b2txt25/data_analyse/data-view.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d316ehisw2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 验证PyTorch unfold行为 ===\n",
      "原始张量形状: torch.Size([1, 1, 1, 30])\n",
      "原始张量内容: [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0]\n",
      "Unfold后形状: torch.Size([1, 1, 1, 5, 14])\n",
      "输出时间步数: 5\n",
      "\n",
      "每个输出时间步对应的原始时间步:\n",
      "输出  0: 原始 [ 0:14] -> [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0]\n",
      "输出  1: 原始 [ 4:18] -> [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0]\n",
      "输出  2: 原始 [ 8:22] -> [8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0]\n",
      "输出  3: 原始 [12:26] -> [12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0]\n",
      "输出  4: 原始 [16:30] -> [16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0]\n",
      "\n",
      "=== 验证转换函数 ===\n",
      "测试案例:\n",
      "输出范围       中心位置            保守范围            可能范围            实际长度(ms)    \n",
      "--------------------------------------------------------------------------------\n",
      "0-0        6.5-6.5         0-13            4-8                   80\n",
      "1-1        10.5-10.5       4-17            8-12                  80\n",
      "0-1        6.5-10.5        0-17            4-12                 160\n",
      "206-207    830.5-834.5     824-841         828-836              160\n",
      "5-10       26.5-46.5       20-53           24-48                480\n",
      "\n",
      "=== 实际数据验证 ===\n",
      "转换结果统计 (基于 4000 个segments):\n",
      "输出时间步长度:   平均 1.6, 中位数 2.0\n",
      "简单映射长度:     平均 3.4, 中位数 5.0\n",
      "保守映射长度:     平均 16.4, 中位数 18.0\n",
      "可能映射长度:     平均 7.4, 中位数 9.0\n",
      "\n",
      "原始时间步 vs 输出时间步的比例:\n",
      "简单映射:    平均比例 1.8x\n",
      "保守映射:    平均比例 11.3x\n",
      "可能映射:    平均比例 4.7x\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 关键发现 ===\n",
      "1. 输出时间步到原始时间步的映射比例约为 4:1\n",
      "2. 简单映射(中心位置)的平均比例: 1.8x\n",
      "3. 保守映射(完整范围)的平均比例: 11.3x\n",
      "4. 平均音素长度: 128 ms\n",
      "5. 中位数音素长度: 160 ms\n",
      "\n",
      "=== 置信度计算原理解释 ===\n",
      "基于之前的代码分析，置信度计算如下:\n",
      "1. 对每个输出时间步t，计算softmax概率: softmax(log_probs[t])\n",
      "2. 取该时间步预测的音素token对应的概率作为置信度\n",
      "3. 对连续的相同音素时间步，计算平均置信度\n",
      "4. 这个置信度反映了模型对该音素预测的确信程度\n",
      "\n",
      "例如您的例子: confidence = 0.9999608993530273\n",
      "这表示模型对该音素'JH'的预测有极高的置信度(99.996%)\n"
     ]
    }
   ],
   "source": [
    "# =============================================================================\n",
    "# 验证时间戳复原算法的正确性\n",
    "# =============================================================================\n",
    "\n",
    "import torch\n",
    "\n",
    "# 创建一个模拟测试来验证我们的理解\n",
    "def test_unfold_behavior():\n",
    "    \"\"\"测试PyTorch unfold的实际行为来验证我们的理解\"\"\"\n",
    "    \n",
    "    print(\"=== 验证PyTorch unfold行为 ===\")\n",
    "    \n",
    "    # 创建一个简单的序列用于测试\n",
    "    original_length = 30\n",
    "    patch_size = 14\n",
    "    patch_stride = 4\n",
    "    \n",
    "    # 创建一个简单的测试张量 [batch=1, features=1, height=1, time_steps=30]\n",
    "    test_tensor = torch.arange(original_length).float().unsqueeze(0).unsqueeze(0).unsqueeze(0)\n",
    "    print(f\"原始张量形状: {test_tensor.shape}\")\n",
    "    print(f\"原始张量内容: {test_tensor.squeeze().tolist()}\")\n",
    "    \n",
    "    # 应用unfold操作 (模拟RNN模型中的操作)\n",
    "    unfolded = test_tensor.unfold(3, patch_size, patch_stride)\n",
    "    print(f\"Unfold后形状: {unfolded.shape}\")\n",
    "    \n",
    "    # 分析每个输出时间步对应的输入\n",
    "    num_patches = unfolded.shape[3]\n",
    "    print(f\"输出时间步数: {num_patches}\")\n",
    "    print()\n",
    "    \n",
    "    print(\"每个输出时间步对应的原始时间步:\")\n",
    "    for i in range(min(10, num_patches)):  # 只显示前10个\n",
    "        patch = unfolded[0, 0, 0, i, :].tolist()\n",
    "        start_idx = i * patch_stride\n",
    "        end_idx = start_idx + patch_size - 1\n",
    "        print(f\"输出 {i:2d}: 原始 [{start_idx:2d}:{end_idx+1:2d}] -> {patch}\")\n",
    "    \n",
    "    return unfolded\n",
    "\n",
    "# 运行测试\n",
    "unfolded_result = test_unfold_behavior()\n",
    "\n",
    "# 验证我们的转换函数\n",
    "def validate_conversion_function():\n",
    "    \"\"\"验证我们的时间戳转换函数\"\"\"\n",
    "    \n",
    "    print(f\"\\n=== 验证转换函数 ===\")\n",
    "    \n",
    "    # 测试几个具体案例\n",
    "    test_cases = [\n",
    "        (0, 0),      # 第一个输出时间步\n",
    "        (1, 1),      # 第二个输出时间步\n",
    "        (0, 1),      # 跨越两个输出时间步\n",
    "        (206, 207),  # 您提供的例子\n",
    "        (5, 10),     # 较长的segment\n",
    "    ]\n",
    "    \n",
    "    print(\"测试案例:\")\n",
    "    print(f\"{'输出范围':10s} {'中心位置':15s} {'保守范围':15s} {'可能范围':15s} {'实际长度(ms)':12s}\")\n",
    "    print(\"-\" * 80)\n",
    "    \n",
    "    for output_start, output_end in test_cases:\n",
    "        conversion = convert_output_timestamp_to_original(output_start, output_end)\n",
    "        \n",
    "        output_range = f\"{output_start}-{output_end}\"\n",
    "        center_pos = f\"{conversion['center_positions'][0]:.1f}-{conversion['center_positions'][1]:.1f}\"\n",
    "        conservative = f\"{conversion['conservative_mapping'][0]}-{conversion['conservative_mapping'][1]}\"\n",
    "        likely = f\"{conversion['likely_mapping'][0]}-{conversion['likely_mapping'][1]}\"\n",
    "        duration_ms = conversion['output_duration'] * PATCH_STRIDE * ORIGINAL_BIN_MS\n",
    "        \n",
    "        print(f\"{output_range:10s} {center_pos:15s} {conservative:15s} {likely:15s} {duration_ms:8d}\")\n",
    "\n",
    "validate_conversion_function()\n",
    "\n",
    "# 如果有实际数据，进行更详细的验证\n",
    "if 'phoneme_data' in locals() and phoneme_data is not None:\n",
    "    print(f\"\\n=== 实际数据验证 ===\")\n",
    "    \n",
    "    # 统计转换结果的分布\n",
    "    all_output_durations = []\n",
    "    all_simple_durations = []\n",
    "    all_conservative_durations = []\n",
    "    all_likely_durations = []\n",
    "    \n",
    "    for phoneme, segments in phoneme_data.items():\n",
    "        for segment in segments[:100]:  # 每个音素取前100个进行统计\n",
    "            output_start = segment['start_time']\n",
    "            output_end = segment['end_time']\n",
    "            \n",
    "            conversion = convert_output_timestamp_to_original(output_start, output_end)\n",
    "            \n",
    "            output_duration = conversion['output_duration']\n",
    "            simple_duration = conversion['simple_mapping'][1] - conversion['simple_mapping'][0] + 1\n",
    "            conservative_duration = conversion['conservative_mapping'][1] - conversion['conservative_mapping'][0] + 1\n",
    "            likely_duration = conversion['likely_mapping'][1] - conversion['likely_mapping'][0] + 1\n",
    "            \n",
    "            all_output_durations.append(output_duration)\n",
    "            all_simple_durations.append(simple_duration)\n",
    "            all_conservative_durations.append(conservative_duration)\n",
    "            all_likely_durations.append(likely_duration)\n",
    "    \n",
    "    print(f\"转换结果统计 (基于 {len(all_output_durations)} 个segments):\")\n",
    "    print(f\"输出时间步长度:   平均 {np.mean(all_output_durations):.1f}, 中位数 {np.median(all_output_durations):.1f}\")\n",
    "    print(f\"简单映射长度:     平均 {np.mean(all_simple_durations):.1f}, 中位数 {np.median(all_simple_durations):.1f}\")\n",
    "    print(f\"保守映射长度:     平均 {np.mean(all_conservative_durations):.1f}, 中位数 {np.median(all_conservative_durations):.1f}\")\n",
    "    print(f\"可能映射长度:     平均 {np.mean(all_likely_durations):.1f}, 中位数 {np.median(all_likely_durations):.1f}\")\n",
    "    \n",
    "    # 转换比例分析\n",
    "    simple_ratios = [s/o for s, o in zip(all_simple_durations, all_output_durations) if o > 0]\n",
    "    conservative_ratios = [c/o for c, o in zip(all_conservative_durations, all_output_durations) if o > 0]\n",
    "    likely_ratios = [l/o for l, o in zip(all_likely_durations, all_output_durations) if o > 0]\n",
    "    \n",
    "    print(f\"\\n原始时间步 vs 输出时间步的比例:\")\n",
    "    print(f\"简单映射:    平均比例 {np.mean(simple_ratios):.1f}x\")\n",
    "    print(f\"保守映射:    平均比例 {np.mean(conservative_ratios):.1f}x\") \n",
    "    print(f\"可能映射:    平均比例 {np.mean(likely_ratios):.1f}x\")\n",
    "    \n",
    "    # 创建可视化\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    \n",
    "    # 子图1: 长度分布对比\n",
    "    plt.subplot(2, 3, 1)\n",
    "    plt.hist([all_output_durations, all_simple_durations, all_conservative_durations, all_likely_durations], \n",
    "             bins=30, alpha=0.7, label=['输出', '简单', '保守', '可能'])\n",
    "    plt.xlabel('时间步长度')\n",
    "    plt.ylabel('频次')\n",
    "    plt.title('不同映射方法的长度分布')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图2: 比例分布\n",
    "    plt.subplot(2, 3, 2)\n",
    "    plt.hist([simple_ratios, conservative_ratios, likely_ratios], \n",
    "             bins=30, alpha=0.7, label=['简单', '保守', '可能'])\n",
    "    plt.xlabel('原始/输出 时间步比例')\n",
    "    plt.ylabel('频次')\n",
    "    plt.title('时间步长度比例分布')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图3: 输出时长 vs 简单映射时长散点图\n",
    "    plt.subplot(2, 3, 3)\n",
    "    plt.scatter(all_output_durations, all_simple_durations, alpha=0.5, s=10)\n",
    "    plt.xlabel('输出时间步长度')\n",
    "    plt.ylabel('简单映射时间步长度')\n",
    "    plt.title('输出 vs 简单映射长度')\n",
    "    plt.plot([0, max(all_output_durations)], [0, max(all_output_durations)*PATCH_STRIDE], 'r--', alpha=0.7, label=f'{PATCH_STRIDE}x线')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图4: 输出时长 vs 保守映射时长散点图\n",
    "    plt.subplot(2, 3, 4)\n",
    "    plt.scatter(all_output_durations, all_conservative_durations, alpha=0.5, s=10, color='orange')\n",
    "    plt.xlabel('输出时间步长度')\n",
    "    plt.ylabel('保守映射时间步长度')\n",
    "    plt.title('输出 vs 保守映射长度')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图5: 输出时长 vs 可能映射时长散点图\n",
    "    plt.subplot(2, 3, 5)\n",
    "    plt.scatter(all_output_durations, all_likely_durations, alpha=0.5, s=10, color='green')\n",
    "    plt.xlabel('输出时间步长度')\n",
    "    plt.ylabel('可能映射时间步长度')\n",
    "    plt.title('输出 vs 可能映射长度')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图6: 实际时长分布(毫秒)\n",
    "    plt.subplot(2, 3, 6)\n",
    "    actual_durations_ms = [d * PATCH_STRIDE * ORIGINAL_BIN_MS for d in all_output_durations]\n",
    "    plt.hist(actual_durations_ms, bins=50, alpha=0.7, color='purple')\n",
    "    plt.xlabel('实际时长 (ms)')\n",
    "    plt.ylabel('频次')\n",
    "    plt.title('音素segment实际时长分布')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 关键洞察\n",
    "    print(f\"\\n=== 关键发现 ===\")\n",
    "    print(f\"1. 输出时间步到原始时间步的映射比例约为 {PATCH_STRIDE}:1\")\n",
    "    print(f\"2. 简单映射(中心位置)的平均比例: {np.mean(simple_ratios):.1f}x\")\n",
    "    print(f\"3. 保守映射(完整范围)的平均比例: {np.mean(conservative_ratios):.1f}x\")\n",
    "    print(f\"4. 平均音素长度: {np.mean(actual_durations_ms):.0f} ms\")\n",
    "    print(f\"5. 中位数音素长度: {np.median(actual_durations_ms):.0f} ms\")\n",
    "\n",
    "print(f\"\\n=== 置信度计算原理解释 ===\")\n",
    "print(\"基于之前的代码分析，置信度计算如下:\")\n",
    "print(\"1. 对每个输出时间步t，计算softmax概率: softmax(log_probs[t])\")\n",
    "print(\"2. 取该时间步预测的音素token对应的概率作为置信度\")\n",
    "print(\"3. 对连续的相同音素时间步，计算平均置信度\")\n",
    "print(\"4. 这个置信度反映了模型对该音素预测的确信程度\")\n",
    "print()\n",
    "print(\"例如您的例子: confidence = 0.9999608993530273\")\n",
    "print(\"这表示模型对该音素'JH'的预测有极高的置信度(99.996%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beufhfhmaao",
   "metadata": {},
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# 滑动窗口时间戳复原算法\n",
    "# =============================================================================\n",
    "\n",
    "# 首先理解PyTorch unfold的工作原理\n",
    "print(\"=== PyTorch unfold操作分析 ===\")\n",
    "print(\"unfold(dimension, size, step) 的含义:\")\n",
    "print(\"- dimension: 在哪个维度上进行unfold\")\n",
    "print(\"- size: 每个窗口的大小 (patch_size = 14)\")\n",
    "print(\"- step: 窗口间的步长 (patch_stride = 4)\")\n",
    "print()\n",
    "\n",
    "# 配置参数\n",
    "PATCH_SIZE = 14    # 滑动窗口大小\n",
    "PATCH_STRIDE = 4   # 滑动窗口步长\n",
    "ORIGINAL_BIN_MS = 20  # 原始时间bin大小(ms)\n",
    "\n",
    "print(f\"配置参数:\")\n",
    "print(f\"- PATCH_SIZE: {PATCH_SIZE}\")\n",
    "print(f\"- PATCH_STRIDE: {PATCH_STRIDE}\")\n",
    "print(f\"- ORIGINAL_BIN_MS: {ORIGINAL_BIN_MS}\")\n",
    "print()\n",
    "\n",
    "# 模拟unfold操作来理解输出时间步与原始时间步的对应关系\n",
    "def analyze_unfold_mapping(original_length, patch_size, patch_stride):\n",
    "    \"\"\"分析unfold操作后的时间步映射关系\"\"\"\n",
    "    \n",
    "    # 计算输出长度 (模拟unfold的行为)\n",
    "    output_length = (original_length - patch_size) // patch_stride + 1\n",
    "    \n",
    "    print(f\"原始序列长度: {original_length}\")\n",
    "    print(f\"输出序列长度: {output_length}\")\n",
    "    print()\n",
    "    \n",
    "    # 为每个输出时间步计算对应的原始时间步范围\n",
    "    mappings = []\n",
    "    for output_idx in range(output_length):\n",
    "        start_idx = output_idx * patch_stride\n",
    "        end_idx = start_idx + patch_size - 1\n",
    "        center_idx = (start_idx + end_idx) / 2\n",
    "        \n",
    "        mappings.append({\n",
    "            'output_idx': output_idx,\n",
    "            'original_start': start_idx,\n",
    "            'original_end': end_idx,\n",
    "            'original_center': center_idx,\n",
    "            'original_range': f\"[{start_idx}:{end_idx+1}]\"\n",
    "        })\n",
    "    \n",
    "    return mappings, output_length\n",
    "\n",
    "# 分析一个示例序列\n",
    "print(\"=== 滑动窗口映射分析 ===\")\n",
    "original_length = 100  # 假设原始序列有100个时间步\n",
    "mappings, output_length = analyze_unfold_mapping(original_length, PATCH_SIZE, PATCH_STRIDE)\n",
    "\n",
    "print(\"前10个输出时间步对应的原始时间步范围:\")\n",
    "for i, mapping in enumerate(mappings[:10]):\n",
    "    print(f\"输出 {mapping['output_idx']:2d} <- 原始 {mapping['original_range']:8s} (中心: {mapping['original_center']:4.1f})\")\n",
    "\n",
    "print(f\"\\n后5个输出时间步:\")\n",
    "for mapping in mappings[-5:]:\n",
    "    print(f\"输出 {mapping['output_idx']:2d} <- 原始 {mapping['original_range']:8s} (中心: {mapping['original_center']:4.1f})\")\n",
    "\n",
    "def convert_output_timestamp_to_original(output_start, output_end, patch_size=PATCH_SIZE, patch_stride=PATCH_STRIDE):\n",
    "    \"\"\"\n",
    "    将输出时间戳转换为原始数据时间戳\n",
    "    \n",
    "    Args:\n",
    "        output_start: 输出序列中的开始时间步\n",
    "        output_end: 输出序列中的结束时间步\n",
    "        patch_size: 滑动窗口大小\n",
    "        patch_stride: 滑动窗口步长\n",
    "    \n",
    "    Returns:\n",
    "        dict: 包含原始时间戳信息的字典\n",
    "    \"\"\"\n",
    "    \n",
    "    # 计算输出时间步对应的原始时间步中心位置\n",
    "    original_start_center = output_start * patch_stride + (patch_size - 1) / 2\n",
    "    original_end_center = output_end * patch_stride + (patch_size - 1) / 2\n",
    "    \n",
    "    # 由于每个输出时间步对应一个patch，我们需要考虑如何处理重叠\n",
    "    # 最简单的方法：使用中心位置\n",
    "    original_start_simple = int(round(original_start_center))\n",
    "    original_end_simple = int(round(original_end_center))\n",
    "    \n",
    "    # 更精确的方法：考虑实际的音素边界\n",
    "    # 输出时间步的范围对应的原始patch范围\n",
    "    patch_start_first = output_start * patch_stride\n",
    "    patch_end_first = patch_start_first + patch_size - 1\n",
    "    \n",
    "    patch_start_last = output_end * patch_stride  \n",
    "    patch_end_last = patch_start_last + patch_size - 1\n",
    "    \n",
    "    # 保守估计：音素可能存在于重叠区域的任何位置\n",
    "    conservative_start = patch_start_first\n",
    "    conservative_end = patch_end_last\n",
    "    \n",
    "    # 最可能的范围：基于中心位置但考虑patch边界\n",
    "    likely_start = max(patch_start_first, int(original_start_center - patch_stride/2))\n",
    "    likely_end = min(patch_end_last, int(original_end_center + patch_stride/2))\n",
    "    \n",
    "    return {\n",
    "        'output_range': (output_start, output_end),\n",
    "        'output_duration': output_end - output_start + 1,\n",
    "        'simple_mapping': (original_start_simple, original_end_simple),\n",
