Rate\" else '%.2f'\n",
    "                if metric == \"Control Stability\": fmt_str = '%.3f'\n",
    "                ax.bar_label(container, fmt=fmt_str, padding=3, fontsize=11)\n",
    "\n",
    "            plt.tight_layout()\n",
    "            \n",
    "            safe_metric_name = metric.replace(\" \", \"_\")\n",
    "            save_path = os.path.join(output_dir, f\"Metric_{safe_metric_name}_Enemy_{count}.png\")\n",
    "            plt.savefig(save_path, dpi=300) # Save as high-resolution PNG\n",
    "            plt.close(fig)\n",
    "            print(f\"Saved independent metric plot to {save_path}\")\n",
    "\n",
    "def main():\n",
    "    # Use the new revision data folder\n",
    "    root_dir = r\"c:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    \n",
    "    print(f\"Loading data from {root_dir}...\")\n",
    "    df = load_data(root_dir)\n",
    "    \n",
    "    if df.empty:\n",
    "        print(\"No data found!\")\n",
    "        return\n",
    "        \n",
    "    print(f\"Loaded {len(df)} episode records across all files.\")\n",
    "    \n",
    "    # Plot RTG Test Results (if any exist in this folder)\n",
    "    plot_rtg_test(df, root_dir)\n",
    "    \n",
    "    # Plot split, high-resolution metrics with error bars\n",
    "    plot_by_metric_split(df, root_dir)\n",
    "    \n",
    "    # Save CSV\n",
    "    csv_path = os.path.join(root_dir, \"benchmark_summary.csv\")\n",
    "    df.to_csv(csv_path, index=False)\n",
    "    print(f\"Saved summary CSV to {csv_path}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cb517a73-9684-452e-b22b-0584ffbb0be0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\RTG_Test...\n",
      "Loaded 12 RTG experiments.\n",
      "[INFO] Saved C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Fig2_RTG_Analysis_Revised.png\n",
      "[INFO] Saved C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Fig3_RTG_Boxplot.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Set style for academic paper\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.family'] = 'serif' # Times New Roman style often preferred in papers\n",
    "plt.rcParams['font.size'] = 11\n",
    "\n",
    "def save_plot(fig, filename):\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "    print(f\"[INFO] Saved {filename}\")\n",
    "\n",
    "def load_rtg_data(rtg_dir):\n",
    "    data = []\n",
    "    # Find all json files in RTG_Test folder\n",
    "    files = glob.glob(os.path.join(rtg_dir, \"*.json\"))\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        # Expected format: 20_{RTG}_E_1_DT_S_100.json\n",
    "        match = re.search(r\"20_(\\d+)_E_\", filename)\n",
    "        if match:\n",
    "            rtg_val = int(match.group(1))\n",
    "            with open(f, 'r') as json_file:\n",
    "                content = json.load(json_file)\n",
    "                win_rate = content.get(\"win_rate\", 0)\n",
    "                avg_reward = content.get(\"avg_reward\", 0)\n",
    "                reward_std = content.get(\"reward_std\", 0)\n",
    "                all_rewards = content.get(\"all_rewards\", [])\n",
    "                \n",
    "                data.append({\n",
    "                    \"RTG\": rtg_val,\n",
    "                    \"WinRate\": win_rate,\n",
    "                    \"AvgReward\": avg_reward,\n",
    "                    \"RewardStd\": reward_std,\n",
    "                    \"AllRewards\": all_rewards\n",
    "                })\n",
    "                \n",
    "    # Sort by RTG ascending\n",
    "    data = sorted(data, key=lambda x: x[\"RTG\"])\n",
    "    \n",
    "    # Filter data to only include RTG <= 110\n",
    "    data = [d for d in data if d[\"RTG\"] <= 110]\n",
    "    return data\n",
    "\n",
    "def plot_rtg_analysis(data, output_dir):\n",
    "    fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "    rtg_values = [str(d['RTG']) for d in data]\n",
    "    rtg_rewards = [d['AvgReward'] for d in data]\n",
    "    rtg_std = [d['RewardStd'] for d in data]\n",
    "    rtg_wins = [d['WinRate'] for d in data]\n",
    "\n",
    "    x = np.arange(len(rtg_values))\n",
    "    width = 0.5\n",
    "    \n",
    "    # Dual Axis Plot\n",
    "    # Bar for Reward with Error Bars (Standard Deviation)\n",
    "    color1 = 'tab:blue'\n",
    "    bars = ax1.bar(x, rtg_rewards, width, label='Avg. Reward', color=color1, alpha=0.7, \n",
    "                   edgecolor='black', yerr=rtg_std, capsize=5)\n",
    "    ax1.set_xlabel('Target Return (RTG)', fontweight='bold')\n",
    "    ax1.set_ylabel('Average Reward', color=color1, fontweight='bold')\n",
    "    ax1.tick_params(axis='y', labelcolor=color1)\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(rtg_values, rotation=45, ha='right')\n",
    "    \n",
    "    # Set y-axis limit for reward (dynamically based on max reward + std)\n",
    "    max_val = max([r + s for r, s in zip(rtg_rewards, rtg_std)]) if rtg_rewards else 60\n",
    "    ax1.set_ylim(0, max_val + 10)\n",
    "\n",
    "    # Line for Success Rate\n",
    "    ax2 = ax1.twinx()\n",
    "    color2 = 'tab:red'\n",
    "    ax2.plot(x, rtg_wins, color=color2, marker='o', linewidth=3, label='Success Rate (%)')\n",
    "    ax2.set_ylabel('Success Rate (%)', color=color2, fontweight='bold')\n",
    "    ax2.tick_params(axis='y', labelcolor=color2)\n",
    "    ax2.set_ylim(-5, 105)\n",
    "\n",
    "    # Annotations (Average values)\n",
    "    for i, (v, s) in enumerate(zip(rtg_rewards, rtg_std)):\n",
    "        # Displaying average slightly below the top of the bar to avoid overlap with error bars\n",
    "        ax1.text(i, v/2, f'{v:.1f}', ha='center', color='white', fontweight='bold', fontsize=9)\n",
    "    \n",
    "    for i, v in enumerate(rtg_wins):\n",
    "        ax2.text(i, v + 5, f'{int(v)}%', ha='center', color=color2, fontweight='bold', fontsize=9)\n",
    "\n",
    "    plt.title(\"Impact of Returns-To-Go (RTG) on Performance\", fontsize=14, pad=15)\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    output_filename = os.path.join(output_dir, 'Fig2_RTG_Analysis_Revised.png')\n",
    "    save_plot(fig, output_filename)\n",
    "    plt.close(fig)\n",
    "\n",
    "def plot_rtg_boxplot(data, output_dir):\n",
    "    # Flatten data for seaborn boxplot\n",
    "    box_data = []\n",
    "    for d in data:\n",
    "        for r in d['AllRewards']:\n",
    "            box_data.append({'Target Return (RTG)': str(d['RTG']), 'Reward': r})\n",
    "            \n",
    "    df = pd.DataFrame(box_data)\n",
    "    \n",
    "    if df.empty:\n",
    "        print(\"[WARNING] No 'all_rewards' data found for boxplot.\")\n",
    "        return\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    \n",
    "    # Create boxplot to show median, IQR, and outliers\n",
    "    sns.boxplot(data=df, x='Target Return (RTG)', y='Reward', ax=ax, hue='Target Return (RTG)', palette='Blues', showfliers=True, legend=False)\n",
    "    \n",
    "    ax.set_title(\"Distribution of Rewards Across Target RTG Settings (IQR & Variance)\", fontsize=14, pad=15)\n",
    "    ax.set_ylabel(\"Cumulative Reward\", fontweight='bold')\n",
    "    ax.set_xlabel(\"Target Return (RTG)\", fontweight='bold')\n",
    "    ax.tick_params(axis='x', rotation=45)\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_horizontalalignment('right')\n",
    "    \n",
    "    # Draw a reference line indicating the typical maximum reward or threshold\n",
    "    # Assuming ~63-64 is the max possible reward when 20 enemies are killed\n",
    "    ax.axhline(y=63.5, color='r', linestyle='--', alpha=0.5, label='Estimated Max Reward (All Targets Cleared)')\n",
    "    ax.legend(loc='lower right')\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    output_filename = os.path.join(output_dir, 'Fig3_RTG_Boxplot.png')\n",
    "    save_plot(fig, output_filename)\n",
    "    plt.close(fig)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Path to the RTG_Test folder\n",
    "    rtg_dir = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\RTG_Test\"\n",
    "    output_dir = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "    \n",
    "    print(f\"Loading data from {rtg_dir}...\")\n",
    "    \n",
    "    data = load_rtg_data(rtg_dir)\n",
    "    \n",
    "    if not data:\n",
    "        print(\"No RTG data found. Please check the directory path.\")\n",
    "    else:\n",
    "        print(f\"Loaded {len(data)} RTG experiments.\")\n",
    "        plot_rtg_analysis(data, output_dir)\n",
    "        plot_rtg_boxplot(data, output_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be81e850-3daf-429d-bc44-8b154a5a1bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\EnemyCount10...\n",
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\EnemyCount15...\n",
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\EnemyCount20...\n",
      "\n",
      "=== Statistical Analysis Results (All Difficulties) ===\n",
      "| Difficulty   | Metric                         | PPO Mean ± SD     | DT_S_100 Mean ± SD   |   ANOVA p-value |   T-test p-value (PPO vs DT_S_100) | Significant? (p < 0.05)   |\n",
      "|:-------------|:-------------------------------|:------------------|:---------------------|----------------:|-----------------------------------:|:--------------------------|\n",
      "| Enemy 10     | Success Rate (Wins)            | 0.980 ± 0.141     | 1.000 ± 0.000        |      0.01015    |                          0.3222    | No                        |\n",
      "| Enemy 10     | Cumulative Reward              | 31.552 ± 0.877    | 32.696 ± 0.283       |      0.0002402  |                          2.741e-12 | Yes                       |\n",
      "| Enemy 10     | Control Stability (Smoothness) | 0.369 ± 0.043     | 0.147 ± 0.062        |      7.819e-143 |                          1.197e-35 | Yes                       |\n",
      "| Enemy 10     | Shooting Accuracy              | 3.758 ± 1.460     | 25.080 ± 8.475       |      8.66e-29   |                          1.854e-23 | Yes                       |\n",
      "| Enemy 10     | Time to Kill (Steps)           | 357.480 ± 168.475 | 214.160 ± 29.366     |      1.497e-21  |                          2.524e-07 | Yes                       |\n",
