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Chla-time-plot.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import numpy as np
+from datetime import datetime
+
+# 设置叶绿素a阈值
+CHLOROPHYLL_THRESHOLD = 100.0  # 大于此值的叶绿素a数据将被剔除
+
+# 1. 读取CSV文件
+try:
+    # 尝试自动检测文件格式
+    df = pd.read_csv(
+        r"D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\不去耀斑index.csv",
+        header=None, skip_blank_lines=True, on_bad_lines='warn')
+
+    # 检查列数，可能有多余列
+    if df.shape[1] > 2:
+        print(f"警告：检测到 {df.shape[1]} 列，但预期只有2列。尝试提取前两列。")
+        df = df.iloc[:, :2]  # 只保留前两列
+
+    # 重命名列
+    df.columns = ['Time', 'Chl_a']
+
+    # 打印前几行用于调试
+    print("原始数据预览:")
+    print(df.head())
+
+    # 检查是否有标题行
+    if not isinstance(df['Time'].iloc[0], str) or any(char.isalpha() for char in str(df['Time'].iloc[0])):
+        print("检测到可能的标题行，跳过第一行")
+        df = pd.read_csv(
+            r"D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\不去耀斑index.csv",
+            header=None, skiprows=1, skip_blank_lines=True)
+        df = df.iloc[:, :2]  # 只保留前两列
+        df.columns = ['Time', 'Chl_a']
+        print("处理后数据预览:")
+        print(df.head())
+
+except Exception as e:
+    print(f"读取文件时出错: {e}")
+    exit()
+
+# 2. 转换时间格式
+try:
+    # 尝试解析时间
+    df['Time'] = pd.to_datetime(df['Time'], errors='coerce', format='%Y/%m/%d %H:%M')
+
+    # 检查是否有解析失败的时间
+    if df['Time'].isna().any():
+        print("部分时间解析失败，尝试其他格式...")
+        # 尝试其他常见格式
+        df['Time'] = pd.to_datetime(df['Time'], errors='coerce', format='%Y-%m-%d %H:%M')
+        df['Time'] = pd.to_datetime(df['Time'], errors='coerce', format='%Y/%m/%d %H:%M:%S')
+        df['Time'] = pd.to_datetime(df['Time'], errors='coerce', format='%Y-%m-%d %H:%M:%S')
+
+        # 如果仍有失败，尝试自动推断
+        if df['Time'].isna().any():
+            print("使用混合格式解析时间")
+            df['Time'] = pd.to_datetime(df['Time'], errors='coerce', format='mixed')
+
+    # 删除无法解析时间的行
+    na_count = df['Time'].isna().sum()
+    if na_count > 0:
+        print(f"警告: 删除了 {na_count} 行无法解析的时间数据")
+        df = df.dropna(subset=['Time'])
+
+    # 提取日期和时间
+    df['Date'] = df['Time'].dt.date
+    df['Time_of_day'] = df['Time'].dt.time
+
+except Exception as e:
+    print(f"时间解析错误: {e}")
+    exit()
+
+# 3. 确保叶绿素a列是数值型并应用阈值过滤
+try:
+    df['Chl_a'] = pd.to_numeric(df['Chl_a'], errors='coerce')
+
+    # 记录过滤前的数据量
+    original_count = len(df)
+
+    # 应用阈值过滤
+    above_threshold = df[df['Chl_a'] > CHLOROPHYLL_THRESHOLD]
+    if not above_threshold.empty:
+        print(f"警告: 发现 {len(above_threshold)} 行叶绿素a值超过阈值 {CHLOROPHYLL_THRESHOLD} μg/L，将被剔除")
+        print(above_threshold)
+        df = df[df['Chl_a'] <= CHLOROPHYLL_THRESHOLD]
+
+    # 删除无效值
+    na_count = df['Chl_a'].isna().sum()
+    if na_count > 0:
+        print(f"警告: 删除了 {na_count} 行无效的叶绿素值")
+        df = df.dropna(subset=['Chl_a'])
+
+    # 报告过滤结果
+    filtered_count = len(df)
+    print(f"数据过滤结果: 原始 {original_count} 行 → 保留 {filtered_count} 行")
+
+except Exception as e:
+    print(f"叶绿素值转换错误: {e}")