    "        'conservative_mapping': (conservative_start, conservative_end),\n",
    "        'likely_mapping': (likely_start, likely_end),\n",
    "        'center_positions': (original_start_center, original_end_center),\n",
    "        'patch_ranges': {\n",
    "            'first_patch': (patch_start_first, patch_end_first),\n",
    "            'last_patch': (patch_start_last, patch_end_last)\n",
    "        }\n",
    "    }\n",
    "\n",
    "print(f\"\\n=== 时间戳转换示例 ===\")\n",
    "\n",
    "# 使用您提供的例子\n",
    "example_output_start = 206\n",
    "example_output_end = 207\n",
    "\n",
    "conversion = convert_output_timestamp_to_original(example_output_start, example_output_end)\n",
    "\n",
    "print(f\"示例: 输出时间戳 {example_output_start}-{example_output_end}\")\n",
    "print(f\"输出持续时间: {conversion['output_duration']} 个输出时间步\")\n",
    "print(f\"实际时长: {conversion['output_duration'] * PATCH_STRIDE * ORIGINAL_BIN_MS} ms\")\n",
    "print()\n",
    "print(f\"转换结果:\")\n",
    "print(f\"1. 简单映射(中心位置): {conversion['simple_mapping']}\")\n",
    "print(f\"2. 保守映射(完整范围): {conversion['conservative_mapping']}\")  \n",
    "print(f\"3. 可能映射(调整范围): {conversion['likely_mapping']}\")\n",
    "print()\n",
    "print(f\"详细信息:\")\n",
    "print(f\"- 中心位置: {conversion['center_positions']}\")\n",
    "print(f\"- 第一个patch范围: {conversion['patch_ranges']['first_patch']}\")\n",
    "print(f\"- 最后一个patch范围: {conversion['patch_ranges']['last_patch']}\")\n",
    "\n",
    "# 如果有phoneme_data，分析实际数据\n",
    "if 'phoneme_data' in locals() and phoneme_data is not None:\n",
    "    print(f\"\\n=== 实际数据转换分析 ===\")\n",
    "    \n",
    "    # 随机选择一些segments进行分析\n",
    "    sample_conversions = []\n",
    "    \n",
    "    count = 0\n",
    "    for phoneme, segments in phoneme_data.items():\n",
    "        for segment in segments[:3]:  # 每个音素取前3个\n",
    "            if count >= 10:  # 只分析前10个\n",
    "                break\n",
    "                \n",
    "            output_start = segment['start_time']\n",
    "            output_end = segment['end_time']\n",
    "            \n",
    "            conversion = convert_output_timestamp_to_original(output_start, output_end)\n",
    "            conversion['phoneme'] = phoneme\n",
    "            conversion['session'] = segment.get('session', 'unknown')\n",
    "            conversion['trial_num'] = segment.get('trial_num', -1)\n",
    "            \n",
    "            sample_conversions.append(conversion)\n",
    "            count += 1\n",
    "        \n",
    "        if count >= 10:\n",
    "            break\n",
    "    \n",
    "    print(f\"前10个segment的时间戳转换:\")\n",
    "    print(f\"{'音素':4s} {'输出':8s} {'简单映射':12s} {'保守映射':12s} {'可能映射':12s} {'实际时长(ms)':10s}\")\n",
    "    print(\"-\" * 70)\n",
    "    \n",
    "    for conv in sample_conversions:\n",
    "        output_range = f\"{conv['output_range'][0]}-{conv['output_range'][1]}\"\n",
    "        simple = f\"{conv['simple_mapping'][0]}-{conv['simple_mapping'][1]}\"\n",
    "        conservative = f\"{conv['conservative_mapping'][0]}-{conv['conservative_mapping'][1]}\"\n",
    "        likely = f\"{conv['likely_mapping'][0]}-{conv['likely_mapping'][1]}\"\n",
    "        duration_ms = conv['output_duration'] * PATCH_STRIDE * ORIGINAL_BIN_MS\n",
    "        \n",
    "        print(f\"{conv['phoneme']:4s} {output_range:8s} {simple:12s} {conservative:12s} {likely:12s} {duration_ms:6d}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a530adfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文字体支持\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'Arial Unicode MS', 'sans-serif']\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "print(\"已启用matplotlib中文支持\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "mks6harmjq",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 滑动窗口参数分析 ===\n",
      "输入patch大小: 14 时间步\n",
      "滑动窗口步长: 4 时间步\n",
      "相邻窗口重叠: 10 时间步\n",
      "原始数据分辨率: 20 ms/bin\n",
      "输出数据分辨率: 80 ms/output_step\n",
      "\n",
      "=== 时间步长度统计 ===\n",
      "总segment数量: 214218\n",
      "输出时间步长度:\n",
      "  最小: 1 步\n",
      "  最大: 12 步\n",
      "  平均: 2.1 步\n",
      "  中位数: 2.0 步\n",
      "\n",
      "对应的实际时长(毫秒):\n",
      "  最小: 80 ms\n",
      "  最大: 960 ms\n",
      "  平均: 170.4 ms\n",
      "  中位数: 160.0 ms\n",
      "\n",
      "输出时间步长度分布 (前10个):\n",
      " 2 步 (160ms): 78068 个segment (36.4%)\n",
      " 1 步 ( 80ms): 74821 个segment (34.9%)\n",
      " 3 步 (240ms): 30313 个segment (14.2%)\n",
      " 4 步 (320ms): 21718 个segment (10.1%)\n",
      " 5 步 (400ms): 8254 个segment (3.9%)\n",
      " 6 步 (480ms):  977 个segment (0.5%)\n",
      " 7 步 (560ms):   59 个segment (0.0%)\n",
      " 8 步 (640ms):    7 个segment (0.0%)\n",
      "12 步 (960ms):    1 个segment (0.0%)\n",
      "\n",
      "=== 示例分析 ===\n",
      "示例segment: start_time=206, end_time=207\n",
      "输出时间步数: 2\n",
      "实际时长: 160 ms\n",
      "对应原始数据范围: bin 824 到 bin 841\n",
      "原始数据时间步数: 18\n"
     ]
    },
    {
     "data": {
      "image/png": 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DpaamOtqSk5NlMpkkSU2bNtWzzz7raLvrrrsc22natKlGjx7taDs/qWvatKnWr1+vlJQUl3HZbDbZbDbHtMVikSTZ7XbZ7fY/vd/nMwxDJpNJJkkmGS6XMUkymUwyDMPt31/SmAr+a5w37dmYSqKs9KXk3J++2Jdljd1up//ciP50L/rTvUq7P31pnMiT/vc7asgku2EqfjmZPPZb6qmYSuPfekli92Rf+mpMJcXxvWxgnHwfY+T7SnuMvFaUSkhIKDLPYrE4zTeZTPL399eZM2dksVhUv359R1tERISOHDnicr2Strny6quvatSoUUXmm81mZWdnX8YeXprValXdhJqKDZNCA20ulwkPkwISaspqterEiRNu/f6SxmSSoWv8c2VSQWLg6ZhKoqz0peTcn2E+2Jdljd1uV3p6ugzDkJ8f7274s+hP96I/3au0+9Nqtbp9m1eKPKlgPOJrJcrqH6YTeSHFL+efrfhamR75LfVUTKXxb70ksXuyL301ppLi+F42ME6+jzHyfYVjVFrj47WilCsBAQEKDg52mhcSEqKsrKwibYXzXa1X0jZXnnvuOQ0dOtQxbbFYFB8fr5iYGEVERPy5HbxARkaG9u4/qLx6UkRYsMtlLJnSgf0HVbFiRcXGxrr1+0sak0mGDEnm3GAZMnk8ppIoK30pOfdnug/2ZVljt9tlMpkUExPDD5kb0J/uRX+6V2n3Z0hI8UUGX1Ae86RDB35TxfxIxQaEFb9cfqYOHTjrkd9ST8VUGv/WSxK7J/vSV2MqKY7vZQPj5PsYI99XOEYX5iDu4lNFqaioKO3cudNpntVqVVBQkKKiomQ2m4vML1zvStpcCQ4OdtnZfn5+bv8jKbwUufBmLlcM/e/SZk/8kRYfk8lxw5mnYyqJstWXkny4L8uiwv6jD92D/nQv+tO9SrM/fX2MymueZJIhP5PrW/Ol/57s8dBvqSdjcve/9ZLE7sm+9NWYLgfH97KBcfJ9jJHvK81jsE+NelJSkjZt2uSY3r9/v2w2m6Kiooq0bd26VdWrV3e5XknbAAAAygryJAAAcLXxqaJUq1atZLFYNGvWLEnS2LFj1a5dO/n7+6tz587auHGjVq1apdzcXI0bN07t27eXJHXr1k2LFi3Sjh07lJGRoUmTJjnaunfvrilTpigtLU3Hjx/XzJkzHW0AAABlBXkSAAC42vjU7XsBAQGaMWOGevTooeHDh8vPz0/ffPONJCk6OloTJkxQhw4dFB4ersjISM2ePVuSdOONN2rIkCFq0qSJQkJClJiYqIEDB0qSOnXqpMWLFysxMVGS1LZtW3Xt2tUbuwcAAHDFyJMAAMDVxutFKcNwvn+8c+fO2rdvn7Zs2aLmzZurcuXKjrYBAwaoffv22r17t1q2bKnw8HBH25gxY9SrVy+lpaWpdevWjuchmEwmzZ07V4MHD1ZmZqZat27teF0yANfMZrPjNd8XExERoZiYGA9EBADlE3kS4JtOnjypo0ePKiMjo9i/GfIkALg0rxelXKlSpYo6duzosi0hIcHla5IlqX79+k6vQz5fUlKS2+IDrmZms1m9+6XotLX4ty8ViqoYqnmzZpBwAYAHkScB3mU2m/XEY70VGx2lQwd+K1I8LhQcXllTZywgTwKAi/DJohQA77FYLDptzVLMLd0UFhVX7HKZp4/LvOljWSwWki0AAFBuWCwW2TJO6Z674pSQHPnf9yk7O2Q+p7c+P0WeBACXQFEKgEthUXGKiK1x0WXMF20FAAC4ekVfE6TacWHyM7m+UkqyeTQeACiLfOrtewAAAAAAACgfKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMCvB2Ap+zcuVP9+vXT3r17lZKSonHjxslkMnk7LAAlZDabZbFYisw3DENWq1UZGRkymUyKiIhQTEyMFyIEgLKLPAko+4rLlc5HngTA15SLopTNZlOnTp3Uvn17LVq0SIMHD9bs2bPVr18/b4cGoATMZrN690vRaWtWkTaTyaS6CTW1d/9BGYahqIqhmjdrBgkXAJQQeRJQ9pnNZj2e0lO2jFMXXS44vLKmzlhAngTAZ5SLotTy5cuVnp6u8ePHKzQ0VGPHjtUTTzxBsgWUERaLRaetWYq5pZvCouKc2kySYsOkvHpSxunjMm/6WBaLhWQLAEqIPAko+ywWi2wZp/R0p2DFx1Rwucwh8zm99fkp8iQAPqVcFKW2bdum5s2bKzQ0VJLUsGFDpaamejkqAJcrLCpOEbE1nOaZZCg00KaIsGAZkszeCQ0AyizyJODqER9TQXWqh11kCZvHYgGAkigXRSmLxaKEhATHtMlkkr+/v86cOaNKlSo5LWuz2WSz/e9gnZ6eLkk6e/as7Ha72+Oy5+cr/egB5WUXvS1JkjLPnFBudrZ++eWXS94j7g6HDh1Srs3mFJNJUlCodDpLMrwQU0m4ivtCvtCXknN/ZpTRvpQ8258Xi8nX+7MsslqtOnr0qLfDuGrQn+5lGIaCgoLk5+f+d7UUHjcMw3D7tn2ZL+dJefl27TqUIUtWfrHLpZ06p3PZuR77PcrOyfVITO4+dpQkdk/2pa/GVBIFcefpwLFMyWpRQYbsjL70HfwO+z7GyL0qVaqkyMhIt23PbrfLYrEoOztbkvvzJJNRDjKvZ555Rrm5uRo/frxjXnx8vL777jtVr17dadmXXnpJo0aN8nSIAADARxw6dEg1atS49IJXCfIkAABQUu7Ok8rFlVJRUVHauXOn0zyr1aqgoKAiyz733HMaOnSoY9put+v06dOqXLlyuX0LjcViUXx8vA4dOqSIiAhvh1Pm0Z/uRX+6F/3pXvSne5V2fxa+zbNatWpu37YvI0/yPRw7fB9jVDYwTr6PMfJ9hWP0xx9/yGQyuT1PKhdFqaSkJL333nuO6f3798tmsykqKqrIssHBwQoODnaa585L38qyiIgIDhRuRH+6F/3pXvSne9Gf7lWa/XnNNdeUynZ9GXmS7+LY4fsYo7KBcfJ9jJHvu+aaa0pljNz/QAYf1KpVK1ksFs2aNUuSNHbsWLVr107+/v5ejgwAAMC7yJMAAIC3lIsrpQICAjRjxgz16NFDw4cPl5+fn7755htvhwUAAOB15EkAAMBbykVRSpI6d+6sffv2acuWLWrevLkqV67s7ZDKjODgYL344otFLtfHlaE/3Yv+dC/6073oT/eiP0sPeZJv4d+672OMygbGyfcxRr6vtMeoXLx9DwAAAAAAAL6lXDxTCgAAAAAAAL6FohQAAAAAAAA8jqIULmrZsmWqXbu2AgICdNNNN2nXrl3eDumqceedd2r27NneDuOq8Mwzz6hTp07eDqPMmzFjhuLj4xUaGqrbbrtNv//+u7dDAnTy5EklJCTowIEDjnn8NgEAAFwdKEqhWPv27VO/fv302muvKS0tTdddd51SUlK8HdZVYf78+VqxYoW3w7gqbN++XVOmTNHbb7/t7VDKtH379mn06NFatmyZdu/erTp16qhv377eDqtMclVE2blzp5KSklSpUiUNHz5cPM6xZE6ePKm7777bqS/5bcLVoLjC6sWOFevWrVO9evUUHR2t8ePHeyv0cun8E4kXG4clS5aoZs2aqlatmhYuXOiFSMuvC09Q8rfkO4o76ckYedfl5quleuwzgGJ8/vnnxrRp0xzTa9asMSpUqODFiK4Op06dMuLi4ozrr7/emDVrlrfDKdPy8/ONZs2aGSNGjPB2KGXe4sWLjfvuu88x/e233xpVq1b1YkRlk9lsNpo1a2ZIMvbv328YhmFkZ2cbtWrVMvr372/s3bvX6NChg/H+++97N9Ayom3btsbbb7/t1J/8NqGs27t3r1GpUiXjww8/NI4dO2bcd999RosWLS56rDhx4oQRERFhjBo1yvj111+Nm2++2VizZo2X96R8mDdvniHJmDVr1kXHYceOHUZQUJDx3nvvGdu3bzfq1q1r7N6928vRlw/btm0zwsPDjX379hmGcfHfXf6WPGvv3r1GfHy8sWXLFuPgwYPG3//+d6Nly5aMkZddbr5a2sc+ilIosalTpxoNGzb0dhhlXt++fY0BAwYYffr0oSj1J02ePNkIDQ01Zs6caSxbtsyw2WzeDqnM+uWXX4zKlSsbW7duNc6ePWv07NnTePjhh70dVpnjqoiydOlSo1KlSkZmZqZhGIbx888/G7feeqsXoyw7fv/9d8MwDKf+vBC/TShriiusXuxYMWHCBOMvf/mLYbfbDcMwjE8//dTo1auX54MvZy48kXixcRgyZIjRvn17x7oTJ040nn/+ea/EXZ64OkHJ35LvKO6kJ2PkXZebr5b2sY/b91AiOTk5euuttzRgwABvh1KmrV27VqtXr9a4ceO8HUqZl5GRoRdffFG1a9fWwYMHNWHCBP3tb3/TuXPnvB1amVS/fn11795djRo1UmRkpDZt2qQ333zT22GVOe+9954GDx7sNG/btm1q3ry5QkNDJUkNGzZUamqqN8IrcxISEi7azm8TyqK7775bjz32mGN6z549SkxMvOixYtu2bUpOTpbJZJIkNW3aVFu2bPF88OXM008/rXvvvVfNmzeXdPFx2LZtm9q0aeNYlzHyjHfffVc7duxQrVq19NlnnyknJ4e/JR9Sv359rVmzRj///LPS09M1ZcoU3X777YyRl11uvlraxz6KUiiRF198UWFhYTy340/Izs5W//79NXXqVFWsWNHb4ZR5n3zyiTIzM7V27VqNGjVKK1eulNVq1dy5c70dWpn0ww8/6PPPP9d3332ns2fPqkePHurQoQPPPrpMroooFovFab7JZJK/v7/OnDnjydCuSvw2oaw7v7B6sWPFhW0RERE6cuSIN0IuN1ydSLzYODBGnlfcCUr+lnxHcSc9GSPvutx8tbSPfRSlcElr1qzR5MmTtWDBAgUGBno7nDLr5ZdfVlJSkjp27OjtUK4Khw8fVvPmzRUdHS1JCggIUMOGDbV3714vR1Y2LVy4UA8++KCaNWuma665Rq+88or27dunbdu2eTu0Mi8gIEDBwcFO80JCQpSVleWliK4O/DbhanB+YfVix4oL2ziGlK7iTiRebBwYI88r7gTl+++/z9+SjyjupCfHO99zpWPijvGiKIWL2r9/v3r06KHJkyerfv363g6nTFuwYIGWLVumyMhIRUZGasGCBRo4cKAGDhzo7dDKpBo1ahS5Ve/gwYOqXr26lyIq2+x2u06cOOGYtlqtysrKUn5+vhejujpERUXJbDY7zbNarQoKCvJSRGUfv024GlxYWL3YseLCNo4hpau4E4kXGwfGyPOKO0F59uxZ/pZ8RHEnPTne+Z4rHRN3jBdFKRTr3Llzuvvuu9WlSxfde++9ysjIUEZGBrfzXKENGzZo586d+vnnn/Xzzz+rc+fOGj16tEaPHu3t0Mqkjh07KjU1Ve+++64OHz6sSZMmadu2beratau3QyuTWrZsqU8++UQTJkzQggULdM8996hKlSpq2LCht0Mr85KSkrRp0ybH9P79+2Wz2RQVFeXFqMoufptwNXBVWL3YseLCtq1bt3ISphQVdyJxzpw5xY4DY+R5xZ2gnDhxIn9LPqK4k54BAQGMkY+50t8gt4zXn3psO65qn376qSGpyKe4NyDh8vD2vT/v22+/NZo3b25UqFDBqF27tvHZZ595O6Qyy263G6NHjzauvfZaIzAw0GjUqJHx008/eTusMuv8Y2Vubq4RExPjeK1uSkqKcffdd3sxurLn/P7ktwllXVZWllG/fn3j0UcfNaxWq+OTk5NT7LHCbDYbISEhxsqVK42cnBzjzjvvNAYNGuTN3biqHTp0yNi/f7/j061bN+ONN9646Dj8/PPPRlhYmLF9+3bDarUaN910k/Hmm296eU+ubidPnjQiIiKMqVOnGocOHTLefvttIyQkxPjjjz/4W/IRixcvNkJDQ43x48cb8+fPN5KTk42aNWtyvPMRJc1XS/vYR1EKAAA3u7BIsmzZMiM0NNSoXLmyERMTY/zyyy/eCw6AV12ssHqxY8XUqVONwMBAo1KlSkZCQoJx7NgxL+5F+XL+icSLjcO//vUvIygoyIiIiDAaN25sZGVleSni8qO4E5T8LfmGi530ZIy873Ly1dI89pn+GwwAAChFx44d05YtW9S8eXNVrlzZ2+EA8FEXO1bs379fu3fvVsuWLRUeHu6lCHGxcUhNTVVaWppat27Nc3C8jL8l38cY+Z4rHZM/c+yjKAUAAAAAAACP40HnAAAAAAAA8DiKUgAAAAAAAPA4ilIAAAAAAADwOIpSAAAAAAAA8DiKUgC8Jjs72+X8DRs2KD8/38PRuNazZ099++23kqScnBzl5uaK90MAAAAAwJ9HUQqA19x1112aMWOG07z58+erVatWeu+990q0jZtuuklffvnln46ldevW+r//+78i8+vXr6/evXvr7NmzGjhwoK655hpFRkY6fUwmk3bu3Flk3R9++EFms1mS9NRTT+mZZ54p9vuPHTumCRMmSJJq166tn3/+WXPnztV//vMf7du3T40bNy62iAcAAK4umZmZ6tOnjw4ePOjtUDzKZrPpzTffvOL1MzIy9M4777gxIgCljaIUAI/Lzc3V2bNn9fvvv6tx48Y6ffq0JGn37t164okntHDhQk2YMEEbNmxwWu/cuXN66aWXlJOT45gXHBys0NBQx/TatWu1bt06x/SGDRvk5+fnVEDasWNHkZiCgoIUFBRUZP7w4cPVtGlTnTlzRjNmzFBWVpbS09N19uxZxycsLEwhISFF1h0yZIi+//57SVJAQIDTMrm5uU77cc0112jBggX67LPPFBAQIH9/f40ZM0ZVq1bVTz/9pKCgIJffAQAArj5hYWHavn17kZN35zMMQyaTSVFRUapcubJMJpM2b96sLl26qFatWk6fatWq6T//+c9lxVDcCTtJSklJ0bPPPus0b8OGDerYsaMyMjIu63vO9/jjjysrK+uK169QoYK++OILzZs374q3AcCzArwdAIDyZ8uWLerRo4eOHTumBx98UPn5+fr88891++2367HHHtODDz6oxMRE3XnnnXrppZf0+OOPy8/PT3a7XaNGjdKIESMc2zKZTDKZTI7plStXymQyqXXr1pKkwMBAVatWTYcPH5YkhYeH6/Dhw/rkk08cxR9JOnTokFasWKGjR4/qqaeektls1g033KDTp0/ro48+uuQ+BQQUPZz6+/s7Ckl+fn56++23NWfOHEkFRaknn3zSkdClpqaqZ8+eyszMVHZ2tlavXq1mzZrp4MGDWr16tdq1a+fYrs1mU2BgoPz8OK8AAMDVYPTo0Xr//fed5p06dUpvvvmm5s6d6zT/+eef16OPPiqTyaTAwED99NNPOnr0qLp06aLGjRtr0aJFCgoKcuQ4UsEjEwIDAyUVFI9at26tiIgISVJ6erq2b9+uBg0aOH1PcSfsJCkkJMTpZNnu3bt177336pFHHlF4ePgV9cGaNWt08ODBIv1wOfz9/bVw4ULdcsst6tixoypVqnTF2wLgGRSlAHhc8+bNNWHCBE2fPl2jRo3SH3/8oebNm6tnz5766KOPHEWgO++8Uz/++KOWLFmiBQsWOBKL85OsC11YqHFVLLrmmmtUv359R2HHZDIpIiJCNWrU0A033KDAwECFh4fLZrOpQoUKSk1NVYcOHYpsZ9asWUpOTnaal5eXJz8/P5cFo3/84x966aWXnObl5uYqMDBQqamp2rFjhyZMmKBDhw7pu+++U8WKFbVx40YtX75c+fn5mjt3ro4fP66KFStq/fr1+stf/lJsPwAAgLIjIyND7dq1K3JlVFpamkJDQx050N/+9jedO3fO0V5YNFq5cqXuuOMOmUwmVahQocj2zy8gXekJu8Ii1oV+/fVXtWvXToMGDSqS51yOl156SZMmTbri9QtFRUVpwIABeueddzRy5Mg/vT0ApYuiFACv+OKLL3TTTTdp2LBhmjdvnlq1aqVdu3apVq1aqlq1qhYuXChJ+vDDD2Wz2VS1atUreqaSn5+fjhw5osjISEkFz2ioWrWqbr75Zh04cMBR2Jk2bZoaNWqk7t27O5aT5HQV1oEDBxz/u3nz5i4feP7mm29q5MiRCg8Pl2EYeuCBB2QymWS32yXJ6TL4nJwcJScna9myZXrooYd09uxZbd++XYGBgYqMjNRf/vIXNWnSRCNHjtS5c+cUFBSkZs2aafLkyRSkAAC4ijRs2FA2m027du3SY489punTp6tevXr68MMP9cEHH+jnn3+WJPXu3Vv169cvsv6KFSs0YMAAxxVRF57As9vtys7OVmho6BWfsHPlu+++0z333KMhQ4boueeeu+L9P378uM6ePaubbrrpirdxvp49e+qOO+6gKAWUAdz7AcDjjh49quXLlys+Pl7Hjh3Tli1b1LJlS9WtW1eDBw/W1q1btWLFCrVo0UIHDx50FIPy8vJUuXJlRUdHOz5btmxRly5dHNPvvvuu06Xmfn5+qlatmtPzn0wmk+bPn6/OnTuX6C1/xd0m52r+8OHDlZOTo9OnT+vMmTPav3+/Tp48qTfffFMTJ07UyZMnHR+LxaJly5ZJkh555BGtXLlSq1at0jXXXKO+fftq3bp1jjOO+/fvlyQdOXJE1apVu5zuBgAAPq53797q27evEhMT1alTJzVr1kwzZ86UJNWqVcux3IABA9SqVSundS0Wi3744QfdfvvteuihhxQTE6Po6GhVrFhRQUFBio6OVkxMjGJjY5Wenu50wi4yMtJxwq5Tp076y1/+os6dO6tTp06KjY11nLCrUKGCMjMzHY8ZyM/PV15enipVqqRnn31WTz31lLKzsx2fjIwMxwm+kti5c6eSkpIc0998841q1KihBx54QJGRkZo2bZqqVKmiVq1ayW63KycnR3379lWlSpUUGxursWPHOm0vLi5OWVlZysvLu4LRAOBJFKUAeNzGjRtVvXp1ffXVVzIMQxEREQoKClJaWprjTGBoaKgqVKigoKAgR/Fn6tSpev/9950KO40bN9ayZcsc0wcPHlRqaqpyc3MlyXGF0oX69OmjnJycEj8I8/Dhw04PDN26davL5c4/Mzlu3Djddtttys/PV2pqqn799VdH2969e9WrVy9ZrVZJ0muvvaZPPvlEkydPVps2bdS8eXMtWLBAOTk5+vvf/66NGzfq3LlzysjIoCgFAMBVqF27dnr77bc1fPhwLV++XDVr1tS5c+dUvXr1i64XHh6uJk2aaNWqVVq8eLFOnz6tkydP6p133tEdd9yhkydP6tSpU8rIyNA111xzxSfsXnjhBUVFRSk6OlozZ87UuHHj1LhxY/3zn/9URESE00nDypUrF3kQ+sWcOHFCcXFxTvPS0tLUs2dPNW7cWBMmTNCXX36pDRs26OjRo3r//fe1ceNG/fDDD1q2bJnGjBmj3bt3O60fHR2tkydPljgGAN5BUQqAx3Xv3l0//PCDXn75ZV177bVq06aNJKlSpUqKj4+XVPQB5lLBWbQjR45cdNsmk0kffvihozjkKrEyDEMBAQH6xz/+4fQGvIupUaOGDhw44Pg0atToosvPmjVLb7/9tubNmyd/f38FBAQ4Lpc/deqUOnXqJJPJpLCwMEkFb7FJSEjQCy+8oMWLF6tWrVoKCwvTm2++qW7dumnZsmX67rvvnM4iAgCAq8fzzz+viRMn6p577tGtt96qdu3a6dixY7r22msvup6fn5+ef/55jR49WpK0dOlS/fjjj4721NRULV682DF9pSfsJkyYIJvNpoyMDD322GP617/+pYyMDH3wwQdq1KiRMjIyHB+bzaZ33nmnxPseFhZW5K19cXFx6tKli+Lj43XPPfeocePGkgqex1mhQgUZhqG8vDzdcsstOnPmjK677jqn9TMyMq74oesAPIeiFACvOHPmjH788Uft3r1bKSkpkgre/nL06NFi17mwSOVK4VVVhf/Nz8/X0aNHHWfuMjMzHZdy/+Mf/9Cjjz56yW26enaUVHxS98Ybb+i5557TypUrizz34cCBA2rZsqWaNm2qOXPmOOJctmyZZs+erdtuu00HDx7U0qVLdf3116tp06Zq166dtm/frlGjRrl84DoAACi7DMPQuXPn1KZNG/34449Ov/VpaWmOE3ZSwaMMsrKyimyjYcOG+u2333Tq1Ck9+eSTTlcN7dq1S48++qjOnDkjyX0n7Ardc889OnLkiD755JPLWu98NWvW1L59+5zmnf9w9vP/tyT16NFDXbt2VefOnVWtWjW9+OKLTu15eXmyWq0UpYAygKIUAI9bs2aNqlatqnfffVcREREaPHiwpII35QUHB0sqOAtWXNHncuTl5alq1aqO2/vCwsIct/aVVGFR6vzb944dO1ZskSwqKkrr1q0rUpD697//raSkJD300EOaM2dOkYeQbt++XadOndJdd92lfv366e9//7ukgn7p06ePvv32Wz388MOXFTsAAPBtf/zxhypVqqSoqCj99a9/1fPPP6/o6GhFRUVp6dKlGjx4sOPkWlRUlOrVq+e0/qJFi9SmTRu1a9dOK1euVMWKFdWzZ09He7du3XT99dfr9ddfl+SeE3bns9vtevPNN9W/f3+nRxVcjoYNG2r37t1FrpYqzt69e/XEE0/ot99+04YNGzR79mx9+umnjvZ169bpb3/72xXFAsCzKEoB8Ljk5GSZzWbHQysbNmyo/Px83XLLLXrooYckFTwHoE2bNjIMw1H8ycnJ0dChQx0P5oyMjNTmzZt19913O6arVKni9F3+/v6KjY11TG/fvl1169YtEpPFYnEqMhWehczLy1P9+vWdbt0r/CQnJ8tischmsyk0NNSxXps2bZSdna0vv/xSEydO1P33369p06apbt262rBhQ7Fvp3nqqae0YcMGZWdnq3r16po1a5YWLlyodevWafbs2br55pv1+OOP89BOAACuIjVr1lR2drbOnj3r9NzMfv36qUuXLlq9erX++OMPx0tSDh486Fg3Pz9fTZo00QsvvKBPP/1UI0eO1HPPPVfkxNfzzz+vd955RydOnHDLCbtCs2bNUp06ddS0aVM98MADat26tdasWXPZ2zGZTHrooYf01ltvlWj5efPmqW/fvkpNTZXNZpMkp/xozJgxGjBgwGXHAcDzir4PFABKmclkUsWKFZWbm6vs7GxJ0rlz5xQQEKD+/fvrtttuU4MGDfTbb79pxIgRmjJliiRpwYIFl9x2dna2KlSooJycHAUFBemWW27RTz/95GivXbu20/I///yz2rVrp+zsbMf3SHKcqcvOzi720u8vv/xS9913n9q3b+8ofGVlZemWW25ReHi46tSpoxtuuEHdunXTu+++q6ioqGLj/vHHH7VgwQJ9+eWXGjx4sJ544gkdPnxYzz//vL799lt9/PHHuu6669SmTRs1bdpUX3zxBQ88BwDgKvT777/r2Wef1a+//qrVq1erd+/e2rZtm1566SU98sgjTgWn3Nxc1a1bV+3atdMvv/yi6tWrq2fPnvrPf/6jH3/80bFsp06dNGzYMNntdpcn7GrUqFEkjgtP2J3vzJkzmjt3roKCgjR+/HjVrFlT77zzjsLCwnT77bfrnnvu0WuvvabExMQS7/c///lPJSUlqWPHjpdc9plnntFjjz2mW2+9VYZh6IEHHlDXrl0lSVOmTFGVKlW4UgooI0xGcQ9LAQAvs9vtjmculabNmzerfv36jqudSspmsykzM/OixaaS2rp1q7777jv17t1bFStWlFSQ8M2cOVOPP/6444HoNptNn376qR544IE//Z0AAMA3bNy4Ud99950+++wzbdmyRY899pheeeUVR26yfPly/eMf/5C/v78mTZqkdu3ayTAM+fn5af/+/apVq5bT9vr166cffvhBI0aM0IMPPnhZsZx/wu67777TX//61yLLNG7cWJUqVdKCBQucClyS9O2332r+/PmaNGmSAgMDL+u7d+zYoX/+859avnz5Za1XKD09XR07dtS///1vnicFlBEUpQAAAADAi5YvX65Jkybp3nvvVbdu3VS5cuUiy9hsNo0ZM0a33nqr2rdvX6rxXOqE3ZEjRxQbG+t4s7A7/dmTkp46qQnAPShKAQAAAAAAwOMoIQMAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjKEoBwJ9w/PhxnT59WpJkNptlNpud2m0222Vv88CBA/rtt9/cEh8AAAAA+CqKUgBK1dKlS1WrVq1iizMbN250WYCx2+2yWCyX3P6OHTv0+eef/+k4C+3du1fJycnauXNniZZ/5JFH9Morr0iSnnzySQ0dOtSpvVGjRhoxYsRlxfDss8/q73//e4mWzcrK0tmzZy/rk5GRcVnxAACAK1PaeZA7GYbxp9Y/fPiw438fO3ZMkydPvqLttGvXTq+//nqJlz916pSOHDmi06dPXzIHctWn2dnZkqSMjAy9+eabOnnypHJycpSbmytJ+vXXX7Vjx44r2hcAl0ZRCsBlO3bsmA4cOOD0Kbxa6EI5OTk6ePCgAgMDXbanpKRo9uzZReaPHj1azZo1U1pa2kVjWblypV599VXH9KxZszRo0CANGzbM6TN48GCn4tCmTZs0ffr0Itv76quv9PPPP+vaa6+96Pfm5uYqLy9PISEhCgkJkSQFBQUpKCjIkXieOXNGu3btUrt27S66rQsFBwcrKCioyPy8vDxlZmY6zRs0aJAqVap0WZ/k5OTLigcAAPyPL+RBq1evlslkUnR0tNPHZDIpMjLSaZ6/v7+mTJlSZBv5+fnavXu35syZo549e6pZs2ay2WzFFnQKizeu7NixQ7Vr19bXX38tqeDq8SeffFIrV64sdh1Xdu/erdWrV6ty5colXufFF19U9erVVbly5UvmQLVr13Za1263q2XLlpo3b54yMzM1fPhwnTx5Uh988IFuv/12SdL8+fN1xx13KD8//7L2BUDJUJQCcNl69uyphIQEp8/o0aNdLhsQECCTySQ/P9eHm+DgYFWoUMFp3kcffaTRo0erVatWqlq1qsv1qlatqsWLFyswMFAmk0mnT5/W0aNHValSJSUkJCg+Pl6ffvqp0tLSdO2116pWrVqqUaOGY/3Vq1friSee0IYNG5y2O3/+fPXv318REREX7YM5c+YoMDBQH3/8sV599VWZTCbNnTtX77//vkJCQpSRkaE1a9aoSpUqatWqlWM9m812yaTGZDJpzZo1MplMTp/AwECFh4crLy/PsWxAQIC6desmwzAcn2XLlmnJkiWy2+1O8w3D0Msvv+yy4AUAAErGF/KgwhNix44d08mTJx0fSfriiy+c5iUkJDiWl6T09HQlJiYqNDRUf/3rXzVw4EDVqFFDzz33nI4cOVJsQWfkyJHF9snYsWNVo0YNx4mvBg0aqHv37nrqqaeKvUps9+7d+u2337R//35HcW/8+PGKjIxUq1atihT+fv/9d+3Zs0d79+512s5rr70mi8WirKwsnTt3zulz9uxZvfbaa4qIiFCjRo30f//3f07r+vn56Y033tCQIUMcJ/4CAgI0ceJEDRgwQJK0efNm3XvvvfL39y92/wFcuQBvBwD8P3v3HhdF1f8B/DO7C7sidwENJUREgxSvEFmKqD2UF8hLJVqpyS8vmeaF0nq0NAW1wrS8pJAomZaWaZmPZZqaUj2hEoiX9CFKTMFEdrktl5nfH8bkBijq7C6Xz/v1sphzZobvfBfl7HfPnKGGR6PRYPz48UhISAAAhISEQKPRIC0tDUajETY2NlCr1VCpVDh//jwEQcDx48dRXFyM++6774a/1L/++ms8/fTTmDhxIlauXAlBEGrcT61Ww9bWFoIg4IcffkDbtm1RWlqK33//HY8++igA4O2338b//d//oV+/ftWOf+WVV5CSkoKRI0ciLS0Nbm5uSEtLw48//ogff/yxxmnj4eHh+M9//gMAGDNmDMaMGYOnnnoK7du3x8KFCzF27FhoNBqsWLECdnZ22LFjBy5evFhtIHro0CE8+OCDN8xxnz598Mknn5i0lZWVoaSkBBrN3/90//OT19LSUkybNg1t2rRBv3795PzZ2NigefPmcu6IiIjo9tSHcVDV2KJVq1bV+gYPHmwyVsjPzzcZizg5OeGzzz6Dm5sb/vvf/2LKlClYunQpgGsFKwDIyspC27Zt5WMGDBhgUti6XmpqKrZu3YqkpCSTccnSpUvRqVMnzJ49G8uWLasxznPnztV4zo4dO9bYDgChoaH49ttv5W17e/tq++Tm5uL999/HO++8A1dXV2zYsEEeH14vMzMTaWlpmDp1Knbu3AkA+Oijj9C9e3fk5OTg3LlzOHjwINasWYOrV68CuHaboyiKtzSbi4hqx6IUEd2ymqagazQaTJkyBceOHZMHYiqVCuXl5RBFEX379kVpaSkuXboEJyenGs/7xRdf4LHHHsP48ePx7rvv1joQAyB/DwB48MEHTQYnmzdvRmlpKbKzs3H8+HH89ttvAICuXbuia9euAK7NRtqwYQPuvfdeTJw4Edu2bUNcXBz69euHxMREk+9VVlaGwMBAk8FMZWVlrVPx1Wo1DAYDPvvsMzz77LPo3bs3vvzyS5w4cQLTpk1D+/bta72uKjY2NnBzc6vTftd77bXX5E8UXV1d5falS5ciJiYGAGr9tJaIiIhurj6Mg0RRBHBtptT1BShBEPDFF1+YfPjVvn17eX/g2nIA7dq1M5k5LUkSKioqbriuVE3jh7KyMowbNw49e/bE6NGjTfratm2L+Ph4TJgwAV5eXtXW3fz5559NCnhjx47FDz/8gLS0NBw9ehRHjhypdkxtKioqsGnTJowZMwbAtRntb7zxBt555x2MHDlSLij904ULF/Dll19Co9Fg9+7dAK6t86XT6fDtt99CEAQUFhbiySefNDmuf//+2Lt3b51iI6IbY1GKiG5ZTYMSlUpV7VY4APjss88wYsSIWgcDVY4fP47IyEhMmjSp2tTqfzIajfItbderWpBy5syZ8PX1xeOPP44ff/wRAHDgwAE899xzclEKANzc3JCYmIi77rpL/pTv0KFDaNu2LTZs2IDg4GD4+/vj66+/htFoxODBg+Vj+/btix9++EHeXrRokfx1YmIi5s+fD4PBgN69e+PJJ5/EL7/8grKyshoXMP/jjz9MprYXFhaitLQUv/76q8l+dnZ28PDwqDUvn332mfyp5JEjR+Dg4IAXXngBKSkpmD59+g0ySkRERHVl7XEQ8Pfi3Pfcc0+1vpEjR5rMavrtt99M1oPatm0boqKiqsUPAOvWrbvp977enDlzcO7cOaSmptZYRHv22Wfx22+/YebMmTh79izi4+Pl2Ozs7OT99u/fj+TkZOzfvx+2trY4duwYVq5cWeei1OnTpzFt2jT897//xTvvvAMHBwf4+flh1KhR+OWXXxAaGoq33nqr2nUPGDAAYWFhePbZZ3HvvfciIyMD/fr1g7OzM6KjozF+/Hg8+OCD8ms7YcIESJJUp9eIiOqGRSkiumWCICAxMdFkRlH//v0BXCuoFBYW1jidHAAuX76MCxcuyJ/OGY1GXL58GTqdDkuWLMHgwYNx6tQpk2MkSUJpaSnuueceNGvWDF26dEF2djYiIyPlAZBarYYoinjnnXdgY2ODmTNnmsxsevjhh2ucdl5VaBo4cCCeeOIJ9OrVCwAwbdo0TJ8+Ha+++iq++OILdO/e3WRNqv3798PGxsbk00ng2gyqP//8E127doVOp5OfRPPnn3/W+snoo48+KhfPrufj42OyPWjQIHzxxRc1niM/Px8TJkxAfHw8fvjhB4SFheFf//oXdu7ciUOHDlWLk4iIiG6PtcdBVd+valbT999/j/Hjx+Puu+/GV199hXbt2uHKlSt4+eWX8cMPP+D8+fMmH2qFh4cjPT0dOp0OkyZNwsmTJ3Hw4EGUl5fLt/rXxbvvvov4+Hi8/vrrKC8vx8mTJ2ssTFXNMlq8eDG+/vprbNmyBT169JD7f//9d0RFRWHcuHFo164dzp8/D4PBAMD0iX4A4OrqalLM2rp1K1asWIFDhw7hu+++Q9++feHv7w+tVgsAOHbsGMLDw/Hggw/ikUcewdChQ/Hggw9i5syZAK4Vs5599lkUFxdj+/bt8PPzQ/v27fHMM89Ao9Hgk08+MXn4zR9//IEuXbpwfU4iBfFdChHdMpVKhSeeeAKLFy8GAAwdOlQehPTv3x9+fn744IMPajx2165dmDBhAuzs7KBSqXD16lX89ttv+OCDD1BZWYmYmBi4uLiYfApZXl6OkpISHDt2DPfeey/S09OhUqmgVqvx+eef48cff8Trr78u3+P/5ptvAgCWL1+ONWvW4MSJE7VeS3FxMcrKyvDZZ5+hsLBQbq+oqEDr1q0BAPHx8cjPzzc5rlmzZvjoo48wadIkk8FRUVERZs+ejf/7v/9DSUmJ/L2zsrLQvXv3GmOwtbXFmDFjanz6TpUnn3wSxcXFtfa7uLggPT0dHh4e2L9/PwYNGoTU1FS89NJLtRbDiIiI6NZZexxUJSEhAcnJyTh48CD8/f3h6+uLhQsXoqioCF999RUCAwPx6quvmtzOD0BeuLywsBBHjhyBu7s7/vzzTwQFBdX6FMGqOKpkZmYiJiYG//73v/Hrr78iMDDwhjlbvHgxvvrqKyxevBgBAQFye25uLgYNGoRLly5VK/QBgJeXl8l2UlKSfIseABgMBmRnZwMAOnXqhC+//BL+/v746KOPAFxbc2vGjBl46aWXIAgC8vLyTMZ0R44cQYcOHeTC1hNPPIHg4GB89tln+PTTT6HT6XDq1CmUlpZCp9Phjz/+qHFtKiK6fVxYhIhumSiKsLe3R9u2bdG2bVv50ygAeP7557Fly5ZqT0apMmbMGJSWluLKlSu4fPkyOnXqhDlz5uDy5cu4ePEinJ2dER8fb/LUmIKCApSVlckDMRsbG0yYMAEvvPACfvnlF+zfvx8AMHfuXJw5c8bk+5WUlNxwDaXVq1cjODgYgiDIg7bLly+jqKhIfgyzWq2ucX2nqmPOnz8v/5EkCQ4ODpg/fz66dOkiT/c+fvx4tYFVlbp+2lbbGlYA8Msvv+D999/Hgw8+iMceewyvv/46vvzyS3z11Vfw9PTE0KFDsWbNmjp9HyIiIqqdtcdBVS5evAg3NzfMnDkTY8eORWZmJpKSklBUVIRdu3Zh3LhxN/xgbs2aNaioqEBRURH69euHjRs3oqysDMC12drXPwH4m2++kfsAICAgAD///DNef/11rFy5EmVlZfLDYC5evCg/9beyshIajQZeXl7o168fvvrqK3m2V1ZWFkJDQ1FWVoY+ffpg6tSpKC8vR3l5Od577z107NhR3i4vL0fLli1Ncg2YrjEKAIGBgfjll1/k7czMTDz88MNy0VCn05nMHn/kkUcwa9Ys5ObmYty4cfD09IQoimjXrh2cnZ3xxhtv4K677sKRI0cgiiJOnz5d7XUgojvDohQR3bLKyspqbVVTyEeOHAlXV1fExcXd8nm1Wi2efPJJLF682GTg809FRUXYunUrwsPDYWtrC41Gg6NHj2LJkiUms5YEQZBnO9WkpKQEb775JiIiIkwKPgcOHAAAfP755zeM19bWFufOnYNGo5H/FBQUyEWmBx54ANnZ2Vi3bh0uXLhgMlX9eqIoorCwsNqjj6//U1RUhIqKihvGs2vXLowePRpHjx7Fm2++iY0bN2Lp0qX47LPP4OzsXOPTaYiIiOjWWHscVOXf//43Vq5cCXd3d7zzzjuws7PDTz/9hM8//xz33nsvSktLsWjRIgQFBeHnn382OTY/Px9vvPEGRo4ciebNm+Pf//43XnjhBVRUVCArK6vGPy+//LLJOfz8/OS4bWxscOLECbi7u6Nly5byPrm5uaioqDBZAgG4dstejx49IIoi9u7dC3d3dwiCII+nqgpN14+xqravd31BKj09Hffff7/8VEQA2LlzJ/r06YN9+/bVmMO33noLnTt3RocOHXDx4kWsXbsW99xzD7y9vZGXl4ennnoK4eHh2L59O9LT01FRUVHrzHciuj0sShHRLftnEcVoNMpPddFoNFi+fDlefPHF2zr3yy+/jN9//x3z5s2rdZ/Vq1ejdevWePjhh02OGzFiBLy9veXBYl5eHjw9PeV9/jmIXLVqFQDg1VdfNWlPSEjA008/jaysLPlJLLXx9fVFRUWF/Of6W+Xatm2LLl264LnnnoOrq2utU9tLS0vxySefwMfHp9Y/n332mcli6P/k5+eHQ4cOYdKkSfD09MRnn30GSZIwZMgQ+Pr6Yv369dWeHENERES3ztrjIACYPHky2rVrh7vuuguzZ89GQUEBzpw5g3/961/QarVo3749VqxYgaCgIGi1WgQFBZk8LW7GjBlwc3PD8OHDAVxbSzM6Ohpubm7QarVYvnw5PD090bZtW6SlpUGlUsHFxeWGMX322WcICwszaauadf7P2eJeXl5ISEjAwYMHqxWsbkXVDKjly5cjODgYw4YNwzvvvCMXCZcvX45x48Zh4MCBNY7p3njjDZSVlWHw4MGYMWMGCgsL8ccff6B58+Z44oknAFxbQiE5ORmrV69G3759bzhznYhuHdeUIqJbVllZiY8++ki+Xx+49vSSKv98skldff/993B1dcWbb76JyZMnw8vLC88991y1/V544QU8/vjj8kDkyJEjcHFxwU8//QQAcHd3h52dHb777juTuK5/8kx2djZee+01rFixAg4ODnL71q1bsW/fPpw9exadOnXCuHHjcOTIEbRr1+62rmny5MmYMGECnnnmmRoHMdnZ2di9ezecnZ1rPF6SJPk6qx7VfKNHRJ89exY9evRAeHg4hg0bhjfeeKPW2waJiIjo1ll7HAQA9957LxwcHNC2bVtcuXIFYWFhcHR0RK9evfDpp59i8ODBuHjxorzgenJyMkJDQwEAH3/8MZKSkrB3716UlJQAuHZb29KlSwEAS5YswaZNm/DKK6/A1dUVc+fOhYuLC/bv31/rkgi7du3CoUOHsGfPHpP2nJycWmeuDxs2TP66qqh3IzXtU1V8euCBB/D555+jb9++OHv2LL799lv5NsG3334bhYWFtX64d/z4cWzfvh1jxozBzp07cfDgQQQGBqJPnz4AgD59+qB9+/Z47733sHPnzpvGSUS3hjOliOiWVVZWYvz48fJ6AVW/tG9XeXk5Fi5ciD59+uDFF1/EpEmT8Oyzz2LKlCmYMGEC9Hq9yf4ajQZ33303CgsL8c033wC4VkyqKr4cPXoUWq0WR44cwWOPPYYPP/wQn3/+uTwjqry8HFFRUejYsSPGjh0rn3fXrl0YM2YMVq5cCS8vL7zwwgvo0KEDevfuLRe8ridJEs6dO2ey5kJBQYHJPhkZGQCAw4cPy58WXm/q1KnyE/8+/PBDTJ8+Xe57/PHH8d577wG4VpDq1asXNmzYYHL8P2d/eXt7Y/v27XB1dcWkSZPg7+8vx1ClLgM/IiIiqpm1x0EA8Nxzz2H27NnYv38/Xn31VQiCgE6dOslFo6tXr6J9+/bo378/Dhw4gKeeekr+cOzRRx/Fxx9/LD8x8Hrp6elYuXIlli9fDjc3N6hUKmzcuBFHjhyRHyTzT9988w1GjRqFESNG4F//+pdJ3+nTp9GyZcubrp95o9ng69evx3PPPYe8vDy4u7vXuE/Pnj3lwuD999+Pbdu24emnnwbw99MSa1ugvEuXLkhNTYW/vz9mzpyJt956C3q9Htu2bQNwbbmHqic4X/8BJxEpg0UpIrplLVq0MHmSy4EDB/DGG2/UuO+VK1duOLPn8uXLiIuLw2uvvYaZM2di06ZNAK4tvjlz5kysXbsWfn5+WLx4sfxpmF6vR0JCAjp37owff/wR3377rcmAcO/evRg6dChee+01uLq6Yt68eXjwwQflp7McP34caWlpiI+PhyAIKCwsRExMDCIjI/Hyyy8jOjoawLWFxXfs2AEvLy888MADmDdvnskT+srKyuDr6ysv5Pnjjz+iWbNmUKvV0Ov1GDt2LHbs2IG9e/eiWbNm6Nq1K9566y0UFRUBuDZLateuXZg2bRqAa4WnVatW4dKlSwCuLTL66quvorCwEBqNBkFBQYiJiTF5Mk7VAqVnz57F2bNnkZ2djbvvvhuzZs3CoUOH8Prrr0On08n9V65cuenaVERERFQ7a4+DjEYjVqxYAX9/f2RmZmLfvn24//775fGIIAhwdnbGDz/8AFdXV/Tt2xcDBgzAuXPnAFxbE/Oxxx4D8PdMI+Ba8eXpp5/Go48+ilGjRsntXbt2xcyZM/Hqq6/i9OnTcvuff/6JefPm4ZFHHkFQUJDJB2cbNmzArFmzsHTpUtx33303zek/i1KiKMqxnT9/Hh988AHGjh0rf5BXpbKyEhUVFfKtlOfPn8e6devw448/IiwsrNoanTUVlQRBQEBAANRqNXJzc+VlHHbu3ImTJ0+iV69eUKvVeOmllzBq1Chs3LjxptdDRLdAIiIyg9TUVKlNmzaSVquVgoKCatzn1KlTkiAI0t133y2lpKTUuM/OnTultm3bSjt37pQkSZKMRqMUGRkp2djYSP/3f/8nXbp0Sd43PT1dCg8Pl9RqtRQbGyu3X716VRowYIDk4eEh/fbbb5IkSfL///zzT+muu+6SHB0dpY8++qjGGEpKSqT/+7//kwBIr776qty+ceNGydfXV97u3bu31LlzZyk9PV3q0qWL9Mwzz0h5eXmSJElSZWWl9M4770jBwcHSyZMnJUmSpLFjx0qenp5SaWmpJEmSVFxcLNnb20uLFi2SJEmSLl++LNnZ2cnXcuXKFcnJyUmaNGmS/D2r4rqVP7W9HkRERKQMc42DJEmSzpw5I7Vu3VqKjY2VysrKJEmSpC+++EJq3bq1pFKppFOnTpmcY+/evVKvXr2k/Pz8auf/+OOPpdatW0tlZWVSZGSk1LZtW+nq1avV9issLJTatGkjRUREyG0TJ06UmjVrJi1YsECOo0pycrLUqlUradSoUVJOTk7NSbqBVatWSe3atZMkSZLKy8tr3e+999675XHQ3Llz5eMzMjKkyZMnS25ublL//v2ljIwMSZIkqaioSJo2bZpka2srjRs3Th6rTZ8+XQIgPffcc7d8TURUM0GSriuPExEp6K233sLdd9+Nhx9+2GTdpuu9//77GDJkSK3TsYFrn4Kp1Wp522g04uLFi/D29jbZT5IkxMXFYcCAAQgODjbpKysrw/vvv4+JEydWO/9//vMfBAYGmiyKXpNDhw7hgQceqHU9hevjLCsru+lU9S1btkAURZNPIxMTE9G7d2906NABwLU1IHr37o22bdsCAN577z14e3vLi7w/88wzJlPMb2bhwoX49NNPcfTo0TrtT0RERLfHXOMg4NpM6eufRFdSUoLXX38dgwYNwgMPPFDnGBMSEjB79mxcvnwZL774IoYNG4aQkJAa9/3666/RuXNneZ2qiooK5OTkVBuPKeHtt9/Gm2++ifPnzyt63gEDBqBbt27yzLbi4mLMnj0bo0aNqnbd7777Ljp16oS+ffuatH/yySfo2bOnWa6bqCliUYqIqAHLzc2FKIryAJGIiIiIiKihYFGKiIiIiIiIiIgsjgudExERERERERGRxbEoRUREREREREREFseiFBERERERERERWZzm5rs0baIo4sKFC3BwcIAgCNYOh4iIiMxEkiQYDAZ4enrW+pRNMsVxEhERUdNgrnESi1I3ceHCBXh5eVk7DCIiIrKQ33//HW3atLF2GA0Cx0lERERNi9LjJBalbsLBwQHAtcQ7OjpaORrrEEUReXl5cHd35yfHCmA+lcV8Kov5VBbzqSxz51Ov18PLy0v+3U83x3ES/54rjflUHnOqLOZTWcynssyZT3ONk1iUuomqqeiOjo5NerBVWloKR0dH/kOhAOZTWcynsphPZTGfyrJUPnkbWt1xnMS/50pjPpXHnCqL+VQW86ksS+RT6XGS1V71hIQEeHl5wc7ODn379sX//vc/AEBGRgaCgoLg4uKCmJgYSJIkH3PgwAH4+/vDzc0N8fHxJufbtm0bvL294enpic2bN5v0rVy5Ei1btkS7du2wb98+818cERERERERERHdkFWKUufOncOCBQuwY8cOnDp1Cr6+vhg7diyMRiOGDBmCHj164KeffkJmZiaSkpIAAHl5eYiIiEBUVBRSUlKwadMm7N+/H8C1Qtbo0aMxd+5c7NmzB/PmzcPp06cBAHv27MGsWbOwdu1afPDBB4iOjsaff/5pjcsmIiIiIiIiIqK/WKUodezYMYSEhKB79+64++678cwzz+Ds2bPYvXs3CgoKEB8fD19fX8TGxiIxMREAsGnTJnh6emLu3Lnw8/PDvHnz5L6EhASEhYUhOjoanTt3xpQpU5CcnAwAWL16NcaMGYPIyEj06tULkZGR2L59uzUum4iIiIiIiIiI/mKVNaUCAgKwb98+HD9+HD4+Pli1ahUeeughpKWlISQkBHZ2dgCAwMBAZGZmAgDS0tIQFhYm378YHByM2bNny32PPPKIfP7g4GAsWLBA7hs1apRJ38GDBxEdHV1jbEajEUajUd7W6/UArt2bKYqiUiloUERRhCRJTfb6lcZ8Kov5VBbzqSzmU1nmzidfJyIiIiLLslpRasSIEejWrRsAwMfHBz/88AMWL14MHx8feT9BEKBWq5Gfnw+9Xo+AgAC5z9HRERcuXABwrXB0/XF17atJXFwc5s+fX609Ly8PpaWlt3nFDZsoiigoKIAkSVx8TgHMp7KYT2Uxn8piPpVl7nwaDAbFz0lEREREtbNKUerHH3/E559/ju+//x733HMPli5dioEDB6Jfv37QarUm++p0OhQXF0Oj0Zj0VbUDuO2+msyZMwczZsyQt6see+ju7t6knyojCAIf06kQ5lNZzKeymE9lMZ/KMnc+dTqd4uckIiIiotpZpSi1efNmjBw5Evfddx8AYOHChVi9ejVGjBiBjIwMk30NBgNsbW3h6uqKvLy8au0AbruvJlqttlphDABUKlWTfEMhihJ+yStE7iUDrqIZOrZ0gkrFR2XfKUEQmuzPlDkwn8piPpXFfCrLnPnka0RERERkWVYZfYmiiNzcXHnbYDDIs6FSUlLk9qysLBiNRri6uiIoKMik79ixY2jdujUA3HYf3Vhq9hW88NFxzPr4ZySnZGPWxz/jhY+OIzX7irVDIyIiolpcvnwZPj4++PXXX+u0/4EDB+Dv7w83NzfEx8ebNzgiIiKi61ilKNW7d298+umnWLZsGT788EM8+uijaNWqFaZOnQq9Xo/169cDAGJjYzFgwACo1WpERETg8OHD2Lt3L8rLy7F06VKEh4cDAIYPH44tW7YgPT0dhYWFWLFihdw3YsQIrFq1Cjk5Obh06RISExPlPqpdavYVLNp1Ehk5BXDUaeBmr4WjToMTFwqwaNdJFqaIiIjqocuXL2Pw4MF1Lkjl5eUhIiICUVFRSElJwaZNm7B//37zBklERET0F6vcvjd8+HCcPHkSb7/9Nv744w906tQJ27dvh42NDRISEhAVFYWYmBioVCp8++23AAA3NzcsW7YMAwcOhL29PZydnZGUlAQA6NKlC6ZNm4aePXtCp9PBz88PkydPBgAMGTIEW7duhZ+fHwCgf//+GDZsmDUuu8EQRQkbjmTjanE52rawg0oAVKpKNNdq4G2rQfaVYmw8ko1uXi68lY+IiKgeGTlyJEaNGoUffvihTvtv2rQJnp6emDt3LgRBwLx585CYmIiwsDAzR0pEREQECJIkSdYO4p8uXryI1NRUhISEoEWLFiZ9WVlZOHXqFHr37g17e3uTvszMTOTk5CA0NLTaulH//e9/UVRUhNDQUAhC3Qsper0eTk5OKCgoaDILnZ+6qMeMj9LgqNOguVYDARI8bIzILddCgoAiYwX0pRWIf6IL7mnVNHKipKrbVz08PLh+iQKYT2Uxn8piPpVl7nw2ht/5WVlZ8PHxgSAIyMrKQtu2bW+4/7hx49CsWTOsWrUKAPDHH3+gX79+OHnyZI37G41GGI1GebvqgTD5+fkNNmd3ShRF5OXl8YEGCmE+lXW50IiEQ1nIu2pAs2bNbrr/Lb0xvIWdb+W8t/L29NbOewv73ujMElBSWopmOh0g3Np5b4Vi8d7ReW9h31t53Ux2lVBaaoROpwVQ/X26+X4mG1jO6n5S2GskLHm8u+L/hur1eri4uCg+TrLKTKmbadWqFQYNGlRjn4+PD3x8fGrsCwgIQEBAQI19QUFBisXX2BUUl6OsohI6m+oLvgOAzkaNy4VGFBSXWzgyIiIiupHaxki10ev1JmMnR0dHXLhwodb94+LiMH/+/GrteXl5KC0tvaXv3ViIooiCggJIksQiigIaSj4rRQnpfxSiuEwEcO0NY9UbzGtf/71v1dfSX2+Da+2rsf3vN89y2/V91x0kXXds1dcbfvwDF/Rlt3mVRNQQtXa0QW5uruL/hhoMBkXPV6VeFqXIupzsbGCrUaO0/Note/9UWl4JW40aTnY2VoiOiIiIlKLRaEyeOqzT6VBcXFzr/nPmzMGMGTPk7aqZUu7u7k16ppQgCJzZo5CGks+E77IQ++UZa4dxS6b1863zHSO3cmfJrS7mcQunvsWdby2WWzy1fG5JklBUVITmzZvXmqdbyd/1567Tvrcc9y0ccJs5qdO+tewsSRIKC4tgb2+az1uJ+3Zfy7qd23zL1ZgjbkmSIJWVmGVWuU6nU/R8VViUomo6eDigvYc9TlwogJ2t2uQviyRJyCs0opOnEzp4OFgvSCIiIrpjrq6uyMvLk7cNBkO1JRCup9VqTYpYVVQqVb0uIJibIAhNPgdKagj5/KPg2sxAdwct7nLS/f1mUfj7rbQg4Lqv/9n+15bJPn+3C0L1bdR6rprbq3rsbFV4sqsrena8u17ntKHgrfnKYj6VVZVPc/wbaq7Xh0UpqkalEjCmlzcW7TqJ7CvF8LC3haiWUGSsQG5hGZya2eDpXt5c5JyIiKiBCwoKwocffihvHzt2DK1bt7ZiREQNy2M92uDFh++xdhg3VPUmlYioPmIpkmrUw9sVrwzyx72eTtCXVuByoRH60gp08nTCK4P80cPb1dohEhERUR3p9XqUl1dfCzIiIgKHDx/G3r17UV5ejqVLlyI8PNwKERIREVFTxJlSVKse3q7o5uWC05cKkHspFx4tPdCxpRNnSBERETUwgYGBePvtt/Hoo4+atLu5uWHZsmUYOHAg7O3t4ezsjKSkJKvESERERE0Pi1J0QyqVgI4tHeEilMLDw5EFKSIiogbgn4+Z/vXXX2vdd+LEiQgPD8epU6fQu3dv2Nvbmzk6IiIiomtYlLISUZRwJteAguJyONnZoIOHAws+REREZBU+Pj7w8fGxdhhERETUxLAoZQWp2Vew4Ug2zuYWoqyiErYaNdp72GNML2+u1URERERERERETQIXOrew1OwrWLTrJDJyCuCo06CNix0cdRqcuFCARbtOIjX7irVDJCIiIiIiIiIyOxalLEgUJWw4ko2rxeVo28IOzbUaqFUCmms18Ha1Q0FJOTYeyYYoSjc/GRERERERERFRA8bb9yzoTK4BZ3ML4eGghSCYrh8lCALc7bX4JbcQZ3INuKeVo5WiJCIiIiKyjopKEUfPG6DNB1SCChIkVK3bL0lA1Ue3kiRd9zVQ1WO6D0yPh+lDAP5uv/n3wD/OdeaSQaErJiJq2liUsqCC4nKUVVRCZ6OtsV9no8blQiMKisstHBkRERERkfWt+vYc3v7mrLXDqDMbNW88ISK6EyxKWZCTnQ1sNWqUlleiubZ66kvLry167mRnY4XoiIiIiIis60JBKQCgpaMWLR11kO8tEAT5a0HAdV//s/2vLZN9rrVX3ahw/X7X37xQ/Vw1t1f1OOo0GNGjzZ1dMBFRE8eilAV18HBAew97nLhQADtbtcktfJIkIa/QiE6eTujg4WDFKImIiIiIrOvJEG8838/P2mEQEZGZcb6pBalUAsb08oZTMxtkXylGkbEClaKEImMFsq8Uw6mZDZ7u5Q2VSrj5yYiIiIiIiIiIGjAWpSysh7crXhnkj3s9naAvrcD5/GLoSyvQydMJrwzyRw9vV2uHSERERERERERkdrx9zwp6eLuim5cLzuQaUFBcDic7G3TwcOAMKSIiIiIiIiJqMliUshKVSsA9rRytHQYRERERERERkVXw9j0iIiIiIiIiIrI4FqWIiIiIiIiIiMjiWJQiIiIiIiIiIiKLY1GKiIiIiIiIiIgsjkUpIiIiIiIiIiKyOBaliIiIiIiIiIjI4qxSlEpKSoIgCNX+JCUl4cCBA/D394ebmxvi4+NNjtu2bRu8vb3h6emJzZs3m/StXLkSLVu2RLt27bBv3z6TvldeeQUuLi4IDAzEzz//bPbrIyIiIiIiIiKiG7NKUWrUqFHIz8+X//z+++9wc3ODv78/IiIiEBUVhZSUFGzatAn79+8HAGRkZGD06NGYO3cu9uzZg3nz5uH06dMAgD179mDWrFlYu3YtPvjgA0RHR+PPP/8EALz33nt47733sHPnTixcuBAjR45EWVmZNS6biIiIiIiIiIj+YpWilK2tLZydneU/GzduxNChQ5GSkgJPT0/MnTsXfn5+mDdvHhITEwEACQkJCAsLQ3R0NDp37owpU6YgOTkZALB69WqMGTMGkZGR6NWrFyIjI7F9+3a5b9asWejduzciIiLQsWNHHDx40BqXTUREREREREREf7H6mlKlpaVYvnw5Xn75ZaSlpSEsLAyCIAAAgoODkZqaCgBIS0tDv3795OPq0idJEtLT02s9joiIiIiIiIiIrENj7QA+/PBD3HfffWjbti30ej0CAgLkPkdHR1y4cAEAoNfr4ePjc0t9hYWFEEWxWt+ZM2dqjcdoNMJoNMrber0eACCKIkRRvMOrbZhEUYQkSU32+pXGfCqL+VQW86ks5lNZ5s4nXyciIiIiy7J6UWrNmjV47bXXAAAajQZarVbu0+l0KC4uvu0+jeba5dV2XE3i4uIwf/78au15eXkoLS29jSts+ERRREFBASRJgkpl9cl1DR7zqSzmU1nMp7KYT2WZO58Gg0HxcxIRERFR7axalDp79izOnj2Lhx56CADg6uqKvLw8ud9gMMDW1va2+5o1a4ZmzZohLy8Pjo6O1Y6ryZw5czBjxgx5W6/Xw8vLC+7u7vI5mhpRFCEIAtzd3fmmSgHMp7KYT2Uxn8piPpVl7nzqdDrFz0lEREREtbNqUerjjz/G4MGDYWNjAwAICgrChx9+KPcfO3YMrVu3lvtSUlIwfvz4Wvv69+9fra9nz55ISUmBr6+v3NexY8daY9JqtSYzq6qoVKom/YZCEIQmnwMlMZ/KYj6VxXwqi/lUljnzydeIiIiIyLKsOvr6z3/+g759+8rbEREROHz4MPbu3Yvy8nIsXboU4eHhAIDhw4djy5YtSE9PR2FhIVasWCH3jRgxAqtWrUJOTg4uXbqExMREk74lS5ZAr9fjzJkz2LZtm9xHRERERERERETWYbWiVElJCX744Qf06tVLbnNzc8OyZcswcOBAtGzZEqdPn8a///1vAECXLl0wbdo09OzZE61bt4ZarcbkyZMBAEOGDEH//v3h5+cHHx8fdOvWDcOGDQMATJgwAR4eHmjTpg06d+6MsWPHokePHpa/YCIiIiIiIiIiklnt9r1mzZqZPOWuysSJExEeHo5Tp06hd+/esLe3l/sWLVqE0aNHIycnB6GhofLaUIIgIDk5GVOnTkVRURFCQ0MhCAKAa7fjff311zh8+DC0Wi2Cg4Mtc4FERERERERERFQrqz99ryY+Pj7w8fGpsS8gIAABAQE19gUFBdXYrlKp0Lt3b8XiIyIiIiIiIiKiO8MVPYmIiIiIiIiIyOJYlCIiIiIiIiIiIotjUYqIiIiIiIiIiCyuXq4pRUREREREyikpq8ShX/JQWiECACRJkvskCZAg/f31X13SdftJf/3HZL/ajr/uxCb7XHcu0+P/juX0RYMi10tERA0Di1JERERERI3cm1+dRuJ3WdYOo860Gt7QQUTUFLAoRURERETUyF3SlwIA2raww11OzSAI19oFARAgyF9XEYSq1qp9am4HhL/Pdd05hOvbr/seuMG5qr62QTke7ep5h1dMREQNAYtSRERERERNxJhebTHuAR9rh1ErURSRm5sLN3uttUMhIiIL4LxYIiIiIiIiIiKyOBaliIiIiIiIiIjI4liUIiIiIiIiIiIii2NRioiIiIiIiIiILI5FKSIiIiIiIiIisjgWpYiIiIiIiIiIyOJYlCIiIiIiIiIiIotjUYqIiIiIiIiIiCyORSkiIiIiIiIiIrI4FqWIiIiIGomMjAwEBQXBxcUFMTExkCTphvtLkoRJkybB1dUVzs7OGDt2LEpKSiwULRERETV1LEoRERERNQJGoxFDhgxBjx498NNPPyEzMxNJSUk3PCY5ORmnT5/GsWPHcOjQIZw4cQJxcXGWCZiIiIiaPBaliIiIiBqB3bt3o6CgAPHx8fD19UVsbCwSExNveMyPP/6IESNGwNvbG507d8ajjz6Ks2fPWihiIiIiauo01g6AiIiIiO5cWloaQkJCYGdnBwAIDAxEZmbmDY+59957kZycjOHDh6O0tBRbtmzBjBkzat3faDTCaDTK23q9HgAgiiJEUVTgKhoeURQhSVL9v/6/7uSs77E2mHw2IMypsphPZTGfyjJnPs31GrEoRURERNQI6PV6+Pj4yNuCIECtViM/Px8uLi41HhMdHY3Vq1ejVatWAIAhQ4ZgzJgxtX6PuLg4zJ8/v1p7Xl4eSktL7/AKGiZRFFFQUABJkqBS1d+bEEqN114fg6EQubm5Vo6mdg0lnw0Jc6os5lNZzKeyzJlPg8Gg6PmqsChFRERE1AhoNBpotVqTNp1Oh+Li4lqLUsuXL4ezszOys7MhCAImTJiAmJgYvPXWWzXuP2fOHJOZVHq9Hl5eXnB3d4ejo6NyF9OAiKIIQRDg7u5er99Q6bQXAAAODvbw8PCwcjS1ayj5bEiYU2Uxn8piPpVlznzqdDpFz1eFRSkiIiKiRsDV1RUZGRkmbQaDAba2trUes2nTJixYsAB33303gGszoUJDQ2stSmm12mqFLwBQqVRN+s2EIAj1PwfCX//7K9b6rEHks4FhTpXFfCqL+VSWufJprteHrzoRERFRIxAUFISUlBR5OysrC0ajEa6urrUeI4qiya1cFy9eRGVlpVnjJCIiIqrCmVJEREREjUCfPn2g1+uxfv16jBs3DrGxsRgwYADUajWuXr0KBwcHqNVqk2N69+6NxYsXQ61Wo6ysDEuWLEFERISVroCIiIiaGqvPlHrppZcwZMgQeTsjIwNBQUFwcXFBTEwMJEmS+w4cOAB/f3+4ubkhPj7e5Dzbtm2Dt7c3PD09sXnzZpO+lStXomXLlmjXrh327dtn3gsiIiIisgKNRoOEhARMmTIFbm5u2LFjB5YsWQIAcHFxQXp6erVjFi5ciPvvvx8vvvgipk2bhs6dO2P58uWWDp2IiIiaKKvOlPr555+xatUqpKWlAbj2mOEhQ4YgPDwcW7ZswdSpU5GUlIRx48YhLy8PERERmDlzJqKiojBy5Eh069YNYWFhyMjIwOjRo7Fy5Urcd999GDZsGLp3746OHTtiz549mDVrFrZs2QJ3d3c8+eST+O9//4sWLVpY89KJiIiIFBcREYFz584hNTUVISEh8njn+g/5rufs7IyNGzdaMkQiIiIimdVmSomiiGeffRbTp09Hu3btAAC7d+9GQUEB4uPj4evri9jYWCQmJgK4thCnp6cn5s6dCz8/P8ybN0/uS0hIQFhYGKKjo9G5c2dMmTIFycnJAIDVq1djzJgxiIyMRK9evRAZGYnt27db56KJiIiIzKxVq1YYNGgQP4AjIiKies9qM6XWrFmD9PR0PPvss9i5cycefvhhpKWlISQkBHZ2dgCAwMBAZGZmAgDS0tIQFhYGQbj26JDg4GDMnj1b7nvkkUfkcwcHB2PBggVy36hRo0z6Dh48iOjo6BrjMhqNMBqN8rZerwdwrYgmiqJSl9+giKIISZKa7PUrjflUFvOpLOZTWcynssydT75ORERERJZllaJUYWEhXn31VbRr1w7Z2dlITk7GwoUL0bt3b/j4+Mj7CYIAtVqN/Px86PV6BAQEyH2Ojo64cOECgGuFo+uPq2tfTeLi4jB//vxq7Xl5eSgtLb39i27ARFFEQUEBJEniYzoVwHwqi/lUFvOpLOZTWebOp8FgUPycRERERFQ7qxSlPv30UxQVFWH//v1wc3NDRUUFOnfujPfffx/jxo0z2Ven06G4uBgajQZarbZaO4Db7qvJnDlzMGPGDHlbr9fDy8sL7u7ucHR0vLMLb6BEUYQgCHB3d+ebKgUwn8piPpXFfCqL+VSWufOp0+kUPycRERER1c4qRanz588jJCQEbm5u14LQaBAYGIhTp04hLy/PZF+DwQBbW1u4urqa9FW1A7jtvppotVqTIlYVlUrVpN9QCILQ5HOgJOZTWcynsphPZTGfyjJnPvkaEREREVmWVUZfbdq0QUlJiUlbdnY23n77baSkpMhtWVlZMBqNcHV1RVBQkEnfsWPH0Lp1awC47T4iIiIiIiIiIrIOqxSlBg0ahMzMTKxZswbnz5/HihUrkJaWhmHDhkGv12P9+vUAgNjYWAwYMABqtRoRERE4fPgw9u7di/LycixduhTh4eEAgOHDh2PLli1IT09HYWEhVqxYIfeNGDECq1atQk5ODi5duoTExES5j4iIiIiIiIiIrMMqt++1aNECX375JWbNmoUZM2bgrrvuwscffwwvLy8kJCQgKioKMTExUKlU+PbbbwEAbm5uWLZsGQYOHAh7e3s4OzsjKSkJANClSxdMmzYNPXv2hE6ng5+fHyZPngwAGDJkCLZu3Qo/Pz8AQP/+/TFs2DBrXDYREREREREREf3FKkUpAHjggQdMbqurEhERgXPnziE1NRUhISFo0aKF3Ddx4kSEh4fj1KlT6N27N+zt7eW+RYsWYfTo0cjJyUFoaKi8bpQgCEhOTsbUqVNRVFSE0NBQCIJg/gskIiIiIiIiIqJaWa0odSOtWrXCoEGDauzz8fGBj49PjX0BAQEICAiosS8oKEix+IiIiIiIiIiI6M7wMTNERERERERERGRxLEoREREREREREZHFsShFREREREREREQWx6IUERERERERERFZHItSRERERERERERkcSxKERERERERERGRxWmsHQAREREREd2YJEmQpL++/mv776+r2v/eB3+1S7jWUCle10FERFRPsChFRERERHSbPj2ag0W7MlEuAgLqVjCS/vpPVcHoWvHorz5Juu5ri1wCERGR1bAoRURERER0m3al/4H8kgprh1En9loNut/tYu0wiIiIZCxKERERERHdoZn/6oDBgZ4Q/toWBED4a0sQ/t5PEADhrwbhuj4Bwl/HVO1oevzf5xVq/B4Qrj9Xzftp1AJs1FxSloiI6g8WpYiIiIiI7pC7vS183JpbOwwiIqIGhR+VEBERERERERGRxbEoRUREREREREREFseiFBERERERERERWRyLUkREREREREREZHEsShERERERERERkcWxKEVERERERERERBbHohQREREREREREVkci1JERERERERERGRxLEoREREREREREZHFsShFREREREREREQWx6IUERERERERERFZHItSRERERERERERkcSxKERERERERERGRxbEoRUREREREREREFme1otTUqVMhCIL8p3379gCAjIwMBAUFwcXFBTExMZAkST7mwIED8Pf3h5ubG+Lj403Ot23bNnh7e8PT0xObN2826Vu5ciVatmyJdu3aYd++fea/OCIiIiIiIiIiuiGrFaV++ukn7Nq1C/n5+cjPz8exY8dgNBoxZMgQ9OjRAz/99BMyMzORlJQEAMjLy0NERASioqKQkpKCTZs2Yf/+/QCuFbJGjx6NuXPnYs+ePZg3bx5Onz4NANizZw9mzZqFtWvX4oMPPkB0dDT+/PNPa102ERERERERERHBSkWpiooKnDhxAn369IGzszOcnZ3h4OCA3bt3o6CgAPHx8fD19UVsbCwSExMBAJs2bYKnpyfmzp0LPz8/zJs3T+5LSEhAWFgYoqOj0blzZ0yZMgXJyckAgNWrV2PMmDGIjIxEr169EBkZie3bt1vjsomIiIiIiIiI6C8aa3zT9PR0iKKIrl27IicnB6GhoVi7di3S0tIQEhICOzs7AEBgYCAyMzMBAGlpaQgLC4MgCACA4OBgzJ49W+575JFH5PMHBwdjwYIFct+oUaNM+g4ePIjo6OgaYzMajTAajfK2Xq8HAIiiCFEUlUpBgyKKIiRJarLXrzTmU1nMp7KYT2Uxn8oydz75OhERERFZllWKUpmZmejYsSPeeecduLm5Yfr06Xj22Wdx7733wsfHR95PEASo1Wrk5+dDr9cjICBA7nN0dMSFCxcAXCscXX9cXftqEhcXh/nz51drz8vLQ2lp6e1fdAMmiiIKCgogSRJUKq6Nf6eYT2Uxn8piPpXFfCrL3Pk0GAyKn5OIiIiIameVotTo0aMxevRoeXvVqlXw8fGBv78/tFqtyb46nQ7FxcXQaDQmfVXtAG67ryZz5szBjBkz5G29Xg8vLy+4u7vD0dHxNq+4YRNFEYIgwN3dnW+qFMB8Kov5VBbzqSzmU1nmzqdOp1P8nERERERUO6sUpf7Jw8MDoiiiVatWyMjIMOkzGAywtbWFq6sr8vLyqrUDuO2+mmi12mqFMQBQqVRN+g2FIAhNPgdKYj6VxXwqi/lUFvOpLHPmk68RERERkWVZZfQVExODDz/8UN5OSUmBSqVC586dkZKSIrdnZWXBaDTC1dUVQUFBJn3Hjh1D69atAeC2+4iIiIiIiIiIyDqsUpTq0qUL/v3vf+Obb77BV199hYkTJ+Lpp5/Gv/71L+j1eqxfvx4AEBsbiwEDBkCtViMiIgKHDx/G3r17UV5ejqVLlyI8PBwAMHz4cGzZsgXp6ekoLCzEihUr5L4RI0Zg1apVyMnJwaVLl5CYmCj3ERERERERERGRdVjl9r0nn3wSJ06cwPDhw6FWq/Hkk08iNjYWGo0GCQkJiIqKQkxMDFQqFb799lsAgJubG5YtW4aBAwfC3t4ezs7OSEpKAnCtyDVt2jT07NkTOp0Ofn5+mDx5MgBgyJAh2Lp1K/z8/AAA/fv3x7Bhw6xx2URERERERERE9BerrSkVFxeHuLi4au0RERE4d+4cUlNTERISghYtWsh9EydORHh4OE6dOoXevXvD3t5e7lu0aBFGjx6NnJwchIaGyutGCYKA5ORkTJ06FUVFRQgNDYUgCOa/QCIiIiIiIiIiqlW9XNGzVatWGDRokElBqoqPjw8eeeQRk4JUlYCAADz00EM1LmQeFBSEvn37siBFREREjVZGRgaCgoLg4uKCmJgYSJJUp+NEUUSvXr3w1ltvmTlCIiIior/Vy6IUEREREd0ao9GIIUOGoEePHvjpp5+QmZkpL3VwM2vWrEFBQQGmTp1q3iCJiIiIrsOiFBEREVEjsHv3bhQUFCA+Ph6+vr6IjY1FYmLiTY+7cOECXn75ZbzzzjuwsbGxQKRERERE17AoRURERNQIpKWlISQkBHZ2dgCAwMBAZGZm3vS4F154Ad7e3vj9999x5MgRc4dJREREJLPaQudEREREpBy9Xg8fHx95WxAEqNVq5Ofnw8XFpcZjUlJSsHXrVgwcOBDnzp3DwoULER4ejnfffbfG/Y1GI4xGo8n3BK6tSSWKooJX0/BIktTkc6AEURSZS4Uxp8piPpXFfCrLnPk012vEohQRERFRI6DRaKDVak3adDodiouLay1KrVu3Dvfddx+++OILCIKA//u//4O3tzeef/55dOzYsdr+cXFxmD9/frX2vLw8lJaWKnMhDUxVkc6gNyA3N9fK0TR8oiiioKAAkiRBpeJNHUpgTpXFfCqL+VSWOfNpMBgUPV8VFqWIiIiIGgFXV1dkZGSYtBkMhhqfSlzl/PnzGDhwoPx0Yi8vL7i7u+PcuXM1FqXmzJmDGTNmyNt6vV4+xtHRUaEraVi02t8AAA6ODvDw8LByNA2fKIoQBAHu7u58g6oQ5lRZzKeymE9lmTOfOp1O0fNVYVGKiIiIqBEICgrCunXr5O2srCwYjUa4urrWekybNm1QUlIibxcWFuLKlSto3bp1jftrtdpqs7EAQKVSNfk3E4IgNPkcKKUql8yncphTZTGfymI+lWWufJrr9eGrTkRERNQI9OnTB3q9HuvXrwcAxMbGYsCAAVCr1bh69SoqKyurHRMVFYV169bhm2++QXZ2NiZPnox77rkHgYGBlg6fiIiImqBbLkpVVFQgKSnphvscPHgQZWVltxsTERERUZNy6tSpam1XrlwBAJSUlGDo0KE3PYdGo0FCQgKmTJkCNzc37NixA0uWLAEAuLi4ID09vdoxDz30EJYsWYJJkybhnnvuwS+//IJt27bJt/MRERERmdMtF6UEQcCMGTMQHx+PLVu24OjRoyafvF26dAkjR47Ezp07FQ2UiIiIqLEKDQ3F6dOnERUVJbc99thj2Lx5M2xsbHDu3Lk6nSciIgLnzp3Dhg0bcPLkSQQEBAC49mS4rl271njM+PHjcebMGZSUlCAlJaXGtaSIiIiIzOGW15RSq9UAgNzcXBw/fhzvvvsuTp8+jd69e2PcuHF4+eWXERkZiREjRigeLBEREVFj1KJFC3h4eOCPP/7Ac889h/vvvx8qlQojR46EIAjQaOo+ZGvVqhUGDRpkxmiJiIiIlFHnmVJpaWkoLi4GADg7O2Px4sXYuHEjvvvuO5w9exZ6vR6RkZFwd3fH6tWrzRYwERERUWNjb28PFxcX7Nq1CwEBAejXrx9eeOEFfPDBBwCuzXQiIiIiamzqXJSaNGkSWrRogdDQUBQWFmL//v1Yt24dxo4di06dOsHDwwP79+9HYWGhPIAiIiIiorp799138dRTT+H48eN4/PHH8b///c/aIRERERGZTZ3ngh85cgSXL1/GN998g127diEyMhIlJSWYNWsWMjMz4eDgAAD45JNPEBISgr59+6JNmzZmC5yIiIioMcjNzUV5eTlEUcSxY8cwdOhQLF26FKNGjcLw4cMhiiIEQYAkSSgtLUWzZs2sHTIRERGRIuo8U+r999/Hf/7zH1RWVkKtViMnJwePPfYY8vLysHbtWuTk5AAA4uLiEB0djU8//dRsQRMRERE1BidPnkTr1q2h1+uxceNGJCUloW3btoiNjUXPnj0RGBgIGxsbpKWlQa1Ww97e3tohExERESmmzjOlunbtinfffReiKEKn02HChAm466678NZbb6F58+ZYsWIFunTpgpKSErz99tuwtbU1Z9xEREREDV6HDh3w888/44knnsDatWvxwQcfwMfHB3fddRfee+89rFixAk8++ST69OmDQ4cOwWg0WjtkIiIiIsXUeabUd999h6CgINjb2+PUqVNwcHCAn58fJEmCh4cHfvnlF5SXl+PKlSuoqKgwZ8xEREREjYJarYa/vz90Oh2OHDmCMWPG4IMPPkBYWBjat2+PtLQ0ODs7Q61Ww8nJCR4eHtYOmYiIiEgxdZ4plZaWBhsbG5w/fx4HDx6ERqOBTqfDAw88gNLSUpSWlsJgMKB///6YM2cOli9fbs64iYiIiBqdoUOHQqvVokuXLigtLUVoaCgAQBAEK0dGREREpLw6z5RavXo1mjdvjn/9618IDg7G77//jkmTJmHp0qUoKChA165dUVFRgcWLF+Obb77B+fPnzRk3ERERUaNhMBhQUVGBoUOH4vvvv4dOp8O8efNYjCIiIqJGrc5FqSVLlqCsrAwuLi7w9/dHQkICHnvsMXTp0gWurq5ITU3FokWLoFKp8OSTT2Ljxo3mjJuIiIio0cjLy8PVq1fh6emJN998E23atMG4cePw1FNPoby8HKWlpdYOkYiIiEhxdb5975VXXoFKpcL58+fh7++Pnj17YtasWVCr1Zg2bRpcXFzQv39/AMDo0aPh7OxsrpiJiIiIGpXLly8DADZs2CC3TZs2DRMmTEBlZSUefPBBa4VGREREZDZ1LkqpVNcmVbVp0wZt2rQBAIwZMwYAEBMTY7Kvl5eXUvERERERNUkODg5wcHAAAKxduxanTp1Cu3bt+IRjIiIiajTqfPtelQsXLtywPz8/Hw899BAuXrx420ERERERNRXnz5/H7t275e3Lly9jzZo1JvtIkoRp06ahU6dOlg6PiIiIyGxuuSjVqVMndOvWDfPmzcOvv/5arX/OnDn4/fff4eLiokR8RERERI3ab7/9hqSkJHm7tLQU+/btAwBkZGQgLS0NJ06cwOLFi1FcXGylKImIiIiUd8tFKUmS8Morr+DXX39FamqqSd/69euxadMmbNmyBVqtts7nfPjhh+XB2IEDB+Dv7w83NzfEx8eb7Ldt2zZ4e3vD09MTmzdvNulbuXIlWrZsiXbt2skDuSqvvPIKXFxcEBgYiJ9//vkWrpaIiIjIvNRqNb755hv07t0b4eHhmDRpEs6dO4fDhw/jzTffxPTp0/H8889j5syZGDVqlLXDJSIiIlJMndeUqiIIAkaMGIERI0bIbZIkIT4+HkuWLMHXX3+Nrl271vl8mzZtwp49ezBy5Ejk5eUhIiICM2fORFRUFEaOHIlu3bohLCwMGRkZGD16NFauXIn77rsPw4YNQ/fu3dGxY0fs2bMHs2bNwpYtW+Du7o4nn3wS//3vf9GiRQu89957eO+997Bz507k5+dj5MiROH78ONdjICIionrj/vvvx5IlS1BYWIhff/0V8+bNw/z583Hy5ElERkbi+eefR8eOHa0dJhEREZGibnmmVJXFixfjpZdewsSJE+Hr64vjx48jNTUVISEhdT7HlStXMHPmTHmQtWnTJnh6emLu3Lnw8/PDvHnzkJiYCABISEhAWFgYoqOj0blzZ0yZMgXJyckAgNWrV2PMmDGIjIxEr169EBkZie3bt8t9s2bNQu/evREREYGOHTvi4MGDt3vZRERERIoqLCyEWq2GIAh45JFH8OKLL6Jbt2746quvcPr0aXTo0AEPP/wwXnvtNYiiaO1wiYiIiBRTp6KU0WjEq6++iitXrshtkiShuLgYly5dgsFgwJ9//on8/Pxb+uYzZ87E0KFD5UJWWloawsLCIAgCACA4OFi+RTAtLQ39+vWTj61LnyRJSE9Pr/U4IiIiImvr378/+vTpg3379mHBggWYOXMmgoODAVwbg50+fRoBAQE4f/48Ro4cicrKSitHTERERKSMOt2+l52djSNHjmD58uUoLi5Gamoq5syZI/dXVlZi/fr1CA8Px8qVKzFs2LCbnnP//v345ptvcOLECTz//PMAAL1ej4CAAHkfR0dH+Wl/er0ePj4+t9RXWFgIURSr9Z05c6bWuIxGI4xGo7yt1+sBAKIoNtlPJ0VRhCRJTfb6lcZ8Kov5VBbzqSzmU1nmzqc1X6elS5di0qRJWL16tfz/6dOnY+nSpTh+/DgeeughvPbaa4iLi0NpaSmaN29utViJiIiIlFKnolSHDh3w9ddfIy8vD++//z4GDx6M0NBQvPHGG/Dy8oJarUZ0dDS6deuG8PBweHh44MEHH6z1fKWlpZgwYQJWr14NBweHv4PRaEwWSNfpdPJTZm6nT6O5dnm1HVeTuLg4zJ8/v1p7Xl4eSktLaz2uMRNFEQUFBZAkCSrVbd/xSX9hPpXFfCqL+VQW86ksc+fTYDAofs660ul0CAoKwvr16+X/f/nll+jduzeCg4MxceJEBAcHm3woSERERNTQ3dJC5+7u7njppZcwdepUzJs3D+np6fD09IRarQYA9OjRA/Hx8YiKisLJkydhb29f43lef/11BAUFYdCgQSbtrq6uyMvLk7cNBoO8IPnt9DVr1gzNmjVDXl4eHB0dqx1Xkzlz5mDGjBnytl6vh5eXF9zd3eVzNDWiKEIQBLi7u/NNlQKYT2Uxn8piPpXFfCrL3PnU6XSKn7Ou7r//fmzbtg1hYWHYunUrwsLCsHz5cnh6egIAOnXqhDfffBMDBw60WoxERERESrvlp+/99NNPeOmll/Dpp5/C0dERPXv2REhICBYuXAgXFxc8/fTT+OSTT3Dx4kW0b9++xnN8+OGHyMvLg7OzMwCguLgYH3/8MQCgV69e8n7Hjh1D69atAQBBQUFISUnB+PHja+3r379/tb6ePXsiJSUFvr6+ct+Nnl6j1WpNZlZVUalUTfoNhSAITT4HSmI+lcV8Kov5VBbzqSxz5tOar9HmzZvxzTff4Mcff8T48ePxxBNPoLi4GNu2bTOZVU5ERETUmNzS6Gv16tXo27cv+vbtCzs7OwiCgC1btsBgMMDf3x+rV6+GKIrYsWNHrQUpADh06BAyMjJw/PhxHD9+HBEREViwYAF+++03HD58GHv37kV5eTmWLl2K8PBwAMDw4cOxZcsWpKeno7CwECtWrJD7RowYgVWrViEnJweXLl1CYmKiSd+SJUug1+tx5swZbNu2Te4jIiIisjZ3d3e0b98eY8aMwVtvvYUHH3wQqampyM/PR48ePdChQwd06NAB7du3x913323tcImIiIgUU+eZUidPnkRcXBy++uork9lMfn5+2LhxI44ePYpnnnkGq1atwmeffSbPTKpJmzZtTLbt7e3h5uYGNzc3LFu2DAMHDoS9vT2cnZ2RlJQEAOjSpQumTZuGnj17QqfTwc/PD5MnTwYADBkyBFu3boWfnx+Aa0+xqVpsfcKECdixYwfatGkDo9GI6Oho9OjRo66XTURERGRWP//8M+zs7LB37158+eWXeOCBB7Bnzx4cPnwYjz32GMaPHw8vLy9IkoSSkhJrh0tERESkmDoXpfz9/XH27Nla12Pq3r07vv/+e7zzzjsmT7uri6rCEwBMnDgR4eHhOHXqFHr37m2yLtWiRYswevRo5OTkIDQ0VI5FEAQkJydj6tSpKCoqQmhoKARBAHDtdryvv/4ahw8fhlarlR+xTERERFQf3HXXXQCArl27okWLFujbty+eeeYZXLlyBQsWLEB6ejo6deoEAPLSB0RERESNwS2tKXWjBcIBQK1WY8KECXe8JoOPj0+tha2AgAAEBATU2BcUFFRju0qlQu/eve8oJiIiIiJz8vX1NZlp7urqirffflveFkURlZWVsLGxsUJ0RERERMqrc1Fq9+7daNasWbWCU1lZGYKCguDk5IQPP/wQ0dHRmDVrFuLi4hQPloiIiKgxqno6cNVM78rKSpSVlcHJyQkAcOHCBURFReGhhx7Cv//9b2uGSkRERKSYOhelhg8fjr59+0KSJJN2QRDw9ttvw8nJCWPGjMEDDzyABx54gEUpIiIiojpycnKCIAiQJEn+v0qlQkVFBQ4cOIDHH38cgwYNwvPPP2/tUImIiIgUU+eiVMuWLfHll1/W2Ldt2zacOnUKAFBUVISysjJloiMiIiJqAry9vZGVlWXS5uLiAlEU8fzzz2PFihV44oknrBQdERERkXnUuShVNZ18+/btWLZsGby9veHr64v27dsjLi4Ovr6+8qd7s2bNMlvARERERI1N1Tjrn20qlQrHjh2DWq22QlRERERE5nVLC50DQKdOnTBx4kRUVlaiqKgIR48eBQDk5+fjjTfeQEhIiOJBEhERETVVhw4dQt++fa0dBhEREZHi6lyUKi8vxy+//AJJktCzZ0+5vV+/fli4cCGOHDmCZ555BpMnT8aUKVPMEiwRERFRY3TlyhW8/PLL8rYkSSgtLcWff/6JsWPHonv37tiwYQMcHBysGCURERGRsupclNJqtRg2bBg0mr8PEUURRqMRCQkJGDBgAL7//ns88MADaN26NYYOHWqWgImIiIgam9mzZ0Or1UKlUkGv16NZs2Z45ZVX0KJFC5w6dQovvvgi7r//fnzxxRdo27attcMlIiIiUkSdi1JnzpzBJ598gscee0xu++2333D33XcDAPbt24eOHTtiw4YNGDJkCB566CHY29srHzERERFRI7J48WKEhISgb9++KCsrg6urKwoLC3HgwAEEBQVh//79WLFiBdasWYMXXngBn332mbVDJiIiIlKE6lZ2Hjt2rPx1WVkZ7rnnHnl7//79WLduHbp3747FixezIEVERERUB6Io4vDhwwAAW1tb2NjYoLCwEM8//zwWLVokj6kmTpyIpKQkK0ZKREREpKw6z5RSqVSorKzEggULAFxb60AURXk7KysL//vf//Daa6/hqaeeMk+0RERERI3MgAEDcP/992PRokWQJAllZWVwcnKCSqXCo48+Kj/duLy8HKIoorKy0tohExERESnilmZKCYIAnU4HrVYLnU4HlUoFrVYLrVaLzp0749SpU7hw4YK5YiUiIiJqdDp37gxPT08UFxejuLgYdnZ2WLlyJe655x6MHz8eFy5cQHFxMcrLy2EwGKwdLhEREZFi6lyUKikpgSAIePHFF/HSSy9h+vTpAICXXnpJ/tOtWze8//77ZguWiIiIqLFp1qwZBEFAUVERKisrIUkSJk6ciKNHj8LZ2RldunRBamoqAMDOzs7K0RIREREpp86379na2uK7774z2T5+/LjJPjNnzoSnp6diwRERERE1Bdu2bUPz5s1RUVGBWbNmAQBsbGzw+uuvo3PnzrjrrrusHCERERGR8uo8U0qtVqN79+4mbR06dMDFixfx66+/4rfffkOnTp3g6uqKs2fPKh4oERERUWMVHBwMANBoNHjttddM+rKysuDg4ACDwYC1a9daIToiIiIi86jzTCkA+Pzzz/Hxxx9DEAQAgE6nQ2VlJb744guoVNfqW3l5efDw8ODaUkRERER10KNHD9ja2kIQBIiiCH9/f6xfvx4AkJycjHXr1mHSpEl45513sGPHDjz77LNWjpiIiIhIGbdUlGrTpg369OkDlUoFSZLw8ssvIzc312QfFxcXFqSIiIiI6qioqAjx8fGYMGEC3nvvPUyZMgWvvPIKpk+fjhkzZiAiIgIqlQrLli3Df/7zH2uHS0RERKSYWypKdevWDd26dZO358yZU22fqllURERERHRzjo6OCA0Nlf9fWVmJDRs24N5778Xo0aPx66+/YseOHejTpw969uxp7XCJiIiIFFPnNaVqwgIUERERkbKcnJwwd+5cpKSk4LXXXsPly5cxevRobNy40dqhERERESmqzjOlVq9eDTs7O5NClNFoNBkgSZKEsrIyZSMkIiIiasRycnKwYMECXLhwAQsWLIAgCOjXrx9mzpyJefPm4ezZs3jxxRcBAK6urpg6dSrs7OysHDURERHRnatzUeqrr76CVqutVpTavXu3yX4VFRXKRUdERETUyEmShJKSEoiiiJKSEvz888+4fPkybG1tsXz5ckiShBYtWgC4tvB58+bN8fzzz1s5aiIiIqI7V+ei1Pbt26u1ubu7Y/PmzSZtrq6udx4VERERURPRpk0bxMXF4ZtvvkFcXBzeeecdvPbaa7jvvvvkhc1ffPFFFBcXw83NDT4+PrWeKyMjA+PGjcPZs2cRHR2NpUuX1nm5hatXr8Lf3x8pKSlo27atEpdGREREdENcU4qIiIioHuncuTPUajXy8vIwbtw4XL58GXFxcZg6dSrGjx+Pfv361Xic0WjEkCFD0KNHD/z000/IzMxEUlJSnb9vTEwMLl68qNBVEBEREd3cLRWlLl++jKysLPz222/Izs42V0xERERETUZZWRl+++03+f+CIGD+/PnIysrC6NGjIQgCunXrhp07d+KHH36o9Ty7d+9GQUEB4uPj4evri9jYWCQmJtYphoMHD2Lnzp3ybYJEREREllDn2/cAICEhAfHx8VCr1QAAZ2dnvP/++zh79iyAa2silJaWKh8l0U2IooQzuQYUFJfDyc4GHTwcoFJxJh8REdV/v/76K4KDgyEIAnr27ImuXbsiKCgIMTExcHR0hJ+fH8rLy7FkyRJMmDABR48ehUpV/XPFtLQ0hISEyIugBwYGIjMz86bf32g0YsKECVixYgVeeuklxa+PiIiIqDa3VJSaPXs2Zs+ebdKWnJwMW1tb+Va+6dOn1/l8V69exenTp9GhQwe4uLjcSihEstTsK9hwJBtncwtRVlEJW40a7T3sMaaXN3p4c40zIiKq365evVpje9WYa9myZejevTvc3Nxw8OBBFBUVwcHBodr+er3eZL0pQRCgVquRn59/w3FWbGwsOnTogCeeeOKmRSmj0Qij0WjyPQFAFEWIonjDYxs7SZKafA6UIIoic6kw5lRZzKeymE9lmTOf5nqNbqkoVZOnnnrqto7bunUr/u///g9eXl743//+h6SkJDz22GM3XKDzwIEDmDhxIvLy8vDyyy9jxowZ8vm2bduGmTNnory8HG+99RaioqLkvpUrV2LBggVo3rw5EhISal2LgRqe1OwrWLTrJK4Wl8PDQQudjRal5ZU4caEAi3adxCuD/FmYIiKiBu3hhx+Wv77RGlEajQZardakTafTobi4uNai1MmTJ7FmzRocO3asTrHExcVh/vz51drz8vKa7Gz5qiKdQW9Abm6ulaNp+ERRREFBASRJqnFGIN065lRZzKeymE9lmTOfBoNB0fNVueOi1O0oKCjA5MmTcfDgQQQGBiIpKQkxMTGIiIjAkCFDEB4eji1btmDq1KlISkrCuHHjkJeXh4iICMycORNRUVEYOXIkunXrhrCwMGRkZGD06NFYuXIl7rvvPgwbNgzdu3dHx44dsWfPHsyaNQtbtmyBu7s7nnzySfz3v//lmgmNgChK2HAkG1eLy9G2hZ1cvGyu1cDOVo3sK8XYeCQb3bxceCsfERE1eq6ursjIyDBpMxgMsLW1rXF/SZLw7LPPYuHChfD09KzT95gzZ47Jh4J6vR5eXl5wd3eHo6Pj7QffgGm1vwEAHBwd4OHhYeVoGj5RFCEIAtzd3fkGVSHMqbKYT2Uxn8oyZz51Op2i56tilaKUXq/H22+/jcDAQABA9+7d8eeff5os0GlnZ4fY2Fg899xzGDduHDZt2gRPT0/MnTsXgiBg3rx5SExMRFhYGBISEhAWFobo6GgAwJQpU5CcnIyFCxdi9erVGDNmDCIjIwEAkZGR2L59u7wvNVxncg04m1sIDwdttSdBCoIAd3stfsktxJlcA+5p1TQHykRE1HQEBQVh3bp18nZWVhaMRiNcXWueMfzbb7/hu+++Q3p6OmJiYgBcG6MFBgZizZo1GDVqVLVjtFpttdlYAKBSqZr8mwlBEJp8DpRSlUvmUznMqbKYT2Uxn8oyVz7N9fpYpSjl5eWF0aNHAwDKy8uxbNkyDB069IYLdKalpSEsLEwuPgQHB8trLaSlpeGRRx6Rzx8cHIwFCxbIfdcPqoKDg3Hw4MFai1JcK6G6+nqf79WiMpRXVKCZjS0ESNX6m9mo8GdhBa4WldWr2OtrPhsq5lNZzKeymE9lmTufDf116tOnD/R6PdavX49x48YhNjYWAwYMgFqtxtWrV+Hg4CA/rAYAWrdujaysLJNzPPjgg9iyZQu6du1q4eiJiIioKbJKUapKWloa+vXrB1tbW5w8eRKvv/56rQt06vV6BAQEyH2Ojo64cOECgOoLe9a1ryZcK6G6+nqfr6asGO0dRDRTlUBno67WX1peCZ2DCE2ZHrm5FVaIsGb1NZ8NFfOpLOZTWcynssydT3OtlWApGo0GCQkJiIqKQkxMDFQqFb799lsAgIuLC44dO2ZSbNJoNGjbtm21c7Rp0wb29vaWC5yIiIiaLKsWpQIDA/HVV19h+vTpiI6Ohq+vb60LdP5z8c6qdqD6wp517asJ10qorr7e5+vmJsH2+FX8fKEA3q62JrfwSZKE7CvF6OTpgq5+3vVqTan6ms+GivlUFvOpLOZTWebOp7nWSrCkiIgInDt3DqmpqQgJCZHX0JSk6jOKa/Lrr7+aMToiIiIiU1YtSgmCgB49emDDhg3w9fVFXFxcrQt0urq6Ii8vr1o7gNvuqwnXSqhZfbzPV6UCnu7VFot2ncSvV0rgbq+FzkaN0vJK5BUa4dTMFk/1aguNpvosKmurj/lsyJhPZTGfymI+lWXOfDaW16hVq1YYNGiQtcMgIiIiuimrjL4OHDggL6gJALa212a5+Pv7IyUlRW6/foHOoKAgk75jx46hdevWAHDbfdTw9fB2xSuD/HGvpxP0pRU4n18MfWkFOnk64ZVB/ujhXfPirkRERERERERkXVYpSnXo0AFr167F2rVr8fvvv+Pll1/Gv/71LwwcOFBeoBOAyQKdEREROHz4MPbu3Yvy8nIsXboU4eHhAIDhw4djy5YtSE9PR2FhIVasWCH3jRgxAqtWrUJOTg4uXbqExMREuY8ahx7ernj7ia6If6ILFg3tjPgnumDZE11ZkCIiIiIiIiKqx6xy+95dd92Fbdu24YUXXsCsWbMQHh6OjRs33nCBTjc3NyxbtgwDBw6Evb09nJ2dkZSUBADo0qULpk2bhp49e0Kn08HPzw+TJ08GAAwZMgRbt26Fn58fAKB///4YNmyYNS6bzEilEnBPq6a55hcRERERERFRQ2S1NaUeeughnDhxolp7bQt0AsDEiRMRHh6OU6dOoXfv3iZPhlm0aBFGjx6NnJwchIaGyutGCYKA5ORkTJ06FUVFRQgNDTVZEJuIiIiIiIiIiCzPqgud1+ZGC3T6+PjAx8enxr6AgAAEBATU2BcUFKRYfEREREREREREdGcax2NmiIiIiIiIiIioQamXM6WIiIiIqGn7JPU83v7mDCoqJQCAJAESrv8a8tf4R7sk/bUt919rqzoGJsdLJueq9j2qjof097mu+x6ifFIiIiK6VSxKEREREVG9szX1d/x+pcTaYdSJViPgXk8+cIWIiOhWsShFRERERPVO1aykFx/uiD5+7nK7IAAChL+//uv5NQKE676uar9uP/l44bqvTc9l8j3+ahBQ/XvIuwqAJEoo1l+Bt6eTAldNRETUtLAoRURERET11t2udujUuv4WfERRRG6J2tphEBERNUhc6JyIiIiIiIiIiCyORSkiIiIiIiIiIrI4FqWIiIiIiIiIiMjiWJQiIiIiIiIiIiKLY1GKiIiIiIiIiIgsjkUpIiIiIiIiIiKyOBaliIiIiIiIiIjI4liUIiIiIiIiIiIii2NRioiIiIiIiIiILI5FKSIiIiIiIiIisjgWpYiIiIiIiIiIyOJYlCIiIiIiIiIiIotjUYrIgkRRwulLepy+qMfpS3qIomTtkIiIiIiIiIisQmPtAIiaitTsK9hwJBvncg24y9aIP8ouwtfDAWN6eaOHt6u1wyMiIiIiIiKyKM6UIrKA1OwrWLTrJDJyCuCo08DNXgtHnQYnLhRg0a6TSM2+Yu0QiYiIiIiIiCyKRSkiMxNFCRuOZONqcTnatrBDc60GKpWA5loNvF3tUFBSjo1HsnkrHxERERERETUpLEoRmdmZXAPO5hbCw0ELQRBM+gRBgLu9Fr/kFuJMrsFKERIRERERERFZHotSRGZWUFyOsopK6GzUNfbrbNQoq6hEQXG5hSMjIiIiIiIish4WpYjMzMnOBrYaNUrLK2vsLy2vhK1GDSc7GwtHRkRERERERGQ9VitK7dixA+3atYNGo0HXrl1x8uRJAEBGRgaCgoLg4uKCmJgYSNLf6+wcOHAA/v7+cHNzQ3x8vMn5tm3bBm9vb3h6emLz5s0mfStXrkTLli3Rrl077Nu3z/wXR3SdDh4OaO9hj7xCo8nPMwBIkoS8QiP8POzRwcPBShESERERERERWZ5VilLnzp3DuHHjsHjxYuTk5KBDhw6Ijo6G0WjEkCFD0KNHD/z000/IzMxEUlISACAvLw8RERGIiopCSkoKNm3ahP379wO4VsgaPXo05s6diz179mDevHk4ffo0AGDPnj2YNWsW1q5diw8++ADR0dH4888/rXHZ1ESpVALG9PKGUzMbZF8pRpGxAqIoochYgewrxXBqZoOne3lDpRJufjIiIiIiIiKiRsIqRamTJ09i8eLFePzxx9GyZUtMmjQJx44dw+7du1FQUID4+Hj4+voiNjYWiYmJAIBNmzbB09MTc+fOhZ+fH+bNmyf3JSQkICwsDNHR0ejcuTOmTJmC5ORkAMDq1asxZswYREZGolevXoiMjMT27dutcdnUhPXwdsUrg/xxr6cT9KUVuFxohL60Ap08nfDKIH/08Ha1dohEREREREREFqWxxjcdPHiwyfbp06fh5+eHtLQ0hISEwM7ODgAQGBiIzMxMAEBaWhrCwsLkp5cFBwdj9uzZct8jjzwiny84OBgLFiyQ+0aNGmXSd/DgQURHR5vvAolq0MPbFd28XHD6UgFyL+XCo6UHOrZ04gwpIiIiIiIiapKsUpS6XllZGd566y3MmDEDZ8+ehY+Pj9wnCALUajXy8/Oh1+sREBAg9zk6OuLChQsAAL1eb3JcXftqYjQaYTQa5W29Xg8AEEURoije4dU2TKIoQpKkJnv9SvNzt4czSuDubg9AgihKNz2GasefT2Uxn8piPpVl7nzydSIiIiKyLKsXpV599VU0b94c0dHR+Pe//w2tVmvSr9PpUFxcDI1GY9JX1Q7gtvtqEhcXh/nz51drz8vLQ2lp6e1dZAMniiIKCgogSRJUKj6w8U4xn8piPpXFfCqL+VSWufNpMBgUPycRERER1c6qRal9+/Zh5cqV+P7772FjYwNXV1dkZGSY7GMwGGBrawtXV1fk5eVVawdw2301mTNnDmbMmCFv6/V6eHl5wd3dHY6Ojnd2wQ2UKIoQBAHu7u58U6UA5lNZzKeymE9lMZ/KMnc+dTqd4uckIiIiotpZrSiVlZWFqKgorFy5Ur4tLygoCOvWrTPZx2g0wtXVFUFBQfjwww/lvmPHjqF169bycSkpKRg/fnytff3796/WVxOtVlttthYAqFSqJv2GQhCEJp8DJTGfymI+lcV8Kov5VJY588nXiIiIiMiyrDL6KikpweDBgxEZGYmhQ4eisLAQhYWF6N27N/R6PdavXw8AiI2NxYABA6BWqxEREYHDhw9j7969KC8vx9KlSxEeHg4AGD58OLZs2YL09HQUFhZixYoVct+IESOwatUq5OTk4NKlS0hMTJT7iIiIiIiIiIjIOqwyU+qrr75CZmYmMjMzq82MSkhIQFRUFGJiYqBSqfDtt98CANzc3LBs2TIMHDgQ9vb2cHZ2RlJSEgCgS5cumDZtGnr27AmdTgc/Pz9MnjwZADBkyBBs3boVfn5+AID+/ftj2LBhFr1eIiIiIiIiIiIyZZWiVGRkJCSp5ieOtW3bFufOnUNqaipCQkLQokULuW/ixIkIDw/HqVOn0Lt3b9jb28t9ixYtwujRo5GTk4PQ0FB53ShBEJCcnIypU6eiqKgIoaGhEATBvBdIREREREREREQ3ZPWn79WkVatWGDRoUI19Pj4+8PHxqbEvICBAXp/qn4KCghSLj4iIiIiIiIiI7gxX9CQiIiIiIiIiIotjUYqIiIiIiIiIiCyORSkiIiIiIiIiIrI4FqWIiIiIiIiIiMjiWJQiIiIiaiQyMjIQFBQEFxcXxMTE1Pq04+vNnz8frq6u0Gq1GDp0KAwGgwUiJSIiImJRioiIiKhRMBqNGDJkCHr06IGffvoJmZmZSEpKuuExmzZtwqZNm/Cf//wHJ06cwMmTJ7F48WLLBExERERNHotSRERERI3A7t27UVBQgPj4ePj6+iI2NhaJiYk3POb333/Hhg0bEBwcjPbt2+OJJ57AsWPHLBQxERERNXUaawdARERERHcuLS0NISEhsLOzAwAEBgYiMzPzhsfMnj3bZPv06dPw8/MzW4xERERE12NRioiIiKgR0Ov18PHxkbcFQYBarUZ+fj5cXFxuevyZM2ewfft2HD16tNZ9jEYjjEajyfcEAFEUIYriHURfO0mUzHZuJYiiCEmq3zE2JMyn8phTZTGfymI+lWXOfJrrNWJRioiIiKgR0Gg00Gq1Jm06nQ7FxcU3LUqJoohnnnkG0dHRuPfee2vdLy4uDvPnz6/WnpeXh9LS0tsLvBZlZWUAgAJ9AXJz1YqeW0miKKKgoACSJEGl4soYd4r5VB5zqizmU1nMp7LMmU9zPQiFRSkiIiKiRsDV1RUZGRkmbQaDAba2tjc99vXXX8eVK1fwxhtv3HC/OXPmYMaMGfK2Xq+Hl5cX3N3d4ejoeHuB18LWNgsA4OToBA8PD0XPrSRRFCEIAtzd3fmGSgHMp/KYU2Uxn8piPpVlznzqdDpFz1eFRSkiIiKiRiAoKAjr1q2Tt7OysmA0GuHq6nrD4z7//HPEx8fj+++/l9ejqo1Wq602GwsAVCqV2d5MCCqh3r9REQTBrDloaphP5TGnymI+lcV8Kstc+TTX68NXnYiIiKgR6NOnD/R6PdavXw8AiI2NxYABA6BWq3H16lVUVlZWO+bkyZOIiorCO++8Ay8vLxQWFqK4uNjSoRMREVETxaIUERERUSOg0WiQkJCAKVOmwM3NDTt27MCSJUsAAC4uLkhPT692zNq1a1FUVIQxY8bAwcEBDg4OCAgIsHToRERE1ETx9j0iIiKiRiIiIgLnzp1DamoqQkJC0KJFCwCAJEk17r9s2TIsW7bMkiESERERyViUIiIiImpEWrVqhUGDBlk7DCIiIqKb4u17RERERERERERkcSxKERERERERERGRxbEoRUREREREREREFseiFBERERERERERWRyLUkREREREREREZHEsShERERERERERkcWxKEVERERERERERBbHohQREREREREREVkci1JERERERERERGRxVi1KXb58GT4+Pvj111/ltoyMDAQFBcHFxQUxMTGQJEnuO3DgAPz9/eHm5ob4+HiTc23btg3e3t7w9PTE5s2bTfpWrlyJli1bol27dti3b59Zr4mIiIiIiIiIiG7OakWpy5cvY/DgwSYFKaPRiCFDhqBHjx746aefkJmZiaSkJABAXl4eIiIiEBUVhZSUFGzatAn79+8HcK2QNXr0aMydOxd79uzBvHnzcPr0aQDAnj17MGvWLKxduxYffPABoqOj8eeff1r6comIiIiIiIiI6DpWK0qNHDkSo0aNMmnbvXs3CgoKEB8fD19fX8TGxiIxMREAsGnTJnh6emLu3Lnw8/PDvHnz5L6EhASEhYUhOjoanTt3xpQpU5CcnAwAWL16NcaMGYPIyEj06tULkZGR2L59u2UvloiIiIiIiIiITGis9Y3XrVsHHx8fTJs2TW5LS0tDSEgI7OzsAACBgYHIzMyU+8LCwiAIAgAgODgYs2fPlvseeeQR+TzBwcFYsGCB3Hd98Ss4OBgHDx5EdHR0jXEZjUYYjUZ5W6/XAwBEUYQoind83Q2RKIqQJKnJXr/SmE9lMZ/KYj6VxXwqy9z55OtEREREZFlWK0r5+PhUa9Pr9SbtgiBArVYjPz8fer0eAQEBcp+joyMuXLhQ43F17atJXFwc5s+fX609Ly8PpaWlt3CFjYcoiigoKIAkSVCpuDb+nWI+lcV8Kov5VBbzqSxz59NgMCh+TiIiIiKqndWKUjXRaDTQarUmbTqdDsXFxdX6qtprOq6ufTWZM2cOZsyYIW/r9Xp4eXnB3d0djo6Od3aBDZQoihAEAe7u7nxTpQDmU1nMp7KYT2Uxn8oydz51Op3i5yQiIiKi2tWropSrqysyMjJM2gwGA2xtbeHq6oq8vLxq7VXH3U5fTbRabbXCGACoVKom/YZCEIQmnwMl1fd8iqKEM7kGFBSXw8nOBh08HKBSCdYOq1b1PZ8NDfOpLOZTWebMJ18jIiIiIsuqV6OvoKAgpKSkyNtZWVkwGo1wdXWt1nfs2DG0bt26xuPq2kdE1aVmX8ELHx3HjI/S8Mr2dMz4KA0vfHQcqdlXrB0aERERERERNSL1qijVp08f6PV6rF+/HgAQGxuLAQMGQK1WIyIiAocPH8bevXtRXl6OpUuXIjw8HAAwfPhwbNmyBenp6SgsLMSKFSvkvhEjRmDVqlXIycnBpUuXkJiYKPcRkanU7CtYtOskMnIK4KjToI2LHRx1Gpy4UIBFu06yMEVERERERESKqVe372k0GiQkJCAqKgoxMTFQqVT49ttvAQBubm5YtmwZBg4cCHt7ezg7OyMpKQkA0KVLF0ybNg09e/aETqeDn58fJk+eDAAYMmQItm7dCj8/PwBA//79MWzYMGtcHlG9JooSNhzJxtXicrRtYSc/6bK5VgM7WzWyrxRj45FsdPNyqde38hEREREREVHDYPWilCRJJtsRERE4d+4cUlNTERISghYtWsh9EydORHh4OE6dOoXevXvD3t5e7lu0aBFGjx6NnJwchIaGyutGCYKA5ORkTJ06FUVFRQgNDZXfbBPR387kGnA2txAeDtpqf0cEQYC7vRa/5BbiTK4B97Rqmov+ExERERERkXKsXpSqSatWrTBo0KAa+3x8fODj41NjX0BAAAICAmrsCwoKUiw+osaooLgcZRWV0NlUX+gfAHQ2alwuNKKguNzCkREREREREVFjVK/WlCIi63Gys4GtRo3S8soa+0vLK2GrUcPJzsbCkREREREREVFjxKIUEQEAOng4oL2HPfIKjdVuq5UkCXmFRvh52KODh4OVIiQiIiIiIqLGhEUpIgIAqFQCxvTyhlMzG2RfKUaRsQKVooQiYwWyrxTDqZkNnu7lzUXOiYiIiIiISBEsShGRrIe3K14Z5I97PZ2gL63A+fxi6Esr0MnTCa8M8kcPb1drh0hERERERESNRL1c6JyIrKeHtyu6ebngTK4BBcXlcLKzQQcPB86QIiIiIiIiIkWxKEVE1ahUAu5p5WjtMIiIiIiIiKgR4+17RERERERERERkcSxKERERERERERGRxbEoRUREREREREREFseiFBERERERERERWRyLUkREREREREREZHEsShERERERERERkcWxKEVERERERERERBbHohQREREREREREVkci1JERERERERERGRxLEoREREREREREZHFsShFREREREREREQWx6IUERERERERERFZHItSRERERERERERkcSxKERERERERERGRxbEoRUQNlihKOH1Jj9MX9Th9SQ9RlKwdEhEREREREdWRxtoBEBHdjtTsK9hwJBvncg24y9aIP8ouwtfDAWN6eaOHt6u1wyMiIiIiIqKb4EwpImpwUrOvYNGuk8jIKYCjTgM3ey0cdRqcuFCARbtOIjX7irVDJCIiIiIioptgUYqIGhRRlLDhSDauFpejbQs7NNdqoFIJaK7VwNvVDgUl5dh4JJu38hEREREREdVzTaYolZGRgaCgILi4uCAmJgaSxDesRA3RmVwDzuYWwsNBC0EQTPoEQYC7vRa/5BbiTK7BShFWJ4oSTl3U44f//YlTF+vv2ldco4vqM/581s3tjHe2bdsGb29veHp6YvPmzRaIkoiIiOiaJrGmlNFoxJAhQxAeHo4tW7Zg6tSpSEpKwrhx46wdGhHdooLicpRVVEJno62xX2ejxuVCIwqKyy0cWc2q1r46m1uIsopK2GrUaO9hX+/WvuIaXVSf8eezbm5nvJORkYHRo0dj5cqVuO+++zBs2DB0794dHTt2tGDkRERE1FQ1iZlSu3fvRkFBAeLj4+Hr64vY2FgkJiZaOywiug1Odjaw1ahRWl5ZY39p+bXCj5OdjYUjq+6fa1+1cbGrl2tfcY0uqs/481l3tzPeSUhIQFhYGKKjo9G5c2dMmTIFycnJFoqYiIiImromUZRKS0tDSEgI7OzsAACBgYHIzMy0clREdDs6eDigvYc98gqN1W5LkSQJeYVG+HnYo4OHg5UivKamta/U9XDtK67RRfUZfz5vze2Md9LS0tCvXz95Ozg4GKmpqWaNk4iIiKhKk7h9T6/Xw8fHR94WBAFqtRr5+flwcXEx2ddoNMJoNJocCwCiKEIURcsEXM+IoghJkprs9SuN+bxzT99/N+K+PInfrhTBw94WolpEsbEcuYVlcG5mg6fuvxuAZNU3qqcv6XEu14CWDrZQCQDwdyyCAHjY2+JsrgGnLxWgY0vHehOnAAmABAESVIJQb+JsqPj3/c5Y+uezob9OtzLeqe0YR0dHXLhwodbvYY1xkiTW779D/HuuLOZTecypsphPZTGfyjJnPs31GjWJopRGo4FWa7r+jE6nQ3FxcbVBWlxcHObPn1/tHHl5eSgtLTVrnPWVKIooKCiAJElQqZrE5DqzYj7vXBsdMP3Blth3Khd/XC2BurwcToIN7vFqhn73eKCNrhy5ublWjTH3kh532RrhZgeoVNVvNRTVEi6LRuReyoWLYL1/W/4ZpwAJTupyCLj21r++xNlQ8e/7nbH0z6fBUH8ekHA7bmW8U9sxVfvXxpLjJBuhEo46NYoLDcjNVSt6biXx77mymE/lMafKYj6VxXwqy5z5NNc4qUkUpVxdXZGRkWHSZjAYYGtrW23fOXPmYMaMGfK2Xq+Hl5cX3N3d4ejYNGcJiKJ47alm7u78h0IBzKcyPDw8EBLQDmdy9ci9lAuPlh7o4OEIlUq4+cEWkC/p8EfZRRQVa9BcW/2f2iJjBfRlgEdLD3h4WO/fln/GKUCCBCCvXAsJQr2Js6Hi3/c7Y+mfT51Od+dBW9GtjHeuPyYvL6/O+1tynLQx2kPR85kL/54ri/lUHnOqLOZTWcynssyZT3ONk5pEUSooKAjr1q2Tt7OysmA0GuHqWv2JPVqtttqnjACgUqma9F8SQRCafA6UxHwqQ6UC7mnlBFeVER4eTvUqnx1bOsHXwwEnLhTA21YDQfi7WCZJEnILy9DJ0wkdWzpZtZD2zzhVgoBrN0kJECXUmzgbMv59v32W/vls6K/RrYx3rj8mJSUF48ePBwAcO3YMrVu3rnV/jpNqxr/nymI+lcecKov5VBbzqSxz5dNcr0+TeNX79OkDvV6P9evXAwBiY2MxYMAAqNX1dyo4ETVsKpWAMb284dTMBtlXilFkrEClKKHIWIHsK8VwamaDp3t5W73QU1OcYj2Mk5om/nzemhuNd65evYrKyuq3Eg8fPhxbtmxBeno6CgsLsWLFCoSHh1s6dCIiImqimkRRSqPRICEhAVOmTIGbmxt27NiBJUuWWDssImrkeni74pVB/rjX0wn60gqczy+GvrQCnTyd8Mogf/Twrn32giX9M87LhcZ6GSc1Tfz5rLsbjXdcXFyQnp5e7ZguXbpg2rRp6NmzJ1q3bg21Wo3JkydbOnQiIiJqoprE7XsAEBERgXPnziE1NRUhISFo0aKFtUMioiagh7crunm54EyuAQXF5XCys0EHD4d6N7OjKs7TlwrkNbp4yx7VF/z5rLvaxjuSVPvTSBctWoTRo0cjJycHoaGhN1xTioiIiEhJTaYoBQCtWrXCoEGDrB0GETUxKpWAe1rV/0XCVSoBHVs6wkUohUc9WjSeCODP5624nfFOQEAAAgICzBQRERERUc2axO17RERERERERERUv7AoRUREREREREREFseiFBERERERERERWRyLUkREREREREREZHEsShERERERERERkcWxKEVERERERERERBansXYA9Z0kSQAAvV5v5UisRxRFGAwG6HQ6qFSsY94p5lNZzKeymE9lMZ/KMnc+q37XV/3up5vjOIl/z5XGfCqPOVUW86ks5lNZ5synucZJLErdhMFgAAB4eXlZORIiIiKyBIPBACcnJ2uH0SBwnERERNS0KD1OEiR+HHhDoijiwoULcHBwgCAI1g7HKvR6Pby8vPD777/D0dHR2uE0eMynsphPZTGfymI+lWXufEqSBIPBAE9PT35aW0ccJ/HvudKYT+Uxp8piPpXFfCrLnPk01ziJM6VuQqVSoU2bNtYOo15wdHTkPxQKYj6VxXwqi/lUFvOpLHPmkzOkbg3HSX/j33NlMZ/KY06VxXwqi/lUlrnyaY5xEj8GJCIiIiIiIiIii2NRioiIiIiIiIiILI5FKboprVaLV199FVqt1tqhNArMp7KYT2Uxn8piPpXFfFJ9xJ9LZTGfymNOlcV8Kov5VFZDzCcXOiciIiIiIiIiIovjTCkiIiIiIiIiIrI4FqWIiIiIiIiIiMjiWJQiIiIiIiIiIiKLY1GKbmjHjh1o164dNBoNunbtipMnT1o7pEbj4YcfRlJSkrXDaBReeuklDBkyxNphNHgJCQnw8vKCnZ0d+vbti//973/WDqlBunz5Mnx8fPDrr7/KbRkZGQgKCoKLiwtiYmLA5RzrrqZ88ncT1Rf8WTQfjpOUxbHSneM4SRkcJymrMYyTWJSiWp07dw7jxo3D4sWLkZOTgw4dOiA6OtraYTUKmzZtwp49e6wdRqPw888/Y9WqVVi+fLm1Q2nQzp07hwULFmDHjh04deoUfH19MXbsWGuH1eBcvnwZgwcPNhkYGI1GDBkyBD169MBPP/2EzMxMvtGqo5ryyd9NVF/wZ9F8OE5SFsdKd47jJGVwnKSsxjJOYlGKanXy5EksXrwYjz/+OFq2bIlJkybh2LFj1g6rwbty5QpmzpyJjh07WjuUBk8URTz77LOYPn062rVrZ+1wGrRjx44hJCQE3bt3x913341nnnkGZ8+etXZYDc7IkSMxatQok7bdu3ejoKAA8fHx8PX1RWxsLBITE60UYcNSUz75u4nqC/4smgfHScriWEkZHCcpg+MkZTWWcZLG2gFQ/TV48GCT7dOnT8PPz89K0TQeM2fOxNChQ1FSUmLtUBq8NWvWID09Hc8++yx27tyJhx9+GLa2ttYOq0EKCAjAvn37cPz4cfj4+GDVqlV46KGHrB1Wg7Nu3Tr4+Phg2rRpcltaWhpCQkJgZ2cHAAgMDERmZqa1QmxQasonfzdRfcGfRfPgOElZHCspg+MkZXCcpKzGMk7iTCmqk7KyMrz11luYOHGitUNp0Pbv349vvvkGS5cutXYoDV5hYSFeffVVtGvXDtnZ2Vi2bBkefPBBDmJvU0BAAEaMGIFu3brB2dkZKSkpePPNN60dVoPj4+NTrU2v15u0C4IAtVqN/Px8S4bWINWUz+vxdxPVF/xZVAbHScriWEk5HCcpg+MkZTWWcRKLUlQnr776Kpo3b17v70etz0pLSzFhwgSsXr0aDg4O1g6nwfv0009RVFSE/fv3Y/78+fj6669hMBiQnJxs7dAapB9//BGff/45vv/+e1y9ehVRUVEYOHAgF5pUgEajgVarNWnT6XQoLi62UkSNB383UX3Bn8U7x3GS8jhWUg7HSebDcZL5NJTfTSxK0U3t27cPK1euxIcffggbGxtrh9Ngvf766wgKCsKgQYOsHUqjcP78eYSEhMDNzQ3AtV9ogYGBvL//Nm3evBkjR47EfffdBycnJyxcuBDnzp1DWlqatUNr8FxdXZGXl2fSZjAYePvEHeLvJqov+LOoDI6TlMexknI4TjIfjpPMoyH9buKaUnRDWVlZiIqKwsqVKxEQEGDtcBq0Dz/8EHl5eXB2dgYAFBcX4+OPP8aPP/6IVatWWTe4BqhNmzbVpp9nZ2ejV69eVoqoYRNFEZcvX5a3DQYDiouLUVlZacWoGoegoCCsW7dO3s7KyoLRaISrq6sVo2rY+LuJ6gv+LCqH4yTlcaykHI6TzIfjJOU1tN9NLEpRrUpKSjB48GBERkZi6NChKCwsBAA0b94cgiBYObqG59ChQ6ioqJC3Z82ahZCQED5O9jYNGjQIzz//PNasWYPBgwfj008/RVpaGrZu3Wrt0Bqk3r17Y8yYMejevTtatmyJhIQEtGrVCoGBgdYOrcHr06cP9Ho91q9fj3HjxiE2NhYDBgyAWq22dmgNEn83UX3Bn0VlcZykPI6VlMNxkvlwnKSsBvm7SSKqxWeffSYBqPYnKyvL2qE1CmPGjJHWr19v7TAatO+++04KCQmRmjVrJrVr107auXOntUNqsERRlBYsWCDdfffdko2NjdStWzfp6NGj1g6rwfrnv5U7duyQ7OzspBYtWkju7u7SiRMnrBdcA3R9Pvm7ieoL/iyaF8dJyuBYSRkcJymL4yRlNfRxkiBJXJ2NiIjI3C5evIjU1FSEhISgRYsW1g6HiIiIqN7gOKnpYlGKiIiIiIiIiIgsjk/fIyIiIiIiIiIii2NRioiIiIiIiIiILI5FKSIiIiIiIiIisjgWpYiIiIiIiIiIyOJYlCIiIiIiIiIiIotjUYqIrKa0tLTG9kOHDqGystLC0dRs1KhR+O677wAAZWVlKC8vBx9aSkRERObGcRIRNQUsShGR1TzyyCNISEgwadu0aRP69OmDdevW1ekcXbt2xa5du+44ltDQULz77rvV2gMCAvDkk0/i6tWrmDx5MpycnODs7GzyRxAEZGRkVDv2xx9/RF5eHgBg+vTpeOmll2r9/hcvXsSyZcsAAO3atcPx48eRnJyMI0eO4Ny5c+jRo0etg1MiIiJqfDhO+hvHSUSNF4tSRGRx5eXluHr1Kv73v/+hR48euHLlCgDg1KlTeO6557B582YsW7YMhw4dMjmupKQEr732GsrKyuQ2rVYLOzs7eXv//v04cOCAvH3o0CGoVCqTgVF6enq1mGxtbWFra1utPSYmBsHBwcjPz0dCQgKKi4tRUFCAq1evyn+aN28OnU5X7dhp06bhhx9+AABoNBqTfcrLy02uw8nJCR9++CF27twJjUYDtVqNRYsW4a677sLRo0dha2tb4/cgIiKixoXjJI6TiJoSjbUDIKKmJzU1FVFRUbh48SJGjhyJyspKfP7553jooYfw7LPPYuTIkfDz88PDDz+M1157DZMmTYJKpYIoipg/fz7mzp0rn+v/27vXkKjyP47j7zM6RnnpshI7ZkstC8VEy27mLbZdlaDbXgIFoc3azAgfJBpt2BOpZ9EGXSC6UKmsGUW1K9SaTAZGLoKlMrvMbu22eUuNvLSul2Ycm/8D6fzX1Lb++2dC/bxgcM6ZmTNnzgP58P39zvdnGAaGYZjbDocDwzD45JNPALBarURERNDc3AxASEgIzc3NXL582Qw1AE1NTZSVldHa2kpOTg6PHz9m0aJFdHZ2cuHChX/8TYGBI/+dBgQEmAHJYrFw+PBhCgsLgaGwtX37dnJzcwFwua3qJesAAAd/SURBVFysX7+e3t5enj59Snl5ObGxsTQ0NFBeXs6KFSvM47rdbqxWKxaLxhVEREQmGuUk5SSRyURFKRHxu7i4OA4ePMjJkyfZu3cvjY2NxMXFsX79ei5cuGCGm1WrVlFdXc3FixcpLi5m5syZAGZAGs2LAWS0EDR9+nTsdrsZWAzDICwsjMjISBYtWoTVaiUkJAS3283UqVNxuVysWbNmxHHy8/NJTEwcts/r9WKxWEYNQtnZ2ezZs2fYvoGBAaxWKy6Xi59++omDBw/S1NREVVUVoaGhVFZWUlpayuDgIN9++y2PHj0iNDSUmzdvsnDhwjGvg4iIiIxPykn/pZwkMvGpKCUib8SVK1f44IMP2LlzJ0VFRXz88cf88ssvzJs3D5vNxrlz5wA4f/48brcbm832P/UKsFgstLS0MGPGDAB6e3ux2WwsWbKE+vp6M7CcOHGCDz/8kJSUFPN9wLDRxfr6evN5XFzcqI08Dxw4QF5eHiEhIfh8PlJTUzEMg2fPngEM68fg8XhITEykpKSEtLQ0njx5gtPpxGq1MmPGDBYuXMjSpUvJy8ujv7+foKAgYmNjOXr0qIKWiIjIBKacpJwkMlloTqOI+F1rayulpaXMnTuXtrY27ty5w/Lly3nvvffIysqitraWsrIyli1bRkNDgxlyvF4vb731FuHh4ebjzp07fPHFF+b28ePHh/U8sFgsREREDOtrYBgGZ8+e5fPPP3+l1WvGmv492v6vv/4aj8dDZ2cnXV1dPHjwgPb2dg4cOMChQ4dob283H93d3ZSUlACwZcsWHA4H169fZ/r06Xz11VdUVFSYI4YPHjwAoKWlhYiIiNe53CIiIjKOKCcpJ4lMJipKiYjfVVZWMmfOHH744Qd8Ph9hYWEEBQXx8OFD6urqAJg2bRpTp04lKCjIDDXHjh3jzJkzwwJLVFQUJSUl5nZDQwMul4uBgQEAc+TtRZs2bcLj8VBUVPRK59zc3My8efPMR21t7ajv+/uU+f3795OQkMDg4CAul4t79+6Zr/3+++98+eWX/PXXXwDs27ePy5cvc/ToUZKSkoiLi6O4uBiPx0N6ejqVlZX09/fT09OjsCUiIjKBKScpJ4lMJrp9T0T8LiUlhZSUFOrq6ti5cydJSUk4nU5mzpzJ3LlzgZGNOQF+/vlnQkNDX3pswzA4f/48xcXFAKOO8Pl8PgIDA8nOzh62ssvLREZGjpiW/jL5+fkcPnwYh8NBQEAAgYGBZt+Gjo4OPvvsM6KioggODgYgIyODmpoampubeeedd7h48SKNjY1UVVXR2dnJsWPHmD9/PtHR0a90viIiIjI+KScpJ4lMJpopJSJvRFdXF9XV1fz6669kZGQA8Oeff9La2jrmZ14MX6N5Plr4/O/g4CCtra3mtPXe3l68Xi8w1FBz69at/3jM0XoiwNiji9988w27d+/G4XBgt9uHvVZfX8/y5cuJiYmhsLDQPM+SkhIKCgpISEigoaGB7777jgULFhATE8OKFStwOp3s3bt31EaiIiIiMrEoJykniUwWKkqJiN/duHEDm83G8ePHCQsLIysrCxhaAWbKlCnA0GorY4WZ1+H1erHZbOa09eDgYHPK+qt6Hrb+Pi29ra1tzPA3a9YsKioqRgSta9euER0dTVpaGoWFhSNWx3E6nXR0dLB69Wo2b95Meno6MHRdNm3axK1bt9i4ceNrnbuIiIiML8pJykkik4lu3xMRv0tMTOTx48fcvXuXXbt28f777+NwOIiPjyctLY38/HzCw8NJSkrC5/OZocbj8bBjxw5yc3PNY/X09PDpp5+OuqQxDPUumD17trntdDqJjIwc8b7u7u5h4amvrw8YCmt2u33YlPQXP+d2u5k2bZr5uaSkJLq7u7l69Sq//fYbP/74Iw6Hg7Vr11JQUDDmijA5OTmkp6ezbt065syZQ35+PpGRkURERFBQUMCSJUvIzMzk7NmzY/5eERERGd+Uk5STRCYTzZQSEb8zDIPQ0FAGBgbM5Yv7+/sB2LZtGwkJCSxevBi73c6pU6f46KOPACguLqavr89cIebJkyd4vV56enrM7ba2NgCzB0J8fDw1NTXmd7/77rvDVp2pq6sjPDycmpoaoqKizP09PT0AL11e+erVq7z99tusXLnSDHR9fX3Ex8eTnJzMkSNHaGxsJDk5mfv371NUVDRm0KquriYnJ4fo6GiSk5O5cuUKZWVllJaWsnnzZi5dukR5eTl//PEHMTExtLS0vN5FFxERkXFBOWkk5SSRicvwjXUTsIjIG/bs2bMxlxn+f7p9+zZ2u90cxXtVbreb3t5eZs2a9a/Poba2lqqqKjZs2GA2Ke3q6uL06dNkZmaajT7dbjfff/89qamp//o7RUREZPxSTlJOEpkIVJQSERERERERERG/0+17IiIiIiIiIiLidypKiYiIiIiIiIiI36koJSIiIiIiIiIifqeilIiIiIiIiIiI+J2KUiIiIiIiIiIi4ncqSomIiIiIiIiIiN+pKCUiIiIiIiIiIn6nopSIiIiIiIiIiPidilIiIiIiIiIiIuJ3/wHHYtFrgH1/0wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 总结 ===\n",