      "| Enemy 15     | Success Rate (Wins)            | 0.960 ± 0.198     | 1.000 ± 0.000        |      1.051e-07  |                          0.1594    | No                        |\n",
      "| Enemy 15     | Cumulative Reward              | 46.473 ± 1.209    | 48.233 ± 0.478       |      4.246e-06  |                          5.736e-14 | Yes                       |\n",
      "| Enemy 15     | Control Stability (Smoothness) | 0.377 ± 0.045     | 0.204 ± 0.162        |      5.525e-72  |                          1.311e-09 | Yes                       |\n",
      "| Enemy 15     | Shooting Accuracy              | 5.054 ± 1.847     | 22.936 ± 6.495       |      3.54e-36   |                          5.435e-26 | Yes                       |\n",
      "| Enemy 15     | Time to Kill (Steps)           | 390.200 ± 173.051 | 267.580 ± 57.441     |      5.059e-34  |                          1.295e-05 | Yes                       |\n",
      "| Enemy 20     | Success Rate (Wins)            | 0.960 ± 0.198     | 1.000 ± 0.000        |      1.249e-06  |                          0.1594    | No                        |\n",
      "| Enemy 20     | Cumulative Reward              | 61.373 ± 1.520    | 63.573 ± 0.377       |      1.821e-07  |                          7.099e-14 | Yes                       |\n",
      "| Enemy 20     | Control Stability (Smoothness) | 0.372 ± 0.033     | 0.202 ± 0.092        |      1.463e-110 |                          3.015e-18 | Yes                       |\n",
      "| Enemy 20     | Shooting Accuracy              | 5.541 ± 2.073     | 22.535 ± 4.873       |      3.345e-47  |                          6.242e-33 | Yes                       |\n",
      "| Enemy 20     | Time to Kill (Steps)           | 464.160 ± 174.620 | 309.000 ± 45.768     |      2.883e-37  |                          1.156e-07 | Yes                       |\n",
      "\n",
      "[INFO] Results saved to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\Statistical_Analysis_Results_All.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n",
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n",
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "def parse_filename(filename):\n",
    "    # Matches PPO: 20_V12_PPO.json -> Model: PPO\n",
    "    if \"PPO\" in filename:\n",
    "        return \"PPO\"\n",
    "    \n",
    "    # Matches DT variants: 20_E_1_DT_S_100.json -> DT_S_100\n",
    "    dt_match = re.search(r\"DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return f\"DT_{dt_match.group(1)}_{dt_match.group(2)}\"\n",
    "    \n",
    "    return \"Unknown\"\n",
    "\n",
    "def load_data(folder_path):\n",
    "    files = glob.glob(os.path.join(folder_path, \"*.json\"))\n",
    "    data_dict = {}\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        model_name = parse_filename(filename)\n",
    "        \n",
    "        with open(f, 'r') as json_file:\n",
    "            try:\n",
    "                content = json.load(json_file)\n",
    "                # Ensure all required raw arrays exist\n",
    "                if all(k in content for k in [\"all_rewards\", \"all_wins\", \"all_steps\", \"all_smoothness\", \"all_accuracies\"]):\n",
    "                    data_dict[model_name] = {\n",
    "                        \"rewards\": np.array(content[\"all_rewards\"]),\n",
    "                        \"wins\": np.array(content[\"all_wins\"]),\n",
    "                        \"steps\": np.array(content[\"all_steps\"]),\n",
    "                        \"smoothness\": np.array(content[\"all_smoothness\"]),\n",
    "                        \"accuracies\": np.array(content[\"all_accuracies\"])\n",
    "                    }\n",
    "            except Exception as e:\n",
    "                print(f\"Error loading {f}: {e}\")\n",
    "                \n",
    "    return data_dict\n",
    "\n",
    "def run_statistical_tests(data_dict, baseline=\"PPO\", target=\"DT_S_100\"):\n",
    "    metrics = {\n",
    "        \"Success Rate (Wins)\": \"wins\",\n",
    "        \"Cumulative Reward\": \"rewards\",\n",
    "        \"Control Stability (Smoothness)\": \"smoothness\",\n",
    "        \"Shooting Accuracy\": \"accuracies\",\n",
    "        \"Time to Kill (Steps)\": \"steps\"\n",
    "    }\n",
    "    \n",
    "    results = []\n",
    "    \n",
    "    for metric_name, key in metrics.items():\n",
    "        arrays = [v[key] for k, v in data_dict.items()]\n",
    "        model_names = list(data_dict.keys())\n",
    "        \n",
    "        # 1. One-Way ANOVA across all models\n",
    "        f_stat, anova_p = stats.f_oneway(*arrays) if len(arrays) > 1 else (np.nan, np.nan)\n",
    "        \n",
    "        # 2. Independent T-Test (Baseline vs Target)\n",
    "        t_p_val = np.nan\n",
    "        t_stat = np.nan\n",
    "        if baseline in data_dict and target in data_dict:\n",
    "            base_data = data_dict[baseline][key]\n",
    "            target_data = data_dict[target][key]\n",
    "            \n",
    "            # For success rate (binary 0 or 1), T-test is acceptable for large N,\n",
    "            # but formally proportions Z-test is better. We will use Welch's t-test for all continuous/binary.\n",
    "            t_stat, t_p_val = stats.ttest_ind(base_data, target_data, equal_var=False)\n",
    "            \n",
    "            base_mean = np.mean(base_data)\n",
    "            target_mean = np.mean(target_data)\n",
    "            base_std = np.std(base_data, ddof=1)\n",
    "            target_std = np.std(target_data, ddof=1)\n",
    "        else:\n",
    "            base_mean = target_mean = base_std = target_std = np.nan\n",
    "            \n",
    "        results.append({\n",
    "            \"Metric\": metric_name,\n",
    "            f\"{baseline} Mean ± SD\": f\"{base_mean:.3f} ± {base_std:.3f}\",\n",
    "            f\"{target} Mean ± SD\": f\"{target_mean:.3f} ± {target_std:.3f}\",\n",
    "            \"ANOVA p-value\": f\"{anova_p:.3e}\",\n",
    "            f\"T-test p-value ({baseline} vs {target})\": f\"{t_p_val:.3e}\",\n",
    "            \"Significant? (p < 0.05)\": \"Yes\" if t_p_val < 0.05 else \"No\"\n",
    "        })\n",
    "        \n",
    "    return pd.DataFrame(results)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    base_folder = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    difficulties = [\"EnemyCount10\", \"EnemyCount15\", \"EnemyCount20\"]\n",
    "    \n",
    "    all_results = []\n",
    "    \n",
    "    for diff in difficulties:\n",
    "        folder_path = os.path.join(base_folder, diff)\n",
    "        print(f\"Loading data from {folder_path}...\")\n",
    "        \n",
    "        data_dict = load_data(folder_path)\n",
    "        \n",
    "        if len(data_dict) < 2:\n",
    "            print(f\"Not enough models found in {diff}. Skipping.\")\n",
    "            continue\n",
    "            \n",
    "        # Run tests comparing PPO vs the best DT model (assuming DT_S_100)\n",
    "        df_results = run_statistical_tests(data_dict, baseline=\"PPO\", target=\"DT_S_100\")\n",
    "        df_results.insert(0, \"Difficulty\", diff.replace(\"EnemyCount\", \"Enemy \"))\n",
    "        \n",
    "        all_results.append(df_results)\n",
    "        \n",
    "    if all_results:\n",
    "        final_df = pd.concat(all_results, ignore_index=True)\n",
    "        print(\"\\n=== Statistical Analysis Results (All Difficulties) ===\")\n",
    "        print(final_df.to_markdown(index=False))\n",
    "        \n",
    "        output_csv = os.path.join(base_folder, \"Statistical_Analysis_Results_All.csv\")\n",
    "        final_df.to_csv(output_csv, index=False)\n",
    "        print(f\"\\n[INFO] Results saved to {output_csv}\")\n",
    "    else:\n",
    "        print(\"No results to display.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e4aed04-8801-4340-84dc-96480edc1f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Success Rate Analysis Results (All Models) ===\n",
      "| Difficulty   | Metric       |   ANOVA p-value | PPO (Mean ± SD)   | DT_C_10 (Mean ± SD)   |   p-val (DT_C_10 vs PPO) | DT_C_5 (Mean ± SD)   |   p-val (DT_C_5 vs PPO) | DT_SC_10 (Mean ± SD)   |   p-val (DT_SC_10 vs PPO) | DT_SC_5 (Mean ± SD)   |   p-val (DT_SC_5 vs PPO) | DT_S_100 (Mean ± SD)   |   p-val (DT_S_100 vs PPO) |\n",
      "|:-------------|:-------------|----------------:|:------------------|:----------------------|-------------------------:|:---------------------|------------------------:|:-----------------------|--------------------------:|:----------------------|-------------------------:|:-----------------------|--------------------------:|\n",
      "| Enemy 10     | Success Rate |       0.01015   | 0.980 ± 0.141     | 0.960 ± 0.198         |                   0.5625 | 0.900 ± 0.303        |               0.09522   | 1.000 ± 0.000          |                    0.3222 | 1.000 ± 0.000         |                   0.3222 | 1.000 ± 0.000          |                    0.3222 |\n",
      "| Enemy 15     | Success Rate |       1.051e-07 | 0.960 ± 0.198     | 0.960 ± 0.198         |                   1      | 0.720 ± 0.454        |               0.00104   | 0.960 ± 0.198          |                    1      | 0.960 ± 0.198         |                   1      | 1.000 ± 0.000          |                    0.1594 |\n",
      "| Enemy 20     | Success Rate |       1.249e-06 | 0.960 ± 0.198     | 0.900 ± 0.303         |                   0.2445 | 0.700 ± 0.463        |               0.0005148 | 0.960 ± 0.198          |                    1      | 0.920 ± 0.274         |                   0.405  | 1.000 ± 0.000          |                    0.1594 |\n",
      "\n",
      "[INFO] Results saved to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\\SuccessRate_Analysis.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "def parse_filename(filename):\n",
    "    if \"PPO\" in filename:\n",
    "        return \"PPO\"\n",
    "    dt_match = re.search(r\"DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return f\"DT_{dt_match.group(1)}_{dt_match.group(2)}\"\n",
    "    return \"Unknown\"\n",
    "\n",
    "def load_data(folder_path):\n",
    "    files = glob.glob(os.path.join(folder_path, \"*.json\"))\n",
    "    data_dict = {}\n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        model_name = parse_filename(filename)\n",