+    exit()
+
+# 4. 按日期分组计算统计量
+daily_stats = df.groupby('Date')['Chl_a'].agg(['mean', 'std', 'count']).reset_index()
+daily_stats.columns = ['Date', 'Daily_Mean', 'Std_Dev', 'Sample_Count']
+
+# 获取唯一日期
+unique_dates = sorted(df['Date'].unique())
+
+# 5. 创建绘图 - 箱型图
+fig1, ax1 = plt.subplots(figsize=(14, 6))
+
+# 绘制箱型图
+ax1.boxplot([df[df['Date'] == date]['Chl_a'] for date in unique_dates], labels=[str(date) for date in unique_dates])
+
+# 添加平均线
+ax1.plot(range(1, len(unique_dates) + 1), daily_stats['Daily_Mean'], color='red', linestyle='--', label='Daily Mean')
+
+ax1.set_title('Daily Chlorophyll-a Concentration with Box Plot', fontsize=14)
+ax1.set_ylabel('Chl-a (μg/L)', fontsize=12)
+ax1.grid(axis='y', linestyle='--', alpha=0.7)
+
+# 添加图例
+ax1.legend()
+
+plt.tight_layout()
+plt.savefig(
+    r'D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\plot\daily_chlorophyll_boxplot.png',
+    dpi=300, bbox_inches='tight')
+
+#
+# # 6. 创建每日时间序列图 - 每个日期一个子图
+# fig2, axs = plt.subplots(len(unique_dates), 1, figsize=(14, 4 * len(unique_dates)), sharex=True)
+#
+# if len(unique_dates) == 1:
+#     axs = [axs]  # 确保单个子图时也能正确处理
+#
+# fig2.suptitle('Chlorophyll-a Concentration Variation Throughout the Day', fontsize=16, y=0.98)
+#
+# for i, date in enumerate(unique_dates):
+#     daily_data = df[df['Date'] == date].sort_values('Time')
+#     ax = axs[i]
+#
+#     # 使用真实时间作横轴
+#     ax.plot(daily_data['Time'], daily_data['Chl_a'], marker='', linestyle='-', color='royalblue',
+#             label=f'Chl-a on {date}', alpha=0.8)
+#
+#     # 添加统计信息
+#     daily_mean = daily_data['Chl_a'].mean()
+#     daily_std = daily_data['Chl_a'].std()
+#     ax.axhline(y=daily_mean, color='g', linestyle='--', alpha=0.7)
+#     ax.axhspan(daily_mean - daily_std, daily_mean + daily_std, color='lightgreen', alpha=0.3)
+#
+#     ax.set_title(f"{date.strftime('%Y-%m-%d')} (n={len(daily_data)})", fontsize=12)
+#     ax.set_ylabel('Chl-a (μg/L)', fontsize=10)
+#     ax.grid(True, linestyle='--', alpha=0.7)
+#
+#     ax.legend(loc='upper left')
+#
+# # 自动优化时间刻度显示
+#     fig2.autofmt_xdate(rotation=45)
+#
+#     # 保存每个日期的图像，确保文件名唯一
+#     save_path = f'D:/WQ/zhanghuilai/hyperspectral-inversion/data/input/一代高光谱/2025-07-11_2025-07-21/plot/daily_chlorophyll_timeseries_{date.strftime("%Y-%m-%d")}.png'
+#     fig2.savefig(save_path, dpi=300, bbox_inches='tight')
+
+# 6. 为每个日期单独创建图像
+for date in unique_dates:
+    daily_data = df[df['Date'] == date].sort_values('Time')
+
+    fig, ax = plt.subplots(figsize=(14, 4))
+
+    # 使用真实时间作横轴
+    ax.plot(daily_data['Time'], daily_data['Chl_a'], marker='o', linestyle='-', color='royalblue',
+            label=f'Chl-a on {date}', alpha=0.8)
+
+    # 添加统计信息
+    daily_mean = daily_data['Chl_a'].mean()
+    daily_std = daily_data['Chl_a'].std()
+    ax.axhline(y=daily_mean, color='g', linestyle='--', alpha=0.7, label='Daily Mean')
+    ax.axhspan(daily_mean - daily_std, daily_mean + daily_std, color='lightgreen', alpha=0.3, label='±1 Std Dev')
+
+    ax.set_title(f"Chlorophyll-a Time Series on {date.strftime('%Y-%m-%d')} (n={len(daily_data)})", fontsize=13)
+    ax.set_xlabel("Time", fontsize=11)
+    ax.set_ylabel("Chl-a (μg/L)", fontsize=11)
+    ax.grid(True, linestyle='--', alpha=0.7)
+
+    # 设置时间格式优化显示
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
+    fig.autofmt_xdate(rotation=45)
+
+    ax.legend(loc='upper left')
+
+    # 保存图像
+    save_path = f'D:/WQ/zhanghuilai/hyperspectral-inversion/data/input/一代高光谱/2025-07-11_2025-07-21/plot/daily_chlorophyll_timeseries_{date.strftime("%Y-%m-%d")}.png'
+    fig.savefig(save_path, dpi=300, bbox_inches='tight')
+    plt.close(fig)  # 防止内存过多占用
+
+# 打印数据统计摘要
+print("\n数据统计摘要:")
+print(f"叶绿素a阈值: {CHLOROPHYLL_THRESHOLD} μg/L")
+print(f"总样本数: {len(df)}")
+print(f"监测天数: {len(unique_dates)}")
+print(f"平均每日样本数: {len(df) / len(unique_dates):.1f}")
+print(f"叶绿素a总体均值: {df['Chl_a'].mean():.2f} ± {df['Chl_a'].std():.2f} μg/L")
+print(f"叶绿素a最小值: {df['Chl_a'].min():.2f} μg/L")
+print(f"叶绿素a最大值: {df['Chl_a'].max():.2f} μg/L")
+print("\n每日统计:")
+print(daily_stats)

chl-a含量图.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib import font_manager
+
+# 1. 设置中文字体（SimHei 或自行替换路径）
+font_path = 'C:/Windows/Fonts/simhei.ttf'  # 适用于 Windows
+zh_font = font_manager.FontProperties(fname=font_path)
+plt.rcParams['font.sans-serif'] = [zh_font.get_name()]
+plt.rcParams['axes.unicode_minus'] = False  # 解决负号乱码
+
+# 2. 读取 CSV 数据
+file_path = r"D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\output\chla画图.csv" # ← 替换为你的实际文件路径
+df = pd.read_csv(file_path)
+
+# 3. 预处理数据
+df.columns = ['timestamp', 'chl_a']
+df['timestamp'] = pd.to_datetime(df['timestamp'])
+df['date'] = df['timestamp'].dt.date  # 只保留年月日
+
+# 4. 计算每日平均和标准差
+daily_stats = df.groupby('date')['chl_a'].agg(['mean', 'std']).reset_index()
+
+# 5. 绘图
+fig, axs = plt.subplots(2, 1, figsize=(10, 8))
+
+# 图 1：每日 chl-a 浓度柱状图（含误差线）
+axs[0].bar(daily_stats['date'], daily_stats['mean'], yerr=daily_stats['std'],
+           capsize=4, color='skyblue', edgecolor='black')
+axs[0].set_title('每日 chl-a 浓度均值（含标准差）', fontproperties=zh_font)
+axs[0].set_xlabel('日期', fontproperties=zh_font)
+axs[0].set_ylabel('chl-a 浓度', fontproperties=zh_font)
+axs[0].tick_params(axis='x', rotation=45)
+
+# 图 2：chl-a 浓度分布直方图
+axs[1].hist(df['chl_a'], bins=30, color='lightgreen', edgecolor='black')
+axs[1].set_title('chl-a 浓度分布直方图', fontproperties=zh_font)
+axs[1].set_xlabel('chl-a 浓度', fontproperties=zh_font)
+axs[1].set_ylabel('频数', fontproperties=zh_font)
+
+# 6. 调整布局并保存图像
+plt.tight_layout()
+plt.savefig('D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\output\chl_a_图表.png', dpi=300, bbox_inches='tight')  # 高分辨率保存
+plt.show()

chla_distintplot.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def plot_spectra(csv_file_path, chla_file_path):
+    """
+    读取CSV文件并根据chl-a指数是否为0将光谱分为两部分绘制
+    用红线标出650.88nm, 670.41nm, 706.54nm波长位置
+    :param csv_file_path: 原始光谱数据CSV文件路径
+    :param chla_file_path: 包含chl-a指数的CSV文件路径
+    """
+    # 读取原始光谱数据
+    df_spectral = pd.read_csv(csv_file_path)
+    wavelengths = df_spectral.columns[1:].astype(float)  # 假设从第2列开始是光谱数据