      "1. RNN使用14时间步的滑动窗口，步长为4\n",
      "2. segment中的时间戳是输出时间步，不是原始时间步\n",
      "3. 每个输出时间步对应原始数据的4个时间步（80ms）\n",
      "4. 输出时间戳206-207表示2个输出步长，对应160ms实际时长\n",
      "5. 这2个输出步长实际上对应原始数据中更多的时间步（由于滑动窗口重叠）\n"
     ]
    }
   ],
   "source": [
    "# =============================================================================\n",
    "# 分析滑动窗口与时间戳的关系\n",
    "# =============================================================================\n",
    "\n",
    "# 从配置文件我们知道：\n",
    "PATCH_SIZE = 14  # 每个输入patch包含14个时间步\n",
    "PATCH_STRIDE = 4  # 滑动窗口步长为4个时间步\n",
    "ORIGINAL_BIN_SIZE_MS = 20  # 原始数据每个bin为20ms\n",
    "\n",
    "print(\"=== 滑动窗口参数分析 ===\")\n",
    "print(f\"输入patch大小: {PATCH_SIZE} 时间步\")\n",
    "print(f\"滑动窗口步长: {PATCH_STRIDE} 时间步\")\n",
    "print(f\"相邻窗口重叠: {PATCH_SIZE - PATCH_STRIDE} 时间步\")\n",
    "print(f\"原始数据分辨率: {ORIGINAL_BIN_SIZE_MS} ms/bin\")\n",
    "print(f\"输出数据分辨率: {ORIGINAL_BIN_SIZE_MS * PATCH_STRIDE} ms/output_step\")\n",
    "\n",
    "# 分析时间步长度分布\n",
    "if 'phoneme_data' in locals() and phoneme_data is not None:\n",
    "    durations = []\n",
    "    output_durations_ms = []\n",
    "    original_durations_ms = []\n",
    "    \n",
    "    for phoneme, segments in phoneme_data.items():\n",
    "        for segment in segments:\n",
    "            # 输出时间步长度\n",
    "            output_duration = segment['end_time'] - segment['start_time'] + 1\n",
    "            durations.append(output_duration)\n",
    "            \n",
    "            # 对应的输出时长（毫秒）\n",
    "            output_duration_ms = output_duration * ORIGINAL_BIN_SIZE_MS * PATCH_STRIDE\n",
    "            output_durations_ms.append(output_duration_ms)\n",
    "            \n",
    "            # 对应的原始数据时长（毫秒）\n",
    "            original_duration_ms = output_duration * PATCH_STRIDE * ORIGINAL_BIN_SIZE_MS\n",
    "            original_durations_ms.append(original_duration_ms)\n",
    "    \n",
    "    print(f\"\\n=== 时间步长度统计 ===\")\n",
    "    print(f\"总segment数量: {len(durations)}\")\n",
    "    print(f\"输出时间步长度:\")\n",
    "    print(f\"  最小: {min(durations)} 步\")\n",
    "    print(f\"  最大: {max(durations)} 步\")\n",
    "    print(f\"  平均: {np.mean(durations):.1f} 步\")\n",
    "    print(f\"  中位数: {np.median(durations):.1f} 步\")\n",
    "    \n",
    "    print(f\"\\n对应的实际时长(毫秒):\")\n",
    "    print(f\"  最小: {min(output_durations_ms)} ms\")\n",
    "    print(f\"  最大: {max(output_durations_ms)} ms\") \n",
    "    print(f\"  平均: {np.mean(output_durations_ms):.1f} ms\")\n",
    "    print(f\"  中位数: {np.median(output_durations_ms):.1f} ms\")\n",
    "    \n",
    "    # 长度分布\n",
    "    from collections import Counter\n",
    "    duration_counts = Counter(durations)\n",
    "    print(f\"\\n输出时间步长度分布 (前10个):\")\n",
    "    for length, count in duration_counts.most_common(10):\n",
    "        actual_ms = length * ORIGINAL_BIN_SIZE_MS * PATCH_STRIDE\n",
    "        print(f\"{length:2d} 步 ({actual_ms:3d}ms): {count:4d} 个segment ({count/len(durations)*100:.1f}%)\")\n",
    "    \n",
    "    # 分析您提到的例子\n",
    "    print(f\"\\n=== 示例分析 ===\")\n",
    "    example_segment = {'start_time': 206, 'end_time': 207}\n",
    "    output_steps = example_segment['end_time'] - example_segment['start_time'] + 1\n",
    "    actual_duration_ms = output_steps * ORIGINAL_BIN_SIZE_MS * PATCH_STRIDE\n",
    "    original_start_bin = example_segment['start_time'] * PATCH_STRIDE\n",
    "    original_end_bin = example_segment['end_time'] * PATCH_STRIDE + PATCH_SIZE - 1\n",
    "    \n",
    "    print(f\"示例segment: start_time={example_segment['start_time']}, end_time={example_segment['end_time']}\")\n",
    "    print(f\"输出时间步数: {output_steps}\")\n",
    "    print(f\"实际时长: {actual_duration_ms} ms\")\n",
    "    print(f\"对应原始数据范围: bin {original_start_bin} 到 bin {original_end_bin}\")\n",
    "    print(f\"原始数据时间步数: {original_end_bin - original_start_bin + 1}\")\n",
    "    \n",
    "    # 可视化长度分布\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    \n",
    "    # 子图1: 输出时间步长度分布\n",
    "    plt.subplot(2, 2, 1)\n",
    "    plt.hist(durations, bins=50, alpha=0.7, edgecolor='black')\n",
    "    plt.xlabel('输出时间步数')\n",
    "    plt.ylabel('频次')\n",
    "    plt.title('输出时间步长度分布')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图2: 实际时长分布\n",
    "    plt.subplot(2, 2, 2)\n",
    "    plt.hist(output_durations_ms, bins=50, alpha=0.7, color='orange', edgecolor='black')\n",
    "    plt.xlabel('实际时长 (ms)')\n",
    "    plt.ylabel('频次')\n",
    "    plt.title('实际时长分布')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图3: 时长vs频次散点图\n",
    "    plt.subplot(2, 2, 3)\n",
    "    unique_durations = list(duration_counts.keys())\n",
    "    counts = [duration_counts[d] for d in unique_durations]\n",
    "    plt.scatter(unique_durations, counts, alpha=0.7)\n",
    "    plt.xlabel('输出时间步数')\n",
    "    plt.ylabel('出现次数')\n",
    "    plt.title('时长频次散点图')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # 子图4: 累积分布\n",
    "    plt.subplot(2, 2, 4)\n",
    "    sorted_durations = sorted(durations)\n",
    "    cumulative = np.arange(1, len(sorted_durations) + 1) / len(sorted_durations)\n",
    "    plt.plot(sorted_durations, cumulative)\n",
    "    plt.xlabel('输出时间步数')\n",
    "    plt.ylabel('累积概率')\n",
    "    plt.title('时长累积分布函数')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "else:\n",
    "    print(\"phoneme_data 未加载，无法进行详细分析\")\n",
    "\n",
    "print(f\"\\n=== 总结 ===\")\n",
    "print(\"1. RNN使用14时间步的滑动窗口，步长为4\")\n",
    "print(\"2. segment中的时间戳是输出时间步，不是原始时间步\")\n",
    "print(\"3. 每个输出时间步对应原始数据的4个时间步（80ms）\")\n",
    "print(\"4. 输出时间戳206-207表示2个输出步长，对应160ms实际时长\")\n",
    "print(\"5. 这2个输出步长实际上对应原始数据中更多的时间步（由于滑动窗口重叠）\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea2c9908",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "找到 4 个PKL文件:\n",
      "  ctc_results_20251008_235600.pkl (25.6 MB)\n",
      "  ctc_results_20251009_202457.pkl (23.9 MB)\n",
      "  phoneme_dataset_20251008_235600.pkl (21.1 MB)\n",
      "  phoneme_dataset_20251009_202457.pkl (19.1 MB)\n",
      "\n",
      "分析最新的phoneme dataset: phoneme_dataset_20251009_202457.pkl\n",
      "=== 分析文件: phoneme_dataset_20251009_202457.pkl ===\n",
      "数据类型: <class 'dict'>\n",
      "字典键数量: 40\n",
      "字典键: ['B', 'R', 'IH', 'NG', ' | ', 'T', 'K', 'L', 'OW', 'S']...\n",
      "总segment数量: 214218\n",
      "\n",
      "音素分布 (前20个):\n",
      "  ' | ': 50607 segments\n",
      "  'T': 13049 segments\n",
      "  'AH': 12663 segments\n",
      "  'IH': 10561 segments\n",
      "  'N': 9368 segments\n",
      "  'S': 7054 segments\n",
      "  'R': 6865 segments\n",
      "  'L': 6429 segments\n",
      "  'IY': 6278 segments\n",
      "  'D': 6260 segments\n",
      "  'AY': 5337 segments\n",
      "  'DH': 5259 segments\n",
      "  'K': 5178 segments\n",
      "  'M': 5125 segments\n",
      "  'UW': 4802 segments\n",
      "  'EH': 4589 segments\n",
      "  'AE': 4373 segments\n",
      "  'Z': 4348 segments\n",
      "  'W': 4100 segments\n",
      "  'AA': 3403 segments\n",
      "\n",
      "=== Segment结构分析 ===\n",
      "示例segment结构:\n",
      "  phoneme: B (<class 'str'>)\n",
      "  start_time: 17 (<class 'int'>)\n",
      "  end_time: 18 (<class 'int'>)\n",
      "  confidence: 0.8151801824569702 (<class 'numpy.float64'>)\n",
      "  session: t15.2023.08.11 (<class 'str'>)\n",
      "  block_num: 2 (<class 'numpy.int64'>)\n",
      "  trial_num: 0 (<class 'numpy.int64'>)\n",
      "  corpus: 50-Word (<class 'str'>)\n",
      "\n",
      "字段统计:\n",
      "  不同session数: 1\n",
      "  不同corpus数: 1\n",
      "  平均持续时间: 1.7 time steps\n",
      "  平均置信度: 0.976\n",
      "\n",
      "=== Session分布分析 ===\n",
      "Session统计:\n",
      "  t15.2023.08.11:\n",
      "    Segments: 4771\n",
      "    Trials: 50\n",
      "    Phonemes: 34\n",
      "  t15.2023.08.13:\n",
      "    Segments: 8607\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.08.18:\n",
      "    Segments: 5281\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.08.20:\n",
      "    Segments: 7333\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.08.25:\n",
      "    Segments: 2319\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.08.27:\n",
      "    Segments: 4064\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.09.01:\n",
      "    Segments: 7875\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.09.03:\n",
      "    Segments: 8962\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.09.24:\n",
      "    Segments: 6282\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.09.29:\n",
      "    Segments: 4035\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.10.01:\n",
      "    Segments: 5869\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.10.06:\n",
      "    Segments: 4510\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.10.08:\n",
      "    Segments: 7583\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.10.13:\n",
      "    Segments: 3903\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.10.15:\n",
      "    Segments: 6246\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.10.20:\n",
      "    Segments: 2584\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.10.22:\n",
      "    Segments: 4172\n",
      "    Trials: 49\n",
      "    Phonemes: 40\n",
      "  t15.2023.11.03:\n",
      "    Segments: 3734\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.11.04:\n",
      "    Segments: 1732\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.11.17:\n",
      "    Segments: 2443\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.11.19:\n",
      "    Segments: 1358\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.11.26:\n",
      "    Segments: 5678\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.12.03:\n",
      "    Segments: 6631\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.12.08:\n",
      "    Segments: 6024\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.12.10:\n",
      "    Segments: 3732\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.12.17:\n",
      "    Segments: 3976\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2023.12.29:\n",
      "    Segments: 5764\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.02.25:\n",
      "    Segments: 5424\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.03.03:\n",
      "    Segments: 4121\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.03.08:\n",
      "    Segments: 5090\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.03.15:\n",
      "    Segments: 7502\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.03.17:\n",
      "    Segments: 7940\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.04.25:\n",
      "    Segments: 6605\n",
      "    Trials: 50\n",
      "    Phonemes: 39\n",
      "  t15.2024.04.28:\n",
      "    Segments: 2575\n",
      "    Trials: 50\n",
      "    Phonemes: 34\n",
      "  t15.2024.05.10:\n",
      "    Segments: 3012\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.06.14:\n",
      "    Segments: 2524\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.07.19:\n",
      "    Segments: 5249\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.07.21:\n",
      "    Segments: 4687\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2024.07.28:\n",
      "    Segments: 4603\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2025.01.10:\n",
      "    Segments: 3078\n",
      "    Trials: 49\n",
      "    Phonemes: 40\n",
      "  t15.2025.01.12:\n",
      "    Segments: 4819\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2025.03.14:\n",
      "    Segments: 1811\n",
      "    Trials: 25\n",
      "    Phonemes: 40\n",
      "  t15.2025.03.16:\n",
      "    Segments: 2812\n",
      "    Trials: 49\n",
      "    Phonemes: 40\n",
      "  t15.2025.03.30:\n",
      "    Segments: 4856\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n",
      "  t15.2025.04.13:\n",
      "    Segments: 2042\n",
      "    Trials: 50\n",
      "    Phonemes: 40\n"
     ]
    },
    {
     "data": {
      "image/png": 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0aZg/fz5q1aqF2NhY3L17F2+++aZugqnk5GSMHTsWvXr1Qnx8PICCdUEWL16MXr164erVqypX+GL+/vtv87+HDBmCqKgoFaspn/T0dMydOxc1atRATEwMgIJr7vLly1iyZImuzklpM3NM7YMeZrCZQgBTv7J161bMmTMHXl5eUBSlyDpgehnUFT4ve/bsAVAQkDRt2tTcDmiV6doZN24ctm3bVmRfZmYmjh8/Dk9PT4SEhJi3a/G8xMfHo2rVqhgxYgRGjhyJwYMHw87ODlFRUejRowe6du1a5PN66SdN0tPTMWvWLFSrVs086yMnJwenTp3CggUL8Mcff6hbYBklJSVhxowZGD16NKZPn46QkBDY2Njg559/xokTJzQ9U/LpIOPLL7+EoigIDg5GZGQkqlevjvr16+PmzZvYt28fateujW7duplDES2Jj49H5cqV8dFHHxXZvmzZMrRv3x5Dhgwpsv3UqVNQFEWTa0yZ2rCxY8eaQ5qEhAS8/fbb8PLyMn/OFFqZZn3rZaa0iZ+fH3bv3g0AWLhwIRRF0Xz/YnLnzh2sXbsW9evXh6Io2LFjBwBgzJgxGDhwoPmBtBYV7iMLr+155coV9O3bF/Xr14ePjw8uXryI8+fPw9PTE4GBgbpZu1BtDKXoucaPHw9nZ2f89ttvACpuMBUWFgZ3d3dkZmZqemAaHx+PVq1aoXv37mjbti1GjBiBxMRE7N69G3379i02gEhLS8O1a9dUqvb50tPT0aBBA3zxxRcACs7Fli1b0KNHD1hZWRW54U5JSUGTJk3Qtm1blaotn5SUFIwZMwa9e/c2z8TLz8/X3aLHQEHdsbGxeOuttzBz5kzMnDkTbm5uuHLlitqllYvpFZjmzZubB3V6VTgAOXDgAJYsWYJKlSrh/Pnzapf2XE9/7wv7559/sH79egQEBJhfTQa0GYCUpPB5OXLkCKKiotCiRYtiMw21pnDf0r59ewQHByM5Odn8VDgnJwexsbFo3LgxgoKCVK62ZKUtOh8ZGQkPDw/MmzcPwcHBGDx4cJH9Wu8nn5aRkWFux7777ju1y3lhN2/exAcffIDQ0FDs2rULkZGRaNiwoab7yJJ+cAIoCHHc3d2xfft2hIWFQVEUDBw4ENnZ2Th06BAGDx5cbNF9td2/fx9169ZFQEBAke2bNm3CnDlzsGLFCgQGBiI8PLzI/rNnz2ryIc7T4+OhQ4fi5s2buHDhAnr27In27dubx/cRERGwtrbG6dOnVa667Ez3X8OGDcOaNWvw1Vdfwc7OThd9fmE5OTnYu3cvqlSpAi8vL5w8eRL5+fm4e/eu2qU909N95KBBg3Dr1i0AQHR0NBRFQWxsrPnz9+/fL/Igl56NoRSVyfTp0+Hg4FDhgymt3zSYhIeHw8vLC5cvX0aXLl3Qs2dPzJ07F9HR0QgODsbQoUPVLrHMcnNzERkZCXd3d6xfvx5AwbnYtm0bevfujYkTJxZ5ypCUlITWrVvr5hdUUlJSMGHCBHTq1Am//PKL2uW8lLS0NHzyyScICAhAaGioLmbjlCQ9PR3z589HnTp18OOPP6pdzksxHYu9vT0URdHkk/iSFP7eb9y40bzdtEZGdnY2jEYjAgICMHLkSJWqfHGm8+Lg4ABFUXRz01C4bwkICEBAQAAiIiLM62Hl5+fj6NGjaNmypXkwriUZGRmYNm0aWrdubZ6pZrJ8+XJUr14dK1aswMCBA4sEnnpUuB0zGo1ql/PCTMFUnz59kJiYqPm1pDIyMhAWFoaWLVsWeU0XABYtWoQ33ngDMTExmDhxIhRFwbx58wD8fykMLcnOzsbSpUuLtMORkZFwcnJCfHw8srKysHLlSgQGBmLu3LnqFltGhduwrl27olu3boiIiMDRo0fRr18/dO3aFREREboMc0y+//57KIoCa2trnD17Vu1yXtjFixdRq1YtjBkzRhfr4QHF+0h/f38sWbIEmZmZGDp0KPz9/dUuUbcYSlGZvQ7BlNYVnsHVqlUrLFiwAADQoEEDeHh4oFatWtiyZQuaN2+O6dOnq1VmueXm5uLzzz+Hs7NzkWBqy5YtCAoKwtixY4tM6dXbVOubN29i6tSpSE5OVruUVyInJ0d35+Bp9+7dw6JFi3Djxg21S3lp9+7dw+LFi3Xz2o5J4e994WDKNCMqOzsb+/fvR8uWLYv8tLpe6Om8lNa3NGrUyPwq0sGDB80zPbS87sezFp0PDw+Hs7MzNmzYgHbt2unyuiqsorRjSUlJmDZtGlJTU9UupUxK+8EJoCCYqlatGpKTkzF79mz4+/trer0yUzvs4uKCoKAgeHh44MiRI+b9Dx48wPr16+Hn54dFixapWOmzldaGNWzYELVq1UK9evUQGxsLHx8fVK5cWTcPcEpy7949TJgwQRd9y/Ncv34dSUlJapfxXM/qI93c3NCoUSNs2rQJnTt3xrp169QqU9cYSlG5MJhSn6lh/Pbbb9GjRw906dIFXl5eOHToECZNmgRbW1s0aNAAly9fVrnS8snJySk1mBowYACGDh2K9PR0dYt8CXoPcSqiitR+6fVYCn/vSwumYmJidDFoLYmezktJfYu3tzcOHz6MSZMmwWAwYPLkycUWedWi0hadN70CfvjwYRw+fFjT6xaVlZ6usWfRWx/5rPDT39/f/CMgWn8lCSj4f7969WpUrlzZ/EAzLy/PfE4WLFiAPn36aL4dftb4eNy4cXB0dESXLl10t+xASfQys6giKUsfGRgYiJSUFJUr1SeGUlRuDKa04fbt2wgMDESVKlWKvLNsNBrx559/qljZiystmFq7di2GDRvGd7OJKqDnBVNkWaX1Lbt379ZViFPaovONGzeuEDMMSH2ma6xFixbYt2+feXtwcLBuXnczyc7OxqpVq+Di4oLo6Gjz9o8//hiKouDChQsqVlc+pbVhP/zwg27Hx6Qdz7q+9NRHag1DKXohDKa0IS4uDgaDAZs2bVK7lFem8A3qhg0bABRcX6Zf6COiiqfw937z5s1ql/Paqyh9S0mLzvv4+Ohm/UjSPtOvutarVw/jx4/H5MmT4erqqsvgs/Ar1bt378aaNWtgZ2eny1fdKkobRtrE6+vVsxGiFxAVFSUiIu3bt5dTp06Jj4+P5Ofni5WVlcqVvV58fX1l7ty5EhUVJW3atJFmzZqpXdJLMxgMMnHiRLG2tpb3339fDAaDhISEiJOTk9qlEdF/pPD3ftSoUWJjYyPDhw9Xu6zXVkXpW1xcXGTmzJlibW0tffv2laysLDl37pxUq1ZN7dKognBxcZHw8HBp2rSpfP3111KnTh05duyYNGnSRO3Sys3GxkYmTpwoVlZWMmjQIBEROXPmjLRu3VrlysqvorRhpE28vl49hlL0wkoKpvLy8sTa2lrlyl4vI0aMkOPHj4ubm5vapbwyBoNBxo0bJwaDQfz8/NQuh4gsoPD3Xo83QRVNRelbXFxcJCwsTOzs7GTAgAG6DAtI2xwcHGTYsGESHBwsiqLoehxsaoerVKki7dq1k8aNG6td0gurKG0YaROvr1dLAQC1iyB9mzFjhkRHR8uxY8fE19dX7XJeS48fP5ZKlSqpXQYREVUgFalv4WxuorIDIIqiqF3GS6tIbRhpD6+vV4ehFL0S48ePF6PRKH/99ZcYDIYK0ZERERERERER0X+HoRS9MmlpaVKjRg21yyAiIiIiIiIiHWAoRUREREREREREFseX64mIiIiIiIiIyOIYShERERERERERkcUxlCIiIiIiIiIiIotjKEVERERERERERBbHUIqIiIiIiIiIiCyOoRQREREREREREVkcQykiIiIiIiIiIrI4hlJERERERERERGRxDKWIiIiIiIiIiMjiGEoRERERaVR2drasXr1a/v33X7VLISIiInrlGEoRERERaZSdnZ0sX75c9u7dW+pn8vPzJSsrSwCYt3Xq1EmMRmORzzx69Og/rZWIiIiovBQUHsEQERERkWqWL18uK1asEEdHR1EURURE0tPTxWAwiKOjo4iIAJCHDx9KXFycuLq6yqVLl6RHjx5ia2tr/pvbt2+Ls7Oz2Nvbi0hBKOXm5iZxcXHqHBgRERFRCRhKEREREWnMlStXZNasWbJnzx4xGAwiInL16lWZMWOG7Ny50xxQmeTm5oqVlZVYW1uLiEhAQIDMnj1bevXqJSIiOTk5YmNjI1ZWnCRPRERE2sFQioiIiEhjAEjHjh1lxIgRMmXKFBERmTJliuTn58uaNWuKfX7ZsmWyY8cOsbW1FRGR33//XTw8PMTFxUUASFZWlmzfvl28vb0tehxEREREz8JQioiIiEgD7t27Jz4+PuLo6CgGg0EePHggTk5O5v23bt0Sd3d3sbOzk+zsbMnMzJQzZ87Ihg0bzGGUydq1ayUgIEC8vLzM2/Lz86V27doyatQoSx0SERER0TPZqF0AEREREYm4urrKrVu3yvU39+/fl44dO4qdnZ2IiMTExMi6deskPDxc2rRpI1euXJFt27ZJWFiYuLi4SOXKlf+L0omIiIheCGdKEREREWnI5cuXpWXLllK3bt1i+548eSKpqamSmZlpDqJERLKysqR+/fpy584d8fb2lkqVKklaWpo4OTnJhx9+KB06dJBmzZpZ8jCIiIiInoszpYiIiIg0xNbWVmxtbSUxMbHYvsTERPH09Cz2up69vb2cP39eHj16JDt27BCj0ShLliyR/v37S3Jysvj7+8vGjRulX79+ljoMIiIioufiTCkiIiIiDUlMTBRvb29p0qRJsX05OTly/fp1eXr4Nnr0aDlx4oQkJCSIiIibm5vk5eVJzZo1xcXFRapUqSInTpyQ06dPc8YUERERaQZnShERERFpjL29vVy6dKnYdtNMqaeFhIRIhw4dJDMzU9577z1ZtWqVZGRkyNKlS2XXrl3SvXt3SUhIYCBFREREmmKldgFERERE9H+5ubnP/UxeXp7532fOnJGdO3fKggUL5MmTJ+Ls7Gzen5ubK0ajURo2bCibN2+WGzdu/Gd1ExEREZUXZ0oRERERaciTJ08kKytLmjdvXmxfTk6O+b/29vaSm5srn376qbRo0UKuXr0qTk5OMm3aNImOjpaIiAgxGAyyefNmSUpKktmzZ0tqaqo0atTI0odEREREVCKuKUVERESkIXl5efLw4UNxdnZ+ob9PSEiQR48eia+v76stjIiIiOgVYyhFREREREREREQWxzWliIiIiIiIiIjI4hhKERERERERERGRxTGUIiIiIiIiIiIii2MoRUREREREREREFsdQioiIiIiIiIiILI6hFBERERERERERWRxDKSIiIiIiIiIisjiGUkREREREREREZHEMpYiIiIiIiIiIyOL+BzMVsJVlXu9qAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析最新的CTC results: ctc_results_20251009_202457.pkl\n",