    "        with open(f, 'r') as json_file:\n",
    "            try:\n",
    "                content = json.load(json_file)\n",
    "                if \"all_wins\" in content:\n",
    "                    data_dict[model_name] = np.array(content[\"all_wins\"])\n",
    "            except Exception as e:\n",
    "                print(f\"Error loading {f}: {e}\")\n",
    "    return data_dict\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    base_folder = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    difficulties = [\"EnemyCount10\", \"EnemyCount15\", \"EnemyCount20\"]\n",
    "    \n",
    "    # We want to compare models against PPO\n",
    "    baseline = \"PPO\"\n",
    "    \n",
    "    results = []\n",
    "    \n",
    "    for diff in difficulties:\n",
    "        folder_path = os.path.join(base_folder, diff)\n",
    "        data_dict = load_data(folder_path)\n",
    "        \n",
    "        if len(data_dict) < 2 or baseline not in data_dict:\n",
    "            continue\n",
    "            \n",
    "        base_data = data_dict[baseline]\n",
    "        base_mean = np.mean(base_data)\n",
    "        base_std = np.std(base_data, ddof=1)\n",
    "        \n",
    "        # Calculate ANOVA across all models for this difficulty\n",
    "        arrays = list(data_dict.values())\n",
    "        f_stat, anova_p = stats.f_oneway(*arrays) if len(arrays) > 1 else (np.nan, np.nan)\n",
    "        \n",
    "        # We will create a row for this difficulty\n",
    "        row = {\n",
    "            \"Difficulty\": diff.replace(\"EnemyCount\", \"Enemy \"),\n",
    "            \"Metric\": \"Success Rate\",\n",
    "            \"ANOVA p-value\": f\"{anova_p:.3e}\",\n",
    "            f\"{baseline} (Mean ± SD)\": f\"{base_mean:.3f} ± {base_std:.3f}\"\n",
    "        }\n",
    "        \n",
    "        # Add T-test vs PPO for every other model\n",
    "        for model_name, data in data_dict.items():\n",
    "            if model_name == baseline:\n",
    "                continue\n",
    "                \n",
    "            mean = np.mean(data)\n",
    "            std = np.std(data, ddof=1)\n",
    "            t_stat, t_p_val = stats.ttest_ind(base_data, data, equal_var=False)\n",
    "            \n",
    "            row[f\"{model_name} (Mean ± SD)\"] = f\"{mean:.3f} ± {std:.3f}\"\n",
    "            row[f\"p-val ({model_name} vs {baseline})\"] = f\"{t_p_val:.3e}\"\n",
    "            \n",
    "        results.append(row)\n",
    "        \n",
    "    if results:\n",
    "        df = pd.DataFrame(results)\n",
    "        print(\"\\n=== Success Rate Analysis Results (All Models) ===\")\n",
    "        print(df.to_markdown(index=False))\n",
    "        \n",
    "        output_csv = os.path.join(base_folder, \"SuccessRate_Analysis.csv\")\n",
    "        df.to_csv(output_csv, index=False)\n",
    "        print(f\"\\n[INFO] Results saved to {output_csv}\")\n",
    "    else:\n",
    "        print(\"No results to display.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9d24720b-7311-412d-9513-139c3ec70c20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "=== Success Rate Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric       |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val (E3_DT_C_5 vs PPO) | E3_DT_C_10 (Mean ± SD)   |   p-val (E3_DT_C_10 vs PPO) | E3_DT_S_100 (Mean ± SD)   |   p-val (E3_DT_S_100 vs PPO) | E3_DT_SC_5 (Mean ± SD)   |   p-val (E3_DT_SC_5 vs PPO) | E3_DT_SC_10 (Mean ± SD)   |   p-val (E3_DT_SC_10 vs PPO) |\n",
      "|:-------------|:-------------|----------------:|:------------------|:------------------------|---------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|\n",
      "| Enemy 10     | Success Rate |       0.01015   | 0.980 ± 0.141     | 0.900 ± 0.303           |                  0.09522   | 0.960 ± 0.198            |                      0.5625 | 1.000 ± 0.000             |                       0.3222 | 1.000 ± 0.000            |                      0.3222 | 1.000 ± 0.000             |                       0.3222 |\n",
      "| Enemy 15     | Success Rate |       1.051e-07 | 0.960 ± 0.198     | 0.720 ± 0.454           |                  0.00104   | 0.960 ± 0.198            |                      1      | 1.000 ± 0.000             |                       0.1594 | 0.960 ± 0.198            |                      1      | 0.960 ± 0.198             |                       1      |\n",
      "| Enemy 20     | Success Rate |       1.249e-06 | 0.960 ± 0.198     | 0.700 ± 0.463           |                  0.0005148 | 0.900 ± 0.303            |                      0.2445 | 1.000 ± 0.000             |                       0.1594 | 0.920 ± 0.274            |                      0.405  | 0.960 ± 0.198             |                       1      |\n",
      "\n",
      "==================================================\n",
      "=== Average Steps Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric        |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val (E3_DT_C_5 vs PPO) | E3_DT_C_10 (Mean ± SD)   |   p-val (E3_DT_C_10 vs PPO) | E3_DT_S_100 (Mean ± SD)   |   p-val (E3_DT_S_100 vs PPO) | E3_DT_SC_5 (Mean ± SD)   |   p-val (E3_DT_SC_5 vs PPO) | E3_DT_SC_10 (Mean ± SD)   |   p-val (E3_DT_SC_10 vs PPO) |\n",
      "|:-------------|:--------------|----------------:|:------------------|:------------------------|---------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|\n",
      "| Enemy 10     | Average Steps |       1.497e-21 | 357.480 ± 168.475 | 595.860 ± 236.313       |                  9.733e-08 | 441.700 ± 195.457        |                   0.02316   | 214.160 ± 29.366          |                    2.524e-07 | 389.820 ± 140.065        |                   0.2993    | 416.540 ± 189.582         |                    0.1029    |\n",
      "| Enemy 15     | Average Steps |       5.059e-34 | 390.200 ± 173.051 | 759.340 ± 217.994       |                  4.154e-15 | 573.680 ± 198.582        |                   3.493e-06 | 267.580 ± 57.441          |                    1.295e-05 | 535.740 ± 179.488        |                   7.711e-05 | 467.680 ± 196.009         |                    0.03876   |\n",
      "| Enemy 20     | Average Steps |       2.883e-37 | 464.160 ± 174.620 | 816.180 ± 190.884       |                  8.616e-16 | 655.160 ± 192.109        |                   1.095e-06 | 309.000 ± 45.768          |                    1.156e-07 | 631.420 ± 212.023        |                   4.063e-05 | 606.980 ± 178.408         |                    0.0001043 |\n",
      "\n",
      "==================================================\n",
      "=== Cumulative Reward Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric            |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val (E3_DT_C_5 vs PPO) | E3_DT_C_10 (Mean ± SD)   |   p-val (E3_DT_C_10 vs PPO) | E3_DT_S_100 (Mean ± SD)   |   p-val (E3_DT_S_100 vs PPO) | E3_DT_SC_5 (Mean ± SD)   |   p-val (E3_DT_SC_5 vs PPO) | E3_DT_SC_10 (Mean ± SD)   |   p-val (E3_DT_SC_10 vs PPO) |\n",
      "|:-------------|:------------------|----------------:|:------------------|:------------------------|---------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|\n",
      "| Enemy 10     | Cumulative Reward |       0.0002402 | 31.552 ± 0.877    | 32.385 ± 2.189          |                    0.01514 | 32.208 ± 1.659           |                   0.01581   | 32.696 ± 0.283            |                    2.741e-12 | 32.335 ± 0.633           |                   1.774e-06 | 32.503 ± 0.771            |                    1.043e-07 |\n",
      "| Enemy 15     | Cumulative Reward |       4.246e-06 | 46.473 ± 1.209    | 46.707 ± 4.033          |                    0.6961  | 48.128 ± 1.295           |                   2.066e-09 | 48.233 ± 0.478            |                    5.736e-14 | 47.585 ± 1.616           |                   0.0001859 | 47.833 ± 1.254            |                    2.765e-07 |\n",
      "| Enemy 20     | Cumulative Reward |       1.821e-07 | 61.373 ± 1.520    | 62.457 ± 3.426          |                    0.04465 | 63.394 ± 1.897           |                   6.296e-08 | 63.573 ± 0.377            |                    7.099e-14 | 62.983 ± 2.361           |                   0.0001119 | 63.513 ± 1.521            |                    2.694e-10 |\n",
      "\n",
      "==================================================\n",
      "=== Control Stability Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric            |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val (E3_DT_C_5 vs PPO) | E3_DT_C_10 (Mean ± SD)   |   p-val (E3_DT_C_10 vs PPO) | E3_DT_S_100 (Mean ± SD)   |   p-val (E3_DT_S_100 vs PPO) | E3_DT_SC_5 (Mean ± SD)   |   p-val (E3_DT_SC_5 vs PPO) | E3_DT_SC_10 (Mean ± SD)   |   p-val (E3_DT_SC_10 vs PPO) |\n",
      "|:-------------|:------------------|----------------:|:------------------|:------------------------|---------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|\n",
      "| Enemy 10     | Control Stability |      7.819e-143 | 0.369 ± 0.043     | 0.087 ± 0.020           |                  2.565e-51 | 0.085 ± 0.025            |                   8.752e-55 | 0.147 ± 0.062             |                    1.197e-35 | 0.086 ± 0.023            |                   2.697e-53 | 0.081 ± 0.020             |                    3.305e-52 |\n",
      "| Enemy 15     | Control Stability |      5.525e-72  | 0.377 ± 0.045     | 0.100 ± 0.020           |                  9.738e-49 | 0.099 ± 0.021            |                   2.446e-49 | 0.204 ± 0.162             |                    1.311e-09 | 0.091 ± 0.022            |                   1.186e-50 | 0.096 ± 0.023             |                    1.546e-50 |\n",
      "| Enemy 20     | Control Stability |      1.463e-110 | 0.372 ± 0.033     | 0.114 ± 0.023           |                  6.095e-63 | 0.115 ± 0.029            |                   1.213e-63 | 0.202 ± 0.092             |                    3.015e-18 | 0.107 ± 0.023            |                   4.088e-64 | 0.108 ± 0.020             |                    2.337e-62 |\n",
      "\n",
      "==================================================\n",