      "=== 分析文件: ctc_results_20251009_202457.pkl ===\n",
      "数据类型: <class 'list'>\n",
      "列表长度: 4\n",
      "第一个元素类型: <class 'dict'>\n",
      "第一个元素的键: ['session', 'input_layer', 'trial_idx', 'block_num', 'trial_num', 'corpus', 'original_sequence', 'sentence_label', 'n_time_steps', 'predicted_phonemes', 'ctc_score', 'alignment_info']\n",
      "示例CTC结果结构:\n",
      "  session: t15.2023.08.11 (<class 'str'>)\n",
      "  input_layer: 0 (<class 'int'>)\n",
      "  trial_idx: 0 (<class 'int'>)\n",
      "  block_num: 2 (<class 'numpy.int64'>)\n",
      "  trial_num: 0 (<class 'numpy.int64'>)\n",
      "  corpus: 50-Word (<class 'str'>)\n",
      "  original_sequence: <class 'numpy.ndarray'> (500,)\n",
      "  sentence_label: Bring it closer. (<class 'str'>)\n",
      "  n_time_steps: 321 (<class 'numpy.int64'>)\n",
      "  predicted_phonemes: ['B', 'R', 'IH', 'NG', ' | ', 'IH', 'T', ' | ', 'K', 'L', 'OW', 'S', 'ER', ' | '] (<class 'list'>)\n",
      "  ctc_score: -409.8164916324466 (<class 'float'>)\n",
      "  alignment_info: [('B', 17, 18, np.float64(0.8151801824569702)), ('R', 19, 20, np.float64(0.9981182217597961)), ('IH', 21, 21, np.float64(0.9999998807907104)), ('NG', 22, 23, np.float64(0.9939739406108856)), (' | ', 24, 26, np.float64(0.9843189120292664)), ('IH', 42, 42, np.float64(1.0)), ('T', 43, 43, np.float64(0.9999998807907104)), (' | ', 44, 47, np.float64(0.9870144575834274)), ('K', 61, 62, np.float64(0.9999440908432007)), ('L', 63, 64, np.float64(0.9994930922985077)), ('OW', 65, 65, np.float64(0.9999935626983643)), ('S', 66, 66, np.float64(0.9999934434890747)), ('ER', 69, 70, np.float64(0.9999157190322876)), (' | ', 71, 74, np.float64(0.9988113343715668))] (<class 'list'>)\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "\"\"\"\n",
    "PKL文件读取和内容展示工具\n",
    "用于查看phoneme_segmented_data目录中的数据结构\n",
    "\"\"\"\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "def load_and_analyze_pkl(pkl_path):\n",
    "    \"\"\"加载并分析PKL文件\"\"\"\n",
    "    print(f\"=== 分析文件: {pkl_path.name} ===\")\n",
    "    \n",
    "    with open(pkl_path, 'rb') as f:\n",
    "        data = pickle.load(f)\n",
    "    \n",
    "    print(f\"数据类型: {type(data)}\")\n",
    "    \n",
    "    if isinstance(data, dict):\n",
    "        print(f\"字典键数量: {len(data)}\")\n",
    "        print(f\"字典键: {list(data.keys())[:10]}...\")  # 显示前10个键\n",
    "        \n",
    "        # 如果是phoneme dataset\n",
    "        if all(isinstance(v, list) for v in data.values()):\n",
    "            total_segments = sum(len(v) for v in data.values())\n",
    "            print(f\"总segment数量: {total_segments}\")\n",
    "            \n",
    "            # 显示每个phoneme的segment数量\n",
    "            phoneme_counts = {k: len(v) for k, v in data.items()}\n",
    "            sorted_phonemes = sorted(phoneme_counts.items(), key=lambda x: x[1], reverse=True)\n",
    "            \n",
    "            print(f\"\\n音素分布 (前20个):\")\n",
    "            for phoneme, count in sorted_phonemes[:20]:\n",
    "                print(f\"  '{phoneme}': {count:4d} segments\")\n",
    "            \n",
    "            return data, phoneme_counts\n",
    "            \n",
    "    elif isinstance(data, list):\n",
    "        print(f\"列表长度: {len(data)}\")\n",
    "        if len(data) > 0:\n",
    "            print(f\"第一个元素类型: {type(data[0])}\")\n",
    "            if isinstance(data[0], dict):\n",
    "                print(f\"第一个元素的键: {list(data[0].keys())}\")\n",
    "        return data, None\n",
    "    \n",
    "    return data, None\n",
    "\n",
    "def analyze_segment_structure(phoneme_data, sample_count=5):\n",
    "    \"\"\"分析segment结构\"\"\"\n",
    "    print(f\"\\n=== Segment结构分析 ===\")\n",
    "    \n",
    "    # 获取一些sample segments\n",
    "    sample_segments = []\n",
    "    for phoneme, segments in list(phoneme_data.items())[:3]:  # 取前3个phoneme\n",
    "        sample_segments.extend(segments[:sample_count])  # 每个phoneme取前5个\n",
    "    \n",
    "    if sample_segments:\n",
    "        print(f\"示例segment结构:\")\n",
    "        segment = sample_segments[0]\n",
    "        for key, value in segment.items():\n",
    "            print(f\"  {key}: {value} ({type(value)})\")\n",
    "        \n",
    "        # 统计不同字段的分布\n",
    "        sessions = set()\n",
    "        corpora = set()\n",
    "        durations = []\n",
    "        confidences = []\n",
    "        \n",
    "        for segment in sample_segments:\n",
    "            sessions.add(segment.get('session', 'unknown'))\n",
    "            corpora.add(segment.get('corpus', 'unknown'))\n",
    "            \n",
    "            start = segment.get('start_time', 0)\n",
    "            end = segment.get('end_time', 0)\n",
    "            if end >= start:\n",
    "                durations.append(end - start + 1)\n",
    "            \n",
    "            conf = segment.get('confidence', 0)\n",
    "            if conf > 0:\n",
    "                confidences.append(conf)\n",
    "        \n",
    "        print(f\"\\n字段统计:\")\n",
    "        print(f\"  不同session数: {len(sessions)}\")\n",
    "        print(f\"  不同corpus数: {len(corpora)}\")\n",
    "        print(f\"  平均持续时间: {np.mean(durations):.1f} time steps\")\n",
    "        print(f\"  平均置信度: {np.mean(confidences):.3f}\")\n",
    "\n",
    "def analyze_session_distribution(phoneme_data):\n",
    "    \"\"\"分析session分布\"\"\"\n",
    "    print(f\"\\n=== Session分布分析 ===\")\n",
    "    \n",
    "    session_stats = defaultdict(lambda: {'segments': 0, 'trials': set(), 'phonemes': set()})\n",
    "    \n",
    "    for phoneme, segments in phoneme_data.items():\n",
    "        for segment in segments:\n",
    "            session = segment.get('session', 'unknown')\n",
    "            trial_num = segment.get('trial_num', -1)\n",
    "            \n",
    "            session_stats[session]['segments'] += 1\n",
    "            session_stats[session]['trials'].add(trial_num)\n",
    "            session_stats[session]['phonemes'].add(phoneme)\n",
    "    \n",
    "    print(f\"Session统计:\")\n",
    "    for session in sorted(session_stats.keys()):\n",
    "        stats = session_stats[session]\n",
    "        print(f\"  {session}:\")\n",
    "        print(f\"    Segments: {stats['segments']}\")\n",
    "        print(f\"    Trials: {len(stats['trials'])}\")\n",
    "        print(f\"    Phonemes: {len(stats['phonemes'])}\")\n",
    "\n",
    "def plot_phoneme_distribution(phoneme_counts):\n",
    "    \"\"\"绘制音素分布图\"\"\"\n",
    "    # 排序并取前20个\n",
    "    sorted_phonemes = sorted(phoneme_counts.items(), key=lambda x: x[1], reverse=True)[:20]\n",
    "    phonemes, counts = zip(*sorted_phonemes)\n",
    "    \n",
    "    plt.figure(figsize=(12, 6))\n",
    "    bars = plt.bar(range(len(phonemes)), counts)\n",
    "    plt.xlabel('音素')\n",
    "    plt.ylabel('Segment数量')\n",
    "    plt.title('音素分布 (前20个)')\n",
    "    plt.xticks(range(len(phonemes)), phonemes, rotation=45)\n",
    "    \n",
    "    # 在条形图上显示数值\n",
    "    for i, bar in enumerate(bars):\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2., height + height*0.01,\n",
    "                f'{int(height)}', ha='center', va='bottom', fontsize=8)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 主要调用函数\n",
    "def analyze_pkl_files():\n",
    "    \"\"\"分析phoneme_segmented_data目录中的所有PKL文件\"\"\"\n",
    "    data_dir = Path(\"../phoneme_segmented_data\")\n",
    "    \n",
    "    if not data_dir.exists():\n",
    "        print(f\"目录不存在: {data_dir}\")\n",
    "        return\n",
    "    \n",
    "    # 查找所有PKL文件\n",
    "    pkl_files = list(data_dir.glob(\"*.pkl\"))\n",
    "    print(f\"找到 {len(pkl_files)} 个PKL文件:\")\n",
    "    \n",
    "    for pkl_file in pkl_files:\n",
    "        size_mb = pkl_file.stat().st_size / 1024 / 1024\n",
    "        print(f\"  {pkl_file.name} ({size_mb:.1f} MB)\")\n",
    "    \n",
    "    # 分析最新的phoneme dataset\n",
    "    phoneme_files = [f for f in pkl_files if f.name.startswith(\"phoneme_dataset_\")]\n",
    "    if phoneme_files:\n",
    "        latest_phoneme_file = max(phoneme_files, key=lambda x: x.stat().st_mtime)\n",
    "        print(f\"\\n分析最新的phoneme dataset: {latest_phoneme_file.name}\")\n",
    "        \n",
    "        phoneme_data, phoneme_counts = load_and_analyze_pkl(latest_phoneme_file)\n",
    "        \n",
    "        if phoneme_data and phoneme_counts:\n",
    "            analyze_segment_structure(phoneme_data)\n",
    "            analyze_session_distribution(phoneme_data)\n",
    "            plot_phoneme_distribution(phoneme_counts)\n",
    "    \n",
    "    # 分析最新的CTC results\n",
    "    ctc_files = [f for f in pkl_files if f.name.startswith(\"ctc_results_\")]\n",
    "    if ctc_files:\n",
    "        latest_ctc_file = max(ctc_files, key=lambda x: x.stat().st_mtime)\n",
    "        print(f\"\\n分析最新的CTC results: {latest_ctc_file.name}\")\n",
    "        \n",
    "        ctc_data, _ = load_and_analyze_pkl(latest_ctc_file)\n",
    "        \n",
    "        if isinstance(ctc_data, list) and len(ctc_data) > 0:\n",
    "            print(f\"示例CTC结果结构:\")\n",
    "            sample = ctc_data[0]\n",
    "            for key, value in sample.items():\n",
    "                if isinstance(value, np.ndarray):\n",
    "                    print(f\"  {key}: {type(value)} {value.shape}\")\n",
    "                else:\n",
    "                    print(f\"  {key}: {value} ({type(value)})\")\n",
    "\n",
    "# 运行分析\n",
    "analyze_pkl_files()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "hw37welyuqr",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据目录: f:\\BRAIN-TO-TEXT\\nejm-brain-to-text.worktrees\\dev2\\data_analyse\\..\\phoneme_segmented_data\n",
      "目录是否存在: True\n",
      "\n",
      "=== 发现的PKL文件 ===\n",
      "ctc_results_20251008_235600.pkl: 25.6 MB\n",
      "ctc_results_20251009_202457.pkl: 23.9 MB\n",
      "phoneme_dataset_20251008_235600.pkl: 21.1 MB\n",
      "phoneme_dataset_20251009_202457.pkl: 19.1 MB\n",
      "\n",
      "=== 加载最新的Phoneme Dataset ===\n",
      "文件: phoneme_dataset_20251009_202457.pkl\n",
      "数据类型: <class 'dict'>\n",
      "音素数量: 40\n",
      "总segment数量: 214218\n",
      "\n",
      "音素分布统计 (前15个):\n",
      " 1. ' | ': 50607 segments (23.6%)\n",
      " 2. 'T': 13049 segments (6.1%)\n",
      " 3. 'AH': 12663 segments (5.9%)\n",
      " 4. 'IH': 10561 segments (4.9%)\n",
      " 5. 'N': 9368 segments (4.4%)\n",
      " 6. 'S': 7054 segments (3.3%)\n",
      " 7. 'R': 6865 segments (3.2%)\n",
      " 8. 'L': 6429 segments (3.0%)\n",
      " 9. 'IY': 6278 segments (2.9%)\n",
      "10. 'D': 6260 segments (2.9%)\n",
      "11. 'AY': 5337 segments (2.5%)\n",
      "12. 'DH': 5259 segments (2.5%)\n",
      "13. 'K': 5178 segments (2.4%)\n",
      "14. 'M': 5125 segments (2.4%)\n",
      "15. 'UW': 4802 segments (2.2%)\n",
      "\n",
      "=== Segment结构分析 ===\n",
      "Segment字段数量: 8\n",
      "Segment字段: ['phoneme', 'start_time', 'end_time', 'confidence', 'session', 'block_num', 'trial_num', 'corpus']\n",
      "\n",
      "第一个segment详细信息:\n",
      "  phoneme: B (<class 'str'>)\n",
      "  start_time: 17 (<class 'int'>)\n",
      "  end_time: 18 (<class 'int'>)\n",
      "  confidence: 0.8151801824569702 (<class 'numpy.float64'>)\n",
      "  session: t15.2023.08.11 (<class 'str'>)\n",
      "  block_num: 2 (<class 'numpy.int64'>)\n",
      "  trial_num: 0 (<class 'numpy.int64'>)\n",
      "  corpus: 50-Word (<class 'str'>)\n",
      "\n",
      "=== 字段统计分析 ===\n",
      "start_time: 范围 [17, 195], 平均 56.3\n",
      "end_time: 范围 [18, 195], 平均 57.3\n",
      "confidence: 范围 [0.815, 1.000], 平均 0.975\n",
      "session: 1 个不同session - ['t15.2023.08.11']\n",
      "trial_num: 6 个不同trial - 范围 0 到 7\n",
      "corpus: 1 个不同corpus - ['50-Word']\n",
      "\n",
      "=== Session分布分析 ===\n",
      "总session数: 45\n",
      "t15.2023.08.11:\n",
      "  Segments: 4771\n",
      "  Trials:  50\n",
      "  Phonemes: 34\n",
      "  Corpus: ['50-Word']\n",
      "t15.2023.08.13:\n",
      "  Segments: 8607\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard', '50-Word']\n",
      "t15.2023.08.18:\n",
      "  Segments: 5281\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.08.20:\n",
      "  Segments: 7333\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.08.25:\n",
      "  Segments: 2319\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.08.27:\n",
      "  Segments: 4064\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.09.01:\n",
      "  Segments: 7875\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.09.03:\n",
      "  Segments: 8962\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.09.24:\n",
      "  Segments: 6282\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard', '50-Word']\n",
      "t15.2023.09.29:\n",
      "  Segments: 4035\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.10.01:\n",
      "  Segments: 5869\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.10.06:\n",
      "  Segments: 4510\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard', '50-Word']\n",
      "t15.2023.10.08:\n",
      "  Segments: 7583\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.10.13:\n",
      "  Segments: 3903\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.10.15:\n",
      "  Segments: 6246\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.10.20:\n",
      "  Segments: 2584\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2023.10.22:\n",
      "  Segments: 4172\n",
      "  Trials:  49\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words', 'Switchboard']\n",
      "t15.2023.11.03:\n",
      "  Segments: 3734\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.11.04:\n",
      "  Segments: 1732\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.11.17:\n",
      "  Segments: 2443\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.11.19:\n",
      "  Segments: 1358\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.11.26:\n",