      "=== Firing Accuracy Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric          |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val (E3_DT_C_5 vs PPO) | E3_DT_C_10 (Mean ± SD)   |   p-val (E3_DT_C_10 vs PPO) | E3_DT_S_100 (Mean ± SD)   |   p-val (E3_DT_S_100 vs PPO) | E3_DT_SC_5 (Mean ± SD)   |   p-val (E3_DT_SC_5 vs PPO) | E3_DT_SC_10 (Mean ± SD)   |   p-val (E3_DT_SC_10 vs PPO) |\n",
      "|:-------------|:----------------|----------------:|:------------------|:------------------------|---------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|:-------------------------|----------------------------:|:--------------------------|-----------------------------:|\n",
      "| Enemy 10     | Firing Accuracy |       8.66e-29  | 3.758 ± 1.460     | 19.718 ± 8.219          |                  1.144e-18 | 20.016 ± 9.567           |                   2.436e-16 | 25.080 ± 8.475            |                    1.854e-23 | 19.246 ± 8.407           |                   9.747e-18 | 20.467 ± 11.866           |                    2.155e-13 |\n",
      "| Enemy 15     | Firing Accuracy |       3.54e-36  | 5.054 ± 1.847     | 18.716 ± 7.146          |                  1.313e-18 | 17.484 ± 6.830           |                   9.637e-18 | 22.936 ± 6.495            |                    5.435e-26 | 16.996 ± 5.218           |                   1.664e-22 | 20.695 ± 8.556            |                    1.068e-17 |\n",
      "| Enemy 20     | Firing Accuracy |       3.345e-47 | 5.541 ± 2.073     | 19.056 ± 5.010          |                  1.621e-26 | 17.041 ± 5.100           |                   2.053e-22 | 22.535 ± 4.873            |                    6.242e-33 | 15.900 ± 5.926           |                   3.694e-17 | 16.379 ± 5.350            |                    4.288e-20 |\n",
      "\n",
      "[INFO] All analysis complete. TXT files saved to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "def parse_filename(filename):\n",
    "    if \"PPO\" in filename:\n",
    "        return \"PPO\"\n",
    "        \n",
    "    # Check for epoch prefix in filename robustly\n",
    "    if \"E_3\" in filename or \"E3\" in filename:\n",
    "        prefix = \"E3_\"\n",
    "    elif \"E_2\" in filename or \"E2\" in filename:\n",
    "        prefix = \"E2_\"\n",
    "    elif \"E_1\" in filename or \"E1\" in filename:\n",
    "        prefix = \"E1_\"\n",
    "    else:\n",
    "        prefix = \"\"\n",
    "        \n",
    "    dt_match = re.search(r\"DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return f\"{prefix}DT_{dt_match.group(1)}_{dt_match.group(2)}\"\n",
    "    return \"Unknown\"\n",
    "\n",
    "def load_data(folder_path):\n",
    "    files = glob.glob(os.path.join(folder_path, \"*.json\"))\n",
    "    data_dict = {}\n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        model_name = parse_filename(filename)\n",
    "        with open(f, 'r') as json_file:\n",
    "            try:\n",
    "                content = json.load(json_file)\n",
    "                data_dict[model_name] = {\n",
    "                    \"Success Rate\": np.array(content.get(\"all_wins\", [])),\n",
    "                    \"Average Steps\": np.array(content.get(\"all_steps\", [])),\n",
    "                    \"Cumulative Reward\": np.array(content.get(\"all_rewards\", [])),\n",
    "                    \"Control Stability\": np.array(content.get(\"all_smoothness\", [])),\n",
    "                    \"Firing Accuracy\": np.array(content.get(\"all_accuracies\", []))\n",
    "                }\n",
    "            except Exception as e:\n",
    "                print(f\"Error loading {f}: {e}\")\n",
    "    return data_dict\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    base_folder = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    difficulties = [\"EnemyCount10\", \"EnemyCount15\", \"EnemyCount20\"]\n",
    "    baseline = \"PPO\"\n",
    "    target_models = [\"PPO\", \"E3_DT_C_5\", \"E3_DT_C_10\", \"E3_DT_S_100\", \"E3_DT_SC_5\", \"E3_DT_SC_10\"]\n",
    "    \n",
    "    metrics_list = [\"Success Rate\", \"Average Steps\", \"Cumulative Reward\", \"Control Stability\", \"Firing Accuracy\"]\n",
    "    \n",
    "    for metric_name in metrics_list:\n",
    "        results = []\n",
    "        for diff in difficulties:\n",
    "            folder_path = os.path.join(base_folder, diff)\n",
    "            data_dict = load_data(folder_path)\n",
    "            \n",
    "            # Filter specifically for the target models agreed upon\n",
    "            valid_models = {k: v[metric_name] for k, v in data_dict.items() if k in target_models and len(v[metric_name]) > 0}\n",
    "            \n",
    "            if len(valid_models) < 2 or baseline not in valid_models:\n",
    "                continue\n",
    "                \n",
    "            base_data = valid_models[baseline]\n",
    "            base_mean = np.mean(base_data)\n",
    "            base_std = np.std(base_data, ddof=1)\n",
    "            \n",
    "            arrays = list(valid_models.values())\n",
    "            f_stat, anova_p = stats.f_oneway(*arrays) if len(arrays) > 1 else (np.nan, np.nan)\n",
    "            \n",
    "            row = {\n",
    "                \"Difficulty\": diff.replace(\"EnemyCount\", \"Enemy \"),\n",
    "                \"Metric\": metric_name,\n",
    "                \"ANOVA p-value\": f\"{anova_p:.3e}\",\n",
    "                f\"{baseline} (Mean ± SD)\": f\"{base_mean:.3f} ± {base_std:.3f}\"\n",
    "            }\n",
    "            \n",
    "            # Ensure fixed order: DT_C_5, DT_C_10, DT_S_100, DT_SC_5, DT_SC_10\n",
    "            for model_name in target_models:\n",
    "                if model_name == baseline or model_name not in valid_models:\n",
    "                    continue\n",
    "                data = valid_models[model_name]\n",
    "                mean = np.mean(data)\n",
    "                std = np.std(data, ddof=1)\n",
    "                t_stat, t_p_val = stats.ttest_ind(base_data, data, equal_var=False)\n",
    "                \n",
    "                row[f\"{model_name} (Mean ± SD)\"] = f\"{mean:.3f} ± {std:.3f}\"\n",
    "                row[f\"p-val ({model_name} vs {baseline})\"] = f\"{t_p_val:.3e}\"\n",
    "                \n",
    "            results.append(row)\n",
    "            \n",
    "        if results:\n",
    "            df = pd.DataFrame(results)\n",
    "            print(f\"\\n{'='*50}\")\n",
    "            print(f\"=== {metric_name} Analysis Results (E3 Models) ===\")\n",
    "            print(df.to_markdown(index=False))\n",
    "            \n",
    "            output_txt = os.path.join(base_folder, f\"{metric_name.replace(' ', '')}_E3_Analysis.txt\")\n",
    "            with open(output_txt, 'w', encoding='utf-8') as f:\n",
    "                f.write(f\"=== {metric_name} Analysis Results (E3 Models) ===\\n\")\n",
    "                f.write(df.to_markdown(index=False))\n",
    "                \n",
    "    print(f\"\\n[INFO] All analysis complete. TXT files saved to {base_folder}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "78348e89-ca65-4dd4-8c20-8f9355134bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "=== Success Rate Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric       |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val(Mean) E3_DT_C_5 |   p-val(SD) E3_DT_C_5 | E3_DT_C_10 (Mean ± SD)   |   p-val(Mean) E3_DT_C_10 |   p-val(SD) E3_DT_C_10 | E3_DT_S_100 (Mean ± SD)   |   p-val(Mean) E3_DT_S_100 |   p-val(SD) E3_DT_S_100 | E3_DT_SC_5 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_5 |   p-val(SD) E3_DT_SC_5 | E3_DT_SC_10 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_10 |   p-val(SD) E3_DT_SC_10 |\n",
      "|:-------------|:-------------|----------------:|:------------------|:------------------------|------------------------:|----------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|\n",
      "| Enemy 10     | Success Rate |       0.01015   | 0.980 ± 0.141     | 0.900 ± 0.303           |               0.09522   |             0.09391   | 0.960 ± 0.198            |                   0.5625 |                 0.5624 | 1.000 ± 0.000             |                    0.3222 |                  0.3198 | 1.000 ± 0.000            |                   0.3222 |                 0.3198 | 1.000 ± 0.000             |                    0.3222 |                  0.3198 |\n",
      "| Enemy 15     | Success Rate |       1.051e-07 | 0.960 ± 0.198     | 0.720 ± 0.454           |               0.00104   |             0.0008868 | 0.960 ± 0.198            |                   1      |                 1      | 1.000 ± 0.000             |                    0.1594 |                  0.1562 | 0.960 ± 0.198            |                   1      |                 1      | 0.960 ± 0.198             |                    1      |                  1      |\n",
      "| Enemy 20     | Success Rate |       1.249e-06 | 0.960 ± 0.198     | 0.700 ± 0.463           |               0.0005148 |             0.0004204 | 0.900 ± 0.303            |                   0.2445 |                 0.244  | 1.000 ± 0.000             |                    0.1594 |                  0.1562 | 0.920 ± 0.274            |                   0.405  |                 0.4048 | 0.960 ± 0.198             |                    1      |                  1      |\n",
      "\n",
      "==================================================\n",
      "=== Average Steps Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric        |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val(Mean) E3_DT_C_5 |   p-val(SD) E3_DT_C_5 | E3_DT_C_10 (Mean ± SD)   |   p-val(Mean) E3_DT_C_10 |   p-val(SD) E3_DT_C_10 | E3_DT_S_100 (Mean ± SD)   |   p-val(Mean) E3_DT_S_100 |   p-val(SD) E3_DT_S_100 | E3_DT_SC_5 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_5 |   p-val(SD) E3_DT_SC_5 | E3_DT_SC_10 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_10 |   p-val(SD) E3_DT_SC_10 |\n",
      "|:-------------|:--------------|----------------:|:------------------|:------------------------|------------------------:|----------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|\n",
      "| Enemy 10     | Average Steps |       1.497e-21 | 357.480 ± 168.475 | 595.860 ± 236.313       |               9.733e-08 |              0.006866 | 441.700 ± 195.457        |                0.02316   |                 0.3475 | 214.160 ± 29.366          |                 2.524e-07 |               2.283e-06 | 389.820 ± 140.065        |                0.2993    |                0.9874  | 416.540 ± 189.582         |                 0.1029    |                  0.19   |\n",
      "| Enemy 15     | Average Steps |       5.059e-34 | 390.200 ± 173.051 | 759.340 ± 217.994       |               4.154e-15 |              0.006249 | 573.680 ± 198.582        |                3.493e-06 |                 0.1422 | 267.580 ± 57.441          |                 1.295e-05 |               1.339e-05 | 535.740 ± 179.488        |                7.711e-05 |                0.2381  | 467.680 ± 196.009         |                 0.03876   |                  0.3602 |\n",
      "| Enemy 20     | Average Steps |       2.883e-37 | 464.160 ± 174.620 | 816.180 ± 190.884       |               8.616e-16 |              0.1088   | 655.160 ± 192.109        |                1.095e-06 |                 0.1255 | 309.000 ± 45.768          |                 1.156e-07 |               6.634e-06 | 631.420 ± 212.023        |                4.063e-05 |                0.04197 | 606.980 ± 178.408         |                 0.0001043 |                  0.4716 |\n",
      "\n",
      "==================================================\n",
      "=== Cumulative Reward Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric            |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val(Mean) E3_DT_C_5 |   p-val(SD) E3_DT_C_5 | E3_DT_C_10 (Mean ± SD)   |   p-val(Mean) E3_DT_C_10 |   p-val(SD) E3_DT_C_10 | E3_DT_S_100 (Mean ± SD)   |   p-val(Mean) E3_DT_S_100 |   p-val(SD) E3_DT_S_100 | E3_DT_SC_5 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_5 |   p-val(SD) E3_DT_SC_5 | E3_DT_SC_10 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_10 |   p-val(SD) E3_DT_SC_10 |\n",
      "|:-------------|:------------------|----------------:|:------------------|:------------------------|------------------------:|----------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|\n",
      "| Enemy 10     | Cumulative Reward |       0.0002402 | 31.552 ± 0.877    | 32.385 ± 2.189          |                 0.01514 |             0.001004  | 32.208 ± 1.659           |                0.01581   |                0.03042 | 32.696 ± 0.283            |                 2.741e-12 |                  0.7558 | 32.335 ± 0.633           |                1.774e-06 |                0.124   | 32.503 ± 0.771            |                 1.043e-07 |                 0.05523 |\n",
      "| Enemy 15     | Cumulative Reward |       4.246e-06 | 46.473 ± 1.209    | 46.707 ± 4.033          |                 0.6961  |             2.324e-06 | 48.128 ± 1.295           |                2.066e-09 |                0.06873 | 48.233 ± 0.478            |                 5.736e-14 |                  0.7023 | 47.585 ± 1.616           |                0.0001859 |                0.07236 | 47.833 ± 1.254            |                 2.765e-07 |                 0.1098  |\n",
      "| Enemy 20     | Cumulative Reward |       1.821e-07 | 61.373 ± 1.520    | 62.457 ± 3.426          |                 0.04465 |             1.426e-05 | 63.394 ± 1.897           |                6.296e-08 |                0.00818 | 63.573 ± 0.377            |                 7.099e-14 |                  0.59   | 62.983 ± 2.361           |                0.0001119 |                0.02508 | 63.513 ± 1.521            |                 2.694e-10 |                 0.1369  |\n",
      "\n",
      "==================================================\n",
      "=== Control Stability Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric            |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val(Mean) E3_DT_C_5 |   p-val(SD) E3_DT_C_5 | E3_DT_C_10 (Mean ± SD)   |   p-val(Mean) E3_DT_C_10 |   p-val(SD) E3_DT_C_10 | E3_DT_S_100 (Mean ± SD)   |   p-val(Mean) E3_DT_S_100 |   p-val(SD) E3_DT_S_100 | E3_DT_SC_5 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_5 |   p-val(SD) E3_DT_SC_5 | E3_DT_SC_10 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_10 |   p-val(SD) E3_DT_SC_10 |\n",
      "|:-------------|:------------------|----------------:|:------------------|:------------------------|------------------------:|----------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|\n",
      "| Enemy 10     | Control Stability |      7.819e-143 | 0.369 ± 0.043     | 0.087 ± 0.020           |               2.565e-51 |             3.338e-05 | 0.085 ± 0.025            |                8.752e-55 |              0.0005481 | 0.147 ± 0.062             |                 1.197e-35 |                 0.2109  | 0.086 ± 0.023            |                2.697e-53 |              0.0001263 | 0.081 ± 0.020             |                 3.305e-52 |               2.231e-05 |\n",
      "| Enemy 15     | Control Stability |      5.525e-72  | 0.377 ± 0.045     | 0.100 ± 0.020           |               9.738e-49 |             1.385e-05 | 0.099 ± 0.021            |                2.446e-49 |              5.021e-05 | 0.204 ± 0.162             |                 1.311e-09 |                 0.1136  | 0.091 ± 0.022            |                1.186e-50 |              3.501e-05 | 0.096 ± 0.023             |                 1.546e-50 |               5.203e-05 |\n",
      "| Enemy 20     | Control Stability |      1.463e-110 | 0.372 ± 0.033     | 0.114 ± 0.023           |               6.095e-63 |             0.003868  | 0.115 ± 0.029            |                1.213e-63 |              0.1251    | 0.202 ± 0.092             |                 3.015e-18 |                 0.06061 | 0.107 ± 0.023            |                4.088e-64 |              0.01046   | 0.108 ± 0.020             |                 2.337e-62 |               0.0007517 |\n",
      "\n",
      "==================================================\n",
      "=== Firing Accuracy Analysis Results (E3 Models) ===\n",
      "| Difficulty   | Metric          |   ANOVA p-value | PPO (Mean ± SD)   | E3_DT_C_5 (Mean ± SD)   |   p-val(Mean) E3_DT_C_5 |   p-val(SD) E3_DT_C_5 | E3_DT_C_10 (Mean ± SD)   |   p-val(Mean) E3_DT_C_10 |   p-val(SD) E3_DT_C_10 | E3_DT_S_100 (Mean ± SD)   |   p-val(Mean) E3_DT_S_100 |   p-val(SD) E3_DT_S_100 | E3_DT_SC_5 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_5 |   p-val(SD) E3_DT_SC_5 | E3_DT_SC_10 (Mean ± SD)   |   p-val(Mean) E3_DT_SC_10 |   p-val(SD) E3_DT_SC_10 |\n",
      "|:-------------|:----------------|----------------:|:------------------|:------------------------|------------------------:|----------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|:-------------------------|-------------------------:|-----------------------:|:--------------------------|--------------------------:|------------------------:|\n",
      "| Enemy 10     | Firing Accuracy |       8.66e-29  | 3.758 ± 1.460     | 19.718 ± 8.219          |               1.144e-18 |             2.184e-06 | 20.016 ± 9.567           |                2.436e-16 |              3.258e-07 | 25.080 ± 8.475            |                 1.854e-23 |               1.163e-05 | 19.246 ± 8.407           |                9.747e-18 |              6.153e-07 | 20.467 ± 11.866           |                 2.155e-13 |               3.933e-06 |\n",
      "| Enemy 15     | Firing Accuracy |       3.54e-36  | 5.054 ± 1.847     | 18.716 ± 7.146          |               1.313e-18 |             1.378e-06 | 17.484 ± 6.830           |                9.637e-18 |              8.306e-05 | 22.936 ± 6.495            |                 5.435e-26 |               6.762e-07 | 16.996 ± 5.218           |                1.664e-22 |              3.547e-09 | 20.695 ± 8.556            |                 1.068e-17 |               1.838e-09 |\n",
      "| Enemy 20     | Firing Accuracy |       3.345e-47 | 5.541 ± 2.073     | 19.056 ± 5.010          |               1.621e-26 |             1.455e-05 | 17.041 ± 5.100           |                2.053e-22 |              2.031e-05 | 22.535 ± 4.873            |                 6.242e-33 |               2.119e-05 | 15.900 ± 5.926           |                3.694e-17 |              4.037e-08 | 16.379 ± 5.350            |                 4.288e-20 |               4.877e-06 |\n",
      "\n",
      "[INFO] All analysis complete. TXT files saved to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "def parse_filename(filename):\n",
    "    if \"PPO\" in filename:\n",
    "        return \"PPO\"\n",
    "        \n",
    "    # Check for epoch prefix in filename robustly\n",
    "    if \"E_3\" in filename or \"E3\" in filename:\n",
    "        prefix = \"E3_\"\n",
    "    elif \"E_2\" in filename or \"E2\" in filename:\n",
    "        prefix = \"E2_\"\n",
    "    elif \"E_1\" in filename or \"E1\" in filename:\n",
    "        prefix = \"E1_\"\n",
    "    else:\n",
    "        prefix = \"\"\n",
    "        \n",
    "    dt_match = re.search(r\"DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return f\"{prefix}DT_{dt_match.group(1)}_{dt_match.group(2)}\"\n",
    "    return \"Unknown\"\n",
    "\n",
    "def load_data(folder_path):\n",
    "    files = glob.glob(os.path.join(folder_path, \"*.json\"))\n",
    "    data_dict = {}\n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        model_name = parse_filename(filename)\n",
    "        with open(f, 'r') as json_file:\n",
    "            try:\n",
    "                content = json.load(json_file)\n",
    "                data_dict[model_name] = {\n",
    "                    \"Success Rate\": np.array(content.get(\"all_wins\", [])),\n",
    "                    \"Average Steps\": np.array(content.get(\"all_steps\", [])),\n",
    "                    \"Cumulative Reward\": np.array(content.get(\"all_rewards\", [])),\n",
    "                    \"Control Stability\": np.array(content.get(\"all_smoothness\", [])),\n",
    "                    \"Firing Accuracy\": np.array(content.get(\"all_accuracies\", []))\n",
    "                }\n",
    "            except Exception as e:\n",
    "                print(f\"Error loading {f}: {e}\")\n",
    "    return data_dict\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    base_folder = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision\"\n",
    "    difficulties = [\"EnemyCount10\", \"EnemyCount15\", \"EnemyCount20\"]\n",
    "    baseline = \"PPO\"\n",