      "  Segments: 5678\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.12.03:\n",
      "  Segments: 6631\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.12.08:\n",
      "  Segments: 6024\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.12.10:\n",
      "  Segments: 3732\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.12.17:\n",
      "  Segments: 3976\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2023.12.29:\n",
      "  Segments: 5764\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Freq words']\n",
      "t15.2024.02.25:\n",
      "  Segments: 5424\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.03.03:\n",
      "  Segments: 4121\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard', '50-Word']\n",
      "t15.2024.03.08:\n",
      "  Segments: 5090\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.03.15:\n",
      "  Segments: 7502\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.03.17:\n",
      "  Segments: 7940\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.04.25:\n",
      "  Segments: 6605\n",
      "  Trials:  50\n",
      "  Phonemes: 39\n",
      "  Corpus: ['Switchboard', '50-Word']\n",
      "t15.2024.04.28:\n",
      "  Segments: 2575\n",
      "  Trials:  50\n",
      "  Phonemes: 34\n",
      "  Corpus: ['50-Word']\n",
      "t15.2024.05.10:\n",
      "  Segments: 3012\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.06.14:\n",
      "  Segments: 2524\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.07.19:\n",
      "  Segments: 5249\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.07.21:\n",
      "  Segments: 4687\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2024.07.28:\n",
      "  Segments: 4603\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2025.01.10:\n",
      "  Segments: 3078\n",
      "  Trials:  49\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2025.01.12:\n",
      "  Segments: 4819\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2025.03.14:\n",
      "  Segments: 1811\n",
      "  Trials:  25\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2025.03.16:\n",
      "  Segments: 2812\n",
      "  Trials:  49\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2025.03.30:\n",
      "  Segments: 4856\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "t15.2025.04.13:\n",
      "  Segments: 2042\n",
      "  Trials:  50\n",
      "  Phonemes: 40\n",
      "  Corpus: ['Switchboard']\n",
      "\n",
      "=== 加载最新的CTC Results ===\n",
      "文件: ctc_results_20251009_202457.pkl\n",
      "数据类型: <class 'list'>\n",
      "条目数量: 4\n",
      "\n",
      "第一个CTC结果结构:\n",
      "  session: t15.2023.08.11 (<class 'str'>)\n",
      "  input_layer: 0 (<class 'int'>)\n",
      "  trial_idx: 0 (<class 'int'>)\n",
      "  block_num: 2 (<class 'numpy.int64'>)\n",
      "  trial_num: 0 (<class 'numpy.int64'>)\n",
      "  corpus: 50-Word (<class 'str'>)\n",
      "  original_sequence: <class 'numpy.ndarray'> shape=(500,) dtype=int32\n",
      "  sentence_label: Bring it closer. (<class 'str'>)\n",
      "  n_time_steps: 321 (<class 'numpy.int64'>)\n",
      "  predicted_phonemes: <class 'list'> length=14\n",
      "    第一个元素: B (<class 'str'>)\n",
      "  ctc_score: -409.8164916324466 (<class 'float'>)\n",
      "  alignment_info: <class 'list'> length=14\n",
      "    第一个元素: ('B', 17, 18, np.float64(0.8151801824569702)) (<class 'tuple'>)\n",
      "\n",
      "前5个CTC结果的共同字段: ['original_sequence', 'trial_idx', 'sentence_label', 'block_num', 'predicted_phonemes', 'corpus', 'input_layer', 'n_time_steps', 'alignment_info', 'session', 'trial_num', 'ctc_score']\n",
      "\n",
      "数组字段形状分布:\n",
      "  original_sequence: 1 种不同形状\n",
      "    (500,): 4 次\n",
      "\n",
      "=== 变量总结 ===\n",
      "以下变量已创建，可供后续使用:\n",
      "- pkl_files_info: 所有pkl文件信息列表\n",
      "- phoneme_data: 音素数据字典\n",
      "- phoneme_counts: 各音素的segment数量统计\n",
      "- sorted_phonemes: 按数量排序的音素列表\n",
      "- sample_segments: 示例segment列表\n",
      "- session_summary: session统计信息字典\n",
      "- ctc_data: CTC结果数据列表\n",
      "\n",
      "可以直接使用这些变量进行进一步分析！\n"
     ]
    }
   ],
   "source": [
    "# =============================================================================\n",
    "# 数据结构展示代码 - 保留所有变量供后续调用\n",
    "# =============================================================================\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict, Counter\n",
    "\n",
    "# 设置数据目录\n",
    "data_dir = Path(\"../phoneme_segmented_data\")\n",
    "print(f\"数据目录: {data_dir.absolute()}\")\n",
    "print(f\"目录是否存在: {data_dir.exists()}\")\n",
    "\n",
    "# 获取所有pkl文件信息\n",
    "pkl_files = list(data_dir.glob(\"*.pkl\")) if data_dir.exists() else []\n",
    "pkl_files_info = []\n",
    "\n",
    "print(f\"\\n=== 发现的PKL文件 ===\")\n",
    "for pkl_file in pkl_files:\n",
    "    size_mb = pkl_file.stat().st_size / 1024 / 1024\n",
    "    mtime = pkl_file.stat().st_mtime\n",
    "    pkl_files_info.append({\n",
    "        'path': pkl_file,\n",
    "        'name': pkl_file.name,\n",
    "        'size_mb': size_mb,\n",
    "        'mtime': mtime\n",
    "    })\n",
    "    print(f\"{pkl_file.name}: {size_mb:.1f} MB\")\n",
    "\n",
    "# 按修改时间排序，获取最新文件\n",
    "pkl_files_info.sort(key=lambda x: x['mtime'], reverse=True)\n",
    "\n",
    "# =============================================================================\n",
    "# 加载和分析 Phoneme Dataset\n",
    "# =============================================================================\n",
    "\n",
    "phoneme_files = [f for f in pkl_files_info if f['name'].startswith(\"phoneme_dataset_\")]\n",
    "if phoneme_files:\n",
    "    latest_phoneme_file = phoneme_files[0]['path']\n",
    "    print(f\"\\n=== 加载最新的Phoneme Dataset ===\")\n",
    "    print(f\"文件: {latest_phoneme_file.name}\")\n",
    "    \n",
    "    # 直接加载数据，不使用函数封装\n",
    "    with open(latest_phoneme_file, 'rb') as f:\n",
    "        phoneme_data = pickle.load(f)\n",
    "    \n",
    "    # 基本信息\n",
    "    phoneme_data_type = type(phoneme_data)\n",
    "    phoneme_keys = list(phoneme_data.keys()) if isinstance(phoneme_data, dict) else None\n",
    "    phoneme_keys_count = len(phoneme_keys) if phoneme_keys else 0\n",
    "    \n",
    "    print(f\"数据类型: {phoneme_data_type}\")\n",
    "    print(f\"音素数量: {phoneme_keys_count}\")\n",
    "    \n",
    "    if isinstance(phoneme_data, dict):\n",
    "        # 统计每个音素的segment数量\n",
    "        phoneme_counts = {k: len(v) for k, v in phoneme_data.items()}\n",
    "        total_segments = sum(phoneme_counts.values())\n",
    "        \n",
    "        print(f\"总segment数量: {total_segments}\")\n",
    "        \n",
    "        # 按数量排序的音素列表\n",
    "        sorted_phonemes = sorted(phoneme_counts.items(), key=lambda x: x[1], reverse=True)\n",
    "        \n",
    "        print(f\"\\n音素分布统计 (前15个):\")\n",
    "        for i, (phoneme, count) in enumerate(sorted_phonemes[:15]):\n",
    "            print(f\"{i+1:2d}. '{phoneme}': {count:4d} segments ({count/total_segments*100:.1f}%)\")\n",
    "        \n",
    "        # 获取示例segments进行结构分析\n",
    "        sample_segments = []\n",
    "        sample_phonemes = []\n",
    "        \n",
    "        for phoneme, segments in list(phoneme_data.items())[:5]:  # 取前5个音素\n",
    "            for segment in segments[:3]:  # 每个音素取前3个segment\n",
    "                sample_segments.append(segment)\n",
    "                sample_phonemes.append(phoneme)\n",
    "        \n",
    "        # 分析segment结构\n",
    "        if sample_segments:\n",
    "            first_segment = sample_segments[0]\n",
    "            segment_keys = list(first_segment.keys())\n",
    "            \n",
    "            print(f\"\\n=== Segment结构分析 ===\")\n",
    "            print(f\"Segment字段数量: {len(segment_keys)}\")\n",
    "            print(f\"Segment字段: {segment_keys}\")\n",
    "            \n",
    "            print(f\"\\n第一个segment详细信息:\")\n",
    "            for key, value in first_segment.items():\n",
    "                value_type = type(value)\n",
    "                if isinstance(value, np.ndarray):\n",
    "                    print(f\"  {key}: {value_type} shape={value.shape} dtype={value.dtype}\")\n",
    "                    if value.size < 10:  # 小数组显示具体值\n",
    "                        print(f\"    值: {value}\")\n",
    "                    else:\n",
    "                        print(f\"    范围: [{value.min():.3f}, {value.max():.3f}]\")\n",
    "                else:\n",
    "                    print(f\"  {key}: {value} ({value_type})\")\n",
    "            \n",
    "            # 统计字段分布\n",
    "            field_stats = defaultdict(list)\n",
    "            for segment in sample_segments:\n",
    "                for key, value in segment.items():\n",
    "                    field_stats[key].append(value)\n",
    "            \n",
    "            print(f\"\\n=== 字段统计分析 ===\")\n",
    "            for key, values in field_stats.items():\n",
    "                if key == 'session':\n",
    "                    unique_sessions = list(set(values))\n",
    "                    print(f\"{key}: {len(unique_sessions)} 个不同session - {unique_sessions}\")\n",
    "                elif key == 'corpus':\n",
    "                    unique_corpus = list(set(values))\n",
    "                    print(f\"{key}: {len(unique_corpus)} 个不同corpus - {unique_corpus}\")\n",
    "                elif key in ['start_time', 'end_time']:\n",
    "                    times = [v for v in values if v is not None]\n",
    "                    if times:\n",
    "                        print(f\"{key}: 范围 [{min(times)}, {max(times)}], 平均 {np.mean(times):.1f}\")\n",
    "                elif key == 'confidence':\n",
    "                    confs = [v for v in values if v is not None and v > 0]\n",
    "                    if confs:\n",
    "                        print(f\"{key}: 范围 [{min(confs):.3f}, {max(confs):.3f}], 平均 {np.mean(confs):.3f}\")\n",
    "                elif key == 'trial_num':\n",
    "                    trials = list(set(values))\n",
    "                    print(f\"{key}: {len(trials)} 个不同trial - 范围 {min(trials)} 到 {max(trials)}\")\n",
    "        \n",
    "        # Session分布分析\n",
    "        session_stats = defaultdict(lambda: {'segments': 0, 'trials': set(), 'phonemes': set(), 'corpus': set()})\n",
    "        \n",
    "        for phoneme, segments in phoneme_data.items():\n",
    "            for segment in segments:\n",
    "                session = segment.get('session', 'unknown')\n",
    "                trial_num = segment.get('trial_num', -1)\n",
    "                corpus = segment.get('corpus', 'unknown')\n",
    "                \n",
    "                session_stats[session]['segments'] += 1\n",
    "                session_stats[session]['trials'].add(trial_num)\n",
    "                session_stats[session]['phonemes'].add(phoneme)\n",
    "                session_stats[session]['corpus'].add(corpus)\n",
    "        \n",
    "        # 转换为更易于访问的格式\n",
    "        session_summary = {}\n",
    "        for session, stats in session_stats.items():\n",
    "            session_summary[session] = {\n",
    "                'segments': stats['segments'],\n",
    "                'trials_count': len(stats['trials']),\n",
    "                'phonemes_count': len(stats['phonemes']),\n",
    "                'corpus_list': list(stats['corpus'])\n",
    "            }\n",
    "        \n",
    "        print(f\"\\n=== Session分布分析 ===\")\n",
    "        print(f\"总session数: {len(session_summary)}\")\n",
    "        for session in sorted(session_summary.keys()):\n",
    "            stats = session_summary[session]\n",
    "            print(f\"{session}:\")\n",
    "            print(f\"  Segments: {stats['segments']:4d}\")\n",
    "            print(f\"  Trials: {stats['trials_count']:3d}\")\n",
    "            print(f\"  Phonemes: {stats['phonemes_count']:2d}\")\n",
    "            print(f\"  Corpus: {stats['corpus_list']}\")\n",
    "\n",
    "else:\n",
    "    print(\"未找到phoneme dataset文件\")\n",
    "    phoneme_data = None\n",
    "    phoneme_counts = None\n",
    "    sorted_phonemes = None\n",
    "    sample_segments = None\n",
    "\n",
    "# =============================================================================\n",
    "# 加载和分析 CTC Results\n",
    "# =============================================================================\n",
    "\n",
    "ctc_files = [f for f in pkl_files_info if f['name'].startswith(\"ctc_results_\")]\n",
    "if ctc_files:\n",
    "    latest_ctc_file = ctc_files[0]['path']\n",
    "    print(f\"\\n=== 加载最新的CTC Results ===\")\n",
    "    print(f\"文件: {latest_ctc_file.name}\")\n",
    "    \n",
    "    # 直接加载数据\n",
    "    with open(latest_ctc_file, 'rb') as f:\n",
    "        ctc_data = pickle.load(f)\n",
    "    \n",
    "    ctc_data_type = type(ctc_data)\n",
    "    ctc_length = len(ctc_data) if hasattr(ctc_data, '__len__') else 0\n",
    "    \n",
    "    print(f\"数据类型: {ctc_data_type}\")\n",
    "    print(f\"条目数量: {ctc_length}\")\n",
    "    \n",
    "    if isinstance(ctc_data, list) and len(ctc_data) > 0:\n",
    "        first_ctc = ctc_data[0]\n",
    "        ctc_keys = list(first_ctc.keys()) if isinstance(first_ctc, dict) else None\n",
    "        \n",
    "        print(f\"\\n第一个CTC结果结构:\")\n",
    "        if ctc_keys:\n",
    "            for key, value in first_ctc.items():\n",
    "                value_type = type(value)\n",
    "                if isinstance(value, np.ndarray):\n",
    "                    print(f\"  {key}: {value_type} shape={value.shape} dtype={value.dtype}\")\n",
    "                elif isinstance(value, list):\n",
    "                    print(f\"  {key}: {value_type} length={len(value)}\")\n",
    "                    if len(value) > 0:\n",
    "                        print(f\"    第一个元素: {value[0]} ({type(value[0])})\")\n",
    "                else:\n",
    "                    print(f\"  {key}: {value} ({value_type})\")\n",
    "        \n",
    "        # 分析前几个CTC结果的共同字段\n",
    "        if len(ctc_data) > 1:\n",
    "            common_keys = set(ctc_data[0].keys())\n",
    "            for i in range(1, min(5, len(ctc_data))):\n",
    "                common_keys &= set(ctc_data[i].keys())\n",
    "            \n",
    "            print(f\"\\n前5个CTC结果的共同字段: {list(common_keys)}\")\n",
    "            \n",
    "            # 统计数组形状分布\n",
    "            shape_stats = defaultdict(list)\n",
    "            for i in range(min(10, len(ctc_data))):\n",
    "                for key, value in ctc_data[i].items():\n",
    "                    if isinstance(value, np.ndarray):\n",
    "                        shape_stats[key].append(value.shape)\n",
    "            \n",
    "            print(f\"\\n数组字段形状分布:\")\n",
    "            for key, shapes in shape_stats.items():\n",
    "                unique_shapes = list(set(shapes))\n",
    "                print(f\"  {key}: {len(unique_shapes)} 种不同形状\")\n",
    "                for shape in unique_shapes[:5]:  # 显示前5种形状\n",
    "                    count = shapes.count(shape)\n",
    "                    print(f\"    {shape}: {count} 次\")\n",
    "\n",
    "else:\n",
    "    print(\"未找到CTC results文件\")\n",
    "    ctc_data = None\n",
    "\n",
    "print(f\"\\n=== 变量总结 ===\")\n",
    "print(\"以下变量已创建，可供后续使用:\")\n",
    "print(\"- pkl_files_info: 所有pkl文件信息列表\")\n",
    "print(\"- phoneme_data: 音素数据字典\")\n",
    "print(\"- phoneme_counts: 各音素的segment数量统计\")\n",
    "print(\"- sorted_phonemes: 按数量排序的音素列表\")\n",
    "print(\"- sample_segments: 示例segment列表\")\n",
    "print(\"- session_summary: session统计信息字典\")\n",
    "print(\"- ctc_data: CTC结果数据列表\")\n",
    "print(\"\\n可以直接使用这些变量进行进一步分析！\")"
   ]
  }
 ],
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  "language_info": {
   "name": "python"
  }
 },
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 "nbformat_minor": 5
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