    "    target_models = [\"PPO\", \"E3_DT_C_5\", \"E3_DT_C_10\", \"E3_DT_S_100\", \"E3_DT_SC_5\", \"E3_DT_SC_10\"]\n",
    "    \n",
    "    metrics_list = [\"Success Rate\", \"Average Steps\", \"Cumulative Reward\", \"Control Stability\", \"Firing Accuracy\"]\n",
    "    \n",
    "    for metric_name in metrics_list:\n",
    "        results = []\n",
    "        for diff in difficulties:\n",
    "            folder_path = os.path.join(base_folder, diff)\n",
    "            data_dict = load_data(folder_path)\n",
    "            \n",
    "            # Filter specifically for the target models agreed upon\n",
    "            valid_models = {k: v[metric_name] for k, v in data_dict.items() if k in target_models and len(v[metric_name]) > 0}\n",
    "            \n",
    "            if len(valid_models) < 2 or baseline not in valid_models:\n",
    "                continue\n",
    "                \n",
    "            base_data = valid_models[baseline]\n",
    "            base_mean = np.mean(base_data)\n",
    "            base_std = np.std(base_data, ddof=1)\n",
    "            \n",
    "            arrays = list(valid_models.values())\n",
    "            f_stat, anova_p = stats.f_oneway(*arrays) if len(arrays) > 1 else (np.nan, np.nan)\n",
    "            \n",
    "            row = {\n",
    "                \"Difficulty\": diff.replace(\"EnemyCount\", \"Enemy \"),\n",
    "                \"Metric\": metric_name,\n",
    "                \"ANOVA p-value\": f\"{anova_p:.3e}\",\n",
    "                f\"{baseline} (Mean ± SD)\": f\"{base_mean:.3f} ± {base_std:.3f}\"\n",
    "            }\n",
    "            \n",
    "            # Ensure fixed order: DT_C_5, DT_C_10, DT_S_100, DT_SC_5, DT_SC_10\n",
    "            for model_name in target_models:\n",
    "                if model_name == baseline or model_name not in valid_models:\n",
    "                    continue\n",
    "                data = valid_models[model_name]\n",
    "                mean = np.mean(data)\n",
    "                std = np.std(data, ddof=1)\n",
    "                \n",
    "                # T-test for Means\n",
    "                t_stat, t_p_val = stats.ttest_ind(base_data, data, equal_var=False)\n",
    "                \n",
    "                # Levene's test for Variances (Standard Deviations)\n",
    "                # Catch potential warnings if variance is 0\n",
    "                try:\n",
    "                    lev_stat, lev_p_val = stats.levene(base_data, data)\n",
    "                except Exception:\n",
    "                    lev_p_val = np.nan\n",
    "                \n",
    "                row[f\"{model_name} (Mean ± SD)\"] = f\"{mean:.3f} ± {std:.3f}\"\n",
    "                row[f\"p-val(Mean) {model_name}\"] = f\"{t_p_val:.3e}\"\n",
    "                row[f\"p-val(SD) {model_name}\"] = f\"{lev_p_val:.3e}\"\n",
    "                \n",
    "            results.append(row)\n",
    "            \n",
    "        if results:\n",
    "            df = pd.DataFrame(results)\n",
    "            print(f\"\\n{'='*50}\")\n",
    "            print(f\"=== {metric_name} Analysis Results (E3 Models) ===\")\n",
    "            print(df.to_markdown(index=False))\n",
    "            \n",
    "            output_txt = os.path.join(base_folder, f\"{metric_name.replace(' ', '')}_E3_Analysis.txt\")\n",
    "            with open(output_txt, 'w', encoding='utf-8') as f:\n",
    "                f.write(f\"=== {metric_name} Analysis Results (E3 Models) ===\\n\")\n",
    "                f.write(df.to_markdown(index=False))\n",
    "                \n",
    "    print(f\"\\n[INFO] All analysis complete. TXT files saved to {base_folder}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc3fb914-65c2-4854-9712-35c73222a9c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd...\n",
      "Loaded 2864 episode records across all files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:170: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=subset, x=\"TargetRTG\", y=\"Avg Reward\", ax=ax, palette=\"Blues_d\", errorbar=None)\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:186: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=subset, x=\"TargetRTG\", y=\"Success Rate\", ax=ax2, palette=\"Oranges_d\", errorbar=None)\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:207: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"Model\"] = pd.Categorical(df[\"Model\"], categories=[\"DT\", \"BC\", \"PPO\"], ordered=True)\n",
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Success_Rate_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Success_Rate_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Success_Rate_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Avg_Steps_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Avg_Steps_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Avg_Steps_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Control_Stability_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Control_Stability_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Control_Stability_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Accuracy_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Accuracy_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Accuracy_Enemy_20.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Reward_Std_Enemy_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Reward_Std_Enemy_15.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Develope\\AppData\\Local\\Temp\\ipykernel_23756\\553200206.py:242: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved independent metric plot to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\Metric_Reward_Std_Enemy_20.png\n",
      "Saved aggregated plot values to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\plot_aggregated_values.csv\n",
      "Saved summary CSV to C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\\benchmark_summary.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import re\n",
    "\n",
    "# Set style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic' # For Korean text support on Windows\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "def save_aggregated_metrics(df, output_dir):\n",
    "    \"\"\"\n",
    "    그래프(barplot)에 그려지는 것과 100% 동일한 그룹별 평균 수치를 계산하여 CSV로 저장합니다.\n",
    "    \"\"\"\n",
    "    # RTG Test 제외 (일반 지표 그래프와 동일한 조건)\n",
    "    df_clean = df[df[\"Model\"] != \"DT_RTG\"]\n",
    "    \n",
    "    # 추출할 지표 목록\n",
    "    metrics = [\"Success Rate\", \"Avg Steps\", \"Control Stability\", \"Accuracy\", \"Reward Std\"]\n",
    "    \n",
    "    # EnemyCount, Model, Name, Epoch 별로 묶어서 평균(mean) 계산\n",
    "    agg_df = df_clean.groupby(['EnemyCount', 'Model', 'Name', 'Epoch'])[metrics].mean().reset_index()\n",
    "    \n",
    "    # 모델 순서를 DT -> BC -> PPO 순으로 보기 좋게 정렬\n",
    "    agg_df[\"Model\"] = pd.Categorical(agg_df[\"Model\"], categories=[\"DT\", \"BC\", \"PPO\"], ordered=True)\n",
    "    agg_df = agg_df.sort_values(by=['EnemyCount', 'Model', 'Name', 'Epoch'])\n",
    "    \n",
    "    # 소수점 셋째 자리까지만 깔끔하게 남기기 (선택 사항)\n",
    "    agg_df = agg_df.round(3)\n",
    "    \n",
    "    # 파일로 저장\n",
    "    save_path = os.path.join(output_dir, \"plot_aggregated_values.csv\")\n",
    "    agg_df.to_csv(save_path, index=False)\n",
    "    print(f\"Saved aggregated plot values to {save_path}\")\n",
    "    \n",
    "def parse_filename(filename, filepath):\n",
    "    \"\"\"\n",
    "    Parses filenames to extract model metadata.\n",
    "    Expected formats: \n",
    "    - E_{Epoch}_DT_{Type}_{DataSize}.json\n",
    "    - {EnemyCount}_{Version}_PPO.json\n",
    "    - {EnemyCount}_{RTG}_E_{Epoch}_DT_{Type}_{DataSize}.json (RTG Test)\n",
    "    - {EnemyCount}_E_{Epoch}_BC_{DataSize}.json (BC Models)\n",
    "    \"\"\"\n",
    "    parent_dir = os.path.basename(os.path.dirname(filepath))\n",
    "    \n",
    "    # RTG Test Files\n",
    "    rtg_match = re.search(r\"(\\d+)_(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if \"RTG_Test\" in parent_dir and rtg_match:\n",
    "         return {\n",
    "            \"Model\": \"DT_RTG\",\n",
    "            \"EnemyCount\": int(rtg_match.group(1)),\n",
    "            \"TargetRTG\": int(rtg_match.group(2)),\n",
    "            \"Epoch\": int(rtg_match.group(3)),\n",
    "            \"Type\": rtg_match.group(4),\n",
    "            \"DataSize\": int(rtg_match.group(5)),\n",
    "            \"Name\": f\"RTG_{rtg_match.group(2)}\"\n",
    "        }\n",
    "\n",
    "    enemy_count = 10 # Default\n",
    "    if \"EmemyCount15\" in parent_dir or \"EnemyCount15\" in parent_dir:\n",
    "        enemy_count = 15\n",
    "    elif \"EnemyCount10\" in parent_dir:\n",
    "        enemy_count = 10\n",
    "    elif \"EnemyCount20\" in parent_dir:\n",
    "        enemy_count = 20\n",
    "        \n",
    "    # BC Models with Prefix (e.g., 10_E_1_BC_100.json)\n",
    "    bc_match_prefix = re.search(r\"(\\d+)_E_(\\d+)_BC_(\\d+)\", filename)\n",
    "    if bc_match_prefix:\n",
    "        return {\n",
    "            \"Model\": \"BC\",\n",
    "            \"Epoch\": int(bc_match_prefix.group(2)),\n",
    "            \"Type\": \"BC\",\n",
    "            \"DataSize\": int(bc_match_prefix.group(3)),\n",
    "            \"EnemyCount\": int(bc_match_prefix.group(1)),\n",
    "            \"Name\": f\"BC_{bc_match_prefix.group(3)}\"\n",
    "        }\n",
    "    \n",
    "    # DT Models\n",
    "    dt_match = re.search(r\"E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match.group(1)),\n",
    "            \"Type\": dt_match.group(2), \n",
    "            \"DataSize\": int(dt_match.group(3)),\n",
    "            \"EnemyCount\": enemy_count,\n",
    "            \"Name\": f\"DT_{dt_match.group(2)}_{dt_match.group(3)}\"\n",
    "        }\n",
    "    \n",
    "    # DT Models with Prefix\n",
    "    dt_match_prefix = re.search(r\"(\\d+)_E_(\\d+)_DT_([A-Z]+)_(\\d+)\", filename)\n",
    "    if dt_match_prefix:\n",
    "        return {\n",
    "            \"Model\": \"DT\",\n",
    "            \"Epoch\": int(dt_match_prefix.group(2)),\n",
    "            \"Type\": dt_match_prefix.group(3),\n",
    "            \"DataSize\": int(dt_match_prefix.group(4)),\n",
    "            \"EnemyCount\": int(dt_match_prefix.group(1)),\n",
    "            \"Name\": f\"DT_{dt_match_prefix.group(3)}_{dt_match_prefix.group(4)}\"\n",
    "        }\n",
    "\n",
    "    # PPO Models\n",
    "    ppo_match = re.search(r\"(\\d+)_V(\\d+)_PPO\", filename)\n",
    "    if ppo_match:\n",
    "        return {\n",
    "            \"Model\": \"PPO\",\n",
    "            \"Epoch\": \"PPO\",\n",
    "            \"Type\": \"PPO\",\n",
    "            \"DataSize\": 0,\n",
    "            \"EnemyCount\": int(ppo_match.group(1)),\n",
    "            \"Name\": \"PPO\"\n",
    "        }\n",
    "        \n",
    "    return None\n",
    "\n",
    "def load_data(root_dir):\n",
    "    data = []\n",
    "    files = glob.glob(os.path.join(root_dir, \"**\", \"*.json\"), recursive=True)\n",
    "    \n",
    "    for f in files:\n",
    "        filename = os.path.basename(f)\n",
    "        meta = parse_filename(filename, f)\n",
    "        \n",
    "        if meta:\n",
    "            try:\n",
    "                with open(f, 'r') as json_file:\n",
    "                    content = json.load(json_file)\n",
    "                    \n",
    "                    # 새로운 원본 데이터 배열이 있는 경우 에피소드 단위로 50개의 행(Row) 생성\n",
    "                    if \"all_wins\" in content and len(content[\"all_wins\"]) > 0:\n",
    "                        for i in range(len(content[\"all_wins\"])):\n",
    "                            row = meta.copy()\n",
    "                            row[\"Success Rate\"] = content[\"all_wins\"][i] * 100.0\n",
    "                            row[\"Avg Reward\"] = content[\"all_rewards\"][i]\n",
    "                            row[\"Avg Steps\"] = content[\"all_steps\"][i]\n",
    "                            row[\"Control Stability\"] = content[\"all_smoothness\"][i]\n",
    "                            row[\"Accuracy\"] = content[\"all_accuracies\"][i]\n",
    "                            row[\"Reward Std\"] = content.get(\"reward_std\", 0) # 스칼라 폴백\n",
    "                            data.append(row)\n",
    "                    else:\n",
    "                        # 예전 스칼라 데이터 포맷\n",
    "                        meta[\"Success Rate\"] = content.get(\"win_rate\", 0)\n",
    "                        meta[\"Avg Reward\"] = content.get(\"avg_reward\", 0)\n",
    "                        meta[\"Avg Steps\"] = content.get(\"avg_steps\", 0)\n",
    "                        meta[\"Control Stability\"] = content.get(\"smoothness\", 0)\n",
    "                        meta[\"Accuracy\"] = content.get(\"accuracy\", 0)\n",
    "                        meta[\"Reward Std\"] = content.get(\"reward_std\", 0)\n",
    "                        data.append(meta)\n",
    "            except Exception as e:\n",
    "                print(f\"Error reading {f}: {e}\")\n",
    "                \n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "def plot_rtg_test(df, output_dir):\n",
    "    \"\"\"\n",
    "    Plots Avg Reward vs Target RTG for RTG Test data.\n",
    "    \"\"\"\n",
    "    subset = df[df[\"Model\"] == \"DT_RTG\"].sort_values(by=\"TargetRTG\")\n",
    "    if subset.empty:\n",
    "        print(\"No RTG Test data found.\")\n",
    "        return\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    fig.suptitle(\"RTG Controllability Test (Avg Reward vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Avg Reward\", ax=ax, palette=\"Blues_d\", errorbar=None)\n",
    "    \n",
    "    ax.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax.set_ylabel(\"Actual Average Reward\", fontsize=12)\n",
    "    \n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, fmt='%.2f', padding=3, fontsize=10)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    save_path = os.path.join(output_dir, \"RTG_Controllability_Reward.png\")\n",
    "    plt.savefig(save_path, dpi=300)\n",
    "    plt.close(fig)\n",
    "\n",
    "    fig2, ax2 = plt.subplots(figsize=(10, 6))\n",
    "    fig2.suptitle(\"RTG Controllability Test (Success Rate vs Target RTG)\", fontsize=16)\n",
    "    \n",
    "    sns.barplot(data=subset, x=\"TargetRTG\", y=\"Success Rate\", ax=ax2, palette=\"Oranges_d\", errorbar=None)\n",
    "    \n",
    "    ax2.set_xlabel(\"Target RTG (Return-to-Go Setting)\", fontsize=12)\n",
    "    ax2.set_ylabel(\"Success Rate (%)\", fontsize=12)\n",
    "    \n",
    "    for container in ax2.containers:\n",
    "        ax2.bar_label(container, fmt='%.1f', padding=3, fontsize=10)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    save_path2 = os.path.join(output_dir, \"RTG_Controllability_SuccessRate.png\")\n",
    "    plt.savefig(save_path2, dpi=300)\n",
    "    plt.close(fig2)\n",
    "\n",
    "def plot_by_metric_split(df, output_dir):\n",
    "    # Filter out RTG Test data for general metrics\n",
    "    df = df[df[\"Model\"] != \"DT_RTG\"]\n",
    "    \n",
    "    metrics = [\"Success Rate\", \"Avg Steps\", \"Control Stability\", \"Accuracy\", \"Reward Std\"]\n",
    "    enemy_counts = [10, 15, 20]\n",
    "    \n",
    "    # 모델 순서 지정\n",
    "    df[\"Model\"] = pd.Categorical(df[\"Model\"], categories=[\"DT\", \"BC\", \"PPO\"], ordered=True)\n",
    "    df = df.sort_values(by=[\"Model\", \"Type\", \"DataSize\", \"Epoch\"])\n",
    "    for metric in metrics:\n",
    "        for count in enemy_counts:\n",
    "            subset = df[df[\"EnemyCount\"] == count]\n",
    "            \n",
    "            if subset.empty:\n",
    "                continue\n",
    "                \n",
    "            fig, ax = plt.subplots(figsize=(10, 7))\n",
    "            fig.suptitle(f\"{metric} (Enemy Count: {count})\", fontsize=18, fontweight='bold')\n",
    "            \n",
    "            # 기본 그래프 그리기\n",
    "            sns.barplot(data=subset, x=\"Name\", y=metric, hue=\"Epoch\", ax=ax, palette=\"muted\", capsize=0.1, errorbar=('ci', 95))\n",
    "            \n",
    "            # === PPO 막대 및 오차막대 정중앙 강제 이동 로직 ===\n",
    "            x_labels = [t.get_text() for t in ax.get_xticklabels()]\n",
    "            if \"PPO\" in x_labels:\n",
    "                ppo_idx = x_labels.index(\"PPO\")\n",
    "                \n",
    "                # 1) 막대(Patch) 위치를 정중앙으로 이동\n",
    "                for patch in ax.patches:\n",
    "                    patch_center = patch.get_x() + patch.get_width() / 2\n",
    "                    # x축 PPO 위치 근처에 있으면서 높이가 존재하는 막대만 선택\n",
    "                    if abs(patch_center - ppo_idx) < 0.5 and patch.get_height() > 0:\n",
    "                        patch.set_x(ppo_idx - patch.get_width() / 2)\n",
    "                        \n",
    "                # 2) 에러바(오차막대) 위치를 정중앙으로 이동\n",
    "                for line in ax.lines:\n",
    "                    xdata = line.get_xdata()\n",
    "                    if len(xdata) > 0 and abs(xdata[0] - ppo_idx) < 0.5:\n",
    "                        offset = ppo_idx - (xdata[0] + xdata[-1]) / 2.0\n",
    "                        line.set_xdata(xdata + offset)\n",
    "            # ==================================================\n",
    "            \n",
    "            ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)\n",
    "            ax.set_xlabel(\"Model Name\", fontsize=14, fontweight='bold')\n",
    "            ax.set_ylabel(metric, fontsize=14, fontweight='bold')\n",
    "            \n",
    "            # 숫자 라벨 추가\n",
    "            for container in ax.containers:\n",
    "                fmt_str = '%.1f' if metric == \"Success Rate\" else '%.2f'\n",
    "                if metric == \"Control Stability\": fmt_str = '%.3f'\n",
    "                ax.bar_label(container, fmt=fmt_str, padding=3, fontsize=12, rotation=45)\n",
    "            plt.tight_layout()\n",
    "            \n",
    "            safe_metric_name = metric.replace(\" \", \"_\")\n",
    "            save_path = os.path.join(output_dir, f\"Metric_{safe_metric_name}_Enemy_{count}.png\")\n",
    "            plt.savefig(save_path, dpi=300)\n",
    "            plt.close(fig)\n",
    "            print(f\"Saved independent metric plot to {save_path}\")\n",
    "\n",
    "\n",
    "def main():\n",
    "    # 최신 결과 데이터가 포함된 FINAL_RESULT_For_Revision_2nd 폴더로 경로 변경\n",
    "    root_dir = r\"C:\\Users\\Develope\\DT\\FINAL_RESULT_For_Revision_2nd\"\n",
    "    \n",
    "    print(f\"Loading data from {root_dir}...\")\n",
    "    df = load_data(root_dir)\n",
    "    \n",
    "    if df.empty:\n",
    "        print(\"No data found!\")\n",
    "        return\n",
    "        \n",
    "    print(f\"Loaded {len(df)} episode records across all files.\")\n",
    "    \n",
    "    # Plot RTG Test Results (if any exist in this folder)\n",
    "    plot_rtg_test(df, root_dir)\n",
    "    \n",
    "    # Plot split, high-resolution metrics with error bars\n",
    "    plot_by_metric_split(df, root_dir)\n",
    "\n",
    "    # =============== 추가된 부분 ===============\n",
    "    # 엑셀(CSV) 파일로 그룹화된 평균 수치를 저장\n",
    "    save_aggregated_metrics(df, root_dir)\n",
    "    # ==========================================\n",
    "    \n",
    "    # Save CSV\n",
    "    csv_path = os.path.join(root_dir, \"benchmark_summary.csv\")\n",
    "    df.to_csv(csv_path, index=False)\n",
    "    print(f\"Saved summary CSV to {csv_path}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aab18b94-e0d9-4171-9741-67065aa264fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========================================================\n",
      "결과를 복사해서 논문 Table 2 (Average Steps)에 추가하세요\n",
      "=========================================================\n",
      "\n",
      "[Target 10 추가용]\n",
      "E3_BC_100\t733.240 ± 213.456\t6.26e-16\tYes (Slower)\n",
      "\n",
      "[Target 15 추가용]\n",
      "E3_BC_100\t937.500 ± 126.328\t1.25e-31\tYes (Slower)\n",
      "\n",
      "[Target 20 추가용]\n",
      "E3_BC_100\t991.360 ± 68.165\t5.40e-29\tYes (Slower)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "# PPO Baseline statistics for Target 10, 15, 20\n",
    "ppo_stats = {\n",
    "    10: (357.480, 168.475),\n",
    "    15: (390.200, 173.051),\n",
    "    20: (464.160, 174.620)\n",
    "}\n",
    "base_path = \"C:/Users/Develope/DT/FINAL_RESULT_For_Revision_2nd\"\n",
    "print(\"=========================================================\")\n",
    "print(\"결과를 복사해서 논문 Table 2 (Average Steps)에 추가하세요\")\n",
    "print(\"=========================================================\\n\")\n",
    "for target in [10, 15, 20]:\n",
    "    ppo_mean, ppo_sd = ppo_stats[target]\n",
    "    \n",
    "    file_path = f\"{base_path}/EnemyCount{target}/{target}_E_3_BC_100.json\"\n",
    "    with open(file_path, 'r') as f:\n",
    "        data = json.load(f)\n",
    "        \n",
    "    steps = data[\"all_steps\"]\n",
    "    \n",
    "    mean = np.mean(steps)\n",
    "    sd = np.std(steps, ddof=1)\n",
    "    \n",
    "    # Welch's t-test comparing against PPO (n=50 for both)\n",
    "    t_stat, p_val = st.ttest_ind_from_stats(\n",
    "        mean1=mean, std1=sd, nobs1=50,\n",
    "        mean2=ppo_mean, std2=ppo_sd, nobs2=50,\n",
    "        equal_var=False\n",
    "    )\n",
    "    \n",
    "    # p-value 포맷팅 (기존 표 스타일 적용)\n",
    "    if p_val < 0.0001:\n",
    "        p_str = f\"{p_val:.2e}\"\n",
    "    else:\n",
    "        p_str = f\"{p_val:.4f}\"\n",
    "        \n",
    "    print(f\"[Target {target} 추가용]\")\n",
    "    print(f\"E3_BC_100\\t{mean:.3f} ± {sd:.3f}\\t{p_str}\\tYes (Slower)\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a478c0b1-562e-4d54-b0ee-7c6037786322",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================================================\n",
      "결과를 복사해서 논문 Table 4 (Control Stability)에 추가하세요\n",
      "===========================================================\n",
      "\n",
      "[Target 10 추가용]\n",
      "E3_BC_100\t0.224 ± 0.074\t2.17e-19\tYes (More Stable)\n",
      "\n",
      "[Target 15 추가용]\n",
      "E3_BC_100\t0.189 ± 0.082\t3.19e-23\tYes (More Stable)\n",
      "\n",
      "[Target 20 추가용]\n",
      "E3_BC_100\t0.151 ± 0.069\t4.02e-31\tYes (More Stable)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "# PPO Baseline statistics (Control Stability) for Target 10, 15, 20\n",
    "ppo_stats = {\n",
    "    10: (0.369, 0.043),\n",
    "    15: (0.377, 0.045),\n",
    "    20: (0.372, 0.033)\n",
    "}\n",
    "base_path = \"C:/Users/Develope/DT/FINAL_RESULT_For_Revision_2nd\"\n",
    "print(\"===========================================================\")\n",
    "print(\"결과를 복사해서 논문 Table 4 (Control Stability)에 추가하세요\")\n",
    "print(\"===========================================================\\n\")\n",
    "for target in [10, 15, 20]:\n",
    "    ppo_mean, ppo_sd = ppo_stats[target]\n",
    "    \n",
    "    file_path = f\"{base_path}/EnemyCount{target}/{target}_E_3_BC_100.json\"\n",
    "    with open(file_path, 'r') as f:\n",
    "        data = json.load(f)\n",
    "        \n",
    "    # Control Stability는 all_smoothness 배열을 사용\n",
    "    smoothness = data[\"all_smoothness\"]\n",
    "    \n",
    "    mean = np.mean(smoothness)\n",
    "    sd = np.std(smoothness, ddof=1)\n",
    "    \n",
    "    # Welch's t-test comparing against PPO (n=50 for both)\n",
    "    t_stat, p_val = st.ttest_ind_from_stats(\n",
    "        mean1=mean, std1=sd, nobs1=50,\n",
    "        mean2=ppo_mean, std2=ppo_sd, nobs2=50,\n",
    "        equal_var=False\n",
    "    )\n",
    "    \n",
    "    # p-value 포맷팅 (논문 스타일)\n",
    "    if p_val < 0.0001:\n",
    "        p_str = f\"{p_val:.2e}\"\n",
    "    else:\n",
    "        p_str = f\"{p_val:.4f}\"\n",
    "        \n",
    "    # Control Stability는 수치가 낮을수록 안정적(More Stable)\n",
    "    if p_val < 0.05:\n",
    "        if mean < ppo_mean:\n",
    "            sig_str = \"Yes (More Stable)\"\n",
    "        else:\n",
    "            sig_str = \"Yes (Less Stable)\"\n",
    "    else:\n",
    "        sig_str = \"No\"\n",
    "        \n",
    "    print(f\"[Target {target} 추가용]\")\n",
    "    print(f\"E3_BC_100\\t{mean:.3f} ± {sd:.3f}\\t{p_str}\\t{sig_str}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d8c5ce25-bead-45ff-a103-c231566ab288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========================================================\n",
      "결과를 복사해서 논문 표(Firing Accuracy)에 추가하세요\n",
      "=========================================================\n",
      "\n",
      "[Target 10 추가용]\n",
      "E3_BC_100\t8.687 ± 4.117\t4.72e-11\tYes (More Accurate)\n",
      "\n",
      "[Target 15 추가용]\n",
      "E3_BC_100\t9.101 ± 3.959\t8.39e-09\tYes (More Accurate)\n",
      "\n",
      "[Target 20 추가용]\n",
      "E3_BC_100\t11.049 ± 3.671\t4.00e-14\tYes (More Accurate)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "# PPO Baseline statistics (Firing Accuracy) for Target 10, 15, 20\n",
    "ppo_stats = {\n",
    "    10: (3.758, 1.460),\n",
    "    15: (5.054, 1.847),\n",
    "    20: (5.541, 2.073)\n",
    "}\n",
    "base_path = \"C:/Users/Develope/DT/FINAL_RESULT_For_Revision_2nd\"\n",
    "print(\"=========================================================\")\n",
    "print(\"결과를 복사해서 논문 표(Firing Accuracy)에 추가하세요\")\n",
    "print(\"=========================================================\\n\")\n",
    "for target in [10, 15, 20]:\n",
    "    ppo_mean, ppo_sd = ppo_stats[target]\n",
    "    \n",
    "    file_path = f\"{base_path}/EnemyCount{target}/{target}_E_3_BC_100.json\"\n",
    "    with open(file_path, 'r') as f:\n",
    "        data = json.load(f)\n",
    "        \n",
    "    # Firing Accuracy는 all_accuracies 배열을 사용\n",
    "    accuracy = data[\"all_accuracies\"]\n",
    "    \n",
    "    mean = np.mean(accuracy)\n",
    "    sd = np.std(accuracy, ddof=1)\n",
    "    \n",
    "    # Welch's t-test comparing against PPO (n=50 for both)\n",
    "    t_stat, p_val = st.ttest_ind_from_stats(\n",
    "        mean1=mean, std1=sd, nobs1=50,\n",
    "        mean2=ppo_mean, std2=ppo_sd, nobs2=50,\n",
    "        equal_var=False\n",
    "    )\n",
    "    \n",
    "    # p-value 포맷팅 (논문 스타일)\n",
    "    if p_val < 0.0001:\n",
    "        p_str = f\"{p_val:.2e}\"\n",
    "    else:\n",
    "        p_str = f\"{p_val:.4f}\"\n",
    "        \n",
    "    # Firing Accuracy는 높을수록 정확(More Accurate)\n",
    "    if p_val < 0.05:\n",
    "        if mean > ppo_mean:\n",
    "            sig_str = \"Yes (More Accurate)\"\n",
    "        else:\n",
    "            sig_str = \"Yes (Less Accurate)\"\n",
    "    else:\n",
    "        sig_str = \"No\"\n",
    "        \n",
    "    print(f\"[Target {target} 추가용]\")\n",
    "    print(f\"E3_BC_100\\t{mean:.3f} ± {sd:.3f}\\t{p_str}\\t{sig_str}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cb60e1b8-15ce-48fa-8dd6-ff14c572063a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========================================================\n",
      "결과를 복사해서 논문 표(Reward Stability)에 추가하세요\n",
      "=========================================================\n",
      "\n",
      "[Target 10 추가용]\n",
      "E3_BC_100\t32.266 ± 3.537\t0.1718\t1.65e-07\tSimilar Mean\n",
      "\t\t\t\t\t\tLess Stable\n",
      "\n",
      "[Target 15 추가용]\n",
      "E3_BC_100\t43.675 ± 6.612\t0.0048\t1.92e-07\tLower Mean\n",
      "\t\t\t\t\t\tLess Stable\n",
      "\n",
      "[Target 20 추가용]\n",
      "E3_BC_100\t48.791 ± 6.975\t1.81e-17\t4.76e-12\tLower Mean\n",
      "\t\t\t\t\t\tLess Stable\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import json\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "base_path = \"C:/Users/Develope/DT/FINAL_RESULT_For_Revision_2nd\"\n",
    "print(\"=========================================================\")\n",
    "print(\"결과를 복사해서 논문 표(Reward Stability)에 추가하세요\")\n",
    "print(\"=========================================================\\n\")\n",
    "for target in [10, 15, 20]:\n",
    "    # 1) BC_100 데이터 로드\n",
    "    bc_file = f\"{base_path}/EnemyCount{target}/{target}_E_3_BC_100.json\"\n",
    "    with open(bc_file, 'r') as f:\n",
    "        bc_data = json.load(f)\n",
    "    bc_rewards = np.array(bc_data[\"all_rewards\"])\n",
    "    \n",
    "    # 2) PPO 데이터 로드\n",
    "    ppo_files = glob.glob(f\"{base_path}/EnemyCount{target}/*_PPO.json\")\n",
    "    if not ppo_files:\n",
    "        print(f\"Target {target} PPO 파일을 찾을 수 없습니다.\")\n",
    "        continue\n",
    "        \n",
    "    with open(ppo_files[0], 'r') as f:\n",
    "        ppo_data = json.load(f)\n",
    "        \n",
    "    if \"all_rewards\" in ppo_data and len(ppo_data[\"all_rewards\"]) > 0:\n",
    "        # 원본 배열이 있는 경우 (가장 정확한 Levene's test 사용)\n",
    "        ppo_rewards = np.array(ppo_data[\"all_rewards\"])\n",
    "        t_stat, p_val_mean = st.ttest_ind(bc_rewards, ppo_rewards, equal_var=False)\n",
    "        stat, p_val_sd = st.levene(bc_rewards, ppo_rewards)\n",
    "        \n",
    "        ppo_mean_val = np.mean(ppo_rewards)\n",
    "        ppo_sd_val = np.std(ppo_rewards, ddof=1)\n",
    "    else:\n",
    "        # 예전 포맷이라 원본 배열이 없는 경우 (요약 통계량으로 t-test 및 F-test 근사)\n",
    "        ppo_mean_val = ppo_data.get(\"avg_reward\", 0)\n",
    "        ppo_sd_val = ppo_data.get(\"reward_std\", 0)\n",
    "        \n",
    "        t_stat, p_val_mean = st.ttest_ind_from_stats(\n",
    "            mean1=np.mean(bc_rewards), std1=np.std(bc_rewards, ddof=1), nobs1=50,\n",
    "            mean2=ppo_mean_val, std2=ppo_sd_val, nobs2=50, equal_var=False)\n",
    "        \n",
    "        var1 = np.var(bc_rewards, ddof=1)\n",
    "        var2 = ppo_sd_val ** 2\n",
    "        f_stat = var1 / var2 if var1 > var2 else var2 / var1\n",
    "        p_val_sd = 2 * (1 - st.f.cdf(f_stat, 49, 49))\n",
    "        \n",
    "    mean = np.mean(bc_rewards)\n",
    "    sd = np.std(bc_rewards, ddof=1)\n",
    "    \n",
    "    # p-value 포맷팅 (논문 스타일)\n",
    "    p_mean_str = f\"{p_val_mean:.2e}\" if p_val_mean < 0.0001 else f\"{p_val_mean:.4f}\"\n",
    "    p_sd_str = f\"{p_val_sd:.2e}\" if p_val_sd < 0.0001 else f\"{p_val_sd:.4f}\"\n",
    "    \n",
    "    # Significant 판별 로직\n",
    "    if p_val_mean < 0.05:\n",
    "        mean_sig = \"Higher Mean\" if mean > ppo_mean_val else \"Lower Mean\"\n",
    "    else:\n",
    "        mean_sig = \"Similar Mean\"\n",
    "        \n",
    "    if p_val_sd < 0.05:\n",
    "        sd_sig = \"High Stability\" if sd < ppo_sd_val else \"Less Stable\"\n",
    "    else:\n",
    "        sd_sig = \"Similar Stability\"\n",
    "        \n",
    "    print(f\"[Target {target} 추가용]\")\n",
    "    print(f\"E3_BC_100\\t{mean:.3f} ± {sd:.3f}\\t{p_mean_str}\\t{p_sd_str}\\t{mean_sig}\")\n",
    "    print(f\"\\t\\t\\t\\t\\t\\t{sd_sig}\\n\")"
   ]
  }
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