+
+    # 读取包含chl-a指数的结果文件
+    df_chla = pd.read_csv(chla_file_path)
+
+    # 确保两个文件的行数匹配
+    if len(df_spectral) != len(df_chla):
+        raise ValueError("光谱数据文件和水质参数文件行数不匹配")
+
+    # 分离光谱数据
+    spectral_data = df_spectral.iloc[:, 1:].values.astype(float)
+
+    # 获取chl-a列
+    chla_values = df_chla['chl-a'].values
+
+    # 根据chl-a值创建分组
+    group1_indices = np.where(chla_values == 0)[0]  # chl-a=0的索引
+    group2_indices = np.where(chla_values != 0)[0]  # chl-a≠0的索引
+
+    # 创建两个图表
+    plt.figure(figsize=(15, 10))
+
+    # 定义要标记的波长位置
+    highlight_wavelengths = [650.88, 670.41, 706.54]
+    colors = ['red', 'green', 'blue']  # 为每个波长使用不同的颜色
+
+    # 第一组：chl-a=0
+    ax1 = plt.subplot(2, 1, 1)
+    if len(group1_indices) > 0:
+        group1_data = spectral_data[group1_indices, :]
+        for i in range(group1_data.shape[0]):
+            plt.plot(wavelengths, group1_data[i, :], linewidth=1, alpha=0.7)
+
+        mean_spectrum = np.mean(group1_data, axis=0)
+        plt.plot(wavelengths, mean_spectrum, 'k-', linewidth=2.5, label='Average Spectrum')
+
+        # 添加波长标记线
+        y_min, y_max = plt.ylim()
+        for i, wave in enumerate(highlight_wavelengths):
+            plt.axvline(x=wave, color=colors[i], linestyle='--', alpha=0.7,
+                        label=f'{wave}nm' if i == 0 else '')
+            plt.text(wave, y_max * 0.95, f'{wave}nm',
+                     fontsize=10, color=colors[i], ha='center', va='top')
+
+        plt.title(f'Spectral Data with chl-a = 0 (n={len(group1_indices)})')
+        plt.ylabel('Reflectance')
+        plt.legend()
+        plt.grid(True, linestyle='--', alpha=0.3)
+
+    # 第二组：chl-a≠0
+    ax2 = plt.subplot(2, 1, 2, sharex=ax1)  # 共享x轴范围
+    if len(group2_indices) > 0:
+        group2_data = spectral_data[group2_indices, :]
+        for i in range(group2_data.shape[0]):
+            plt.plot(wavelengths, group2_data[i, :], linewidth=1, alpha=0.7)
+
+        mean_spectrum = np.mean(group2_data, axis=0)
+        plt.plot(wavelengths, mean_spectrum, 'k-', linewidth=2.5, label='Average Spectrum')
+
+        # 添加波长标记线
+        y_min, y_max = plt.ylim()
+        for i, wave in enumerate(highlight_wavelengths):
+            plt.axvline(x=wave, color=colors[i], linestyle='--', alpha=0.7,
+                        label=f'{wave}nm' if i == 0 else '')
+            plt.text(wave, y_max * 0.95, f'{wave}nm',
+                     fontsize=10, color=colors[i], ha='center', va='top')
+
+        plt.title(f'Spectral Data with chl-a ≠ 0 (n={len(group2_indices)})')
+        plt.xlabel('Wavelength (nm)')
+        plt.ylabel('Reflectance')
+        plt.legend()
+        plt.grid(True, linestyle='--', alpha=0.3)
+
+    # 调整布局并保存
+    plt.tight_layout()
+    plt.savefig(
+        r'D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\plot\spectral_groups_highlighted.png',
+        dpi=300
+    )
+    plt.show()
+
+
+# 使用示例
+if __name__ == "__main__":
+    spectral_csv = r"D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\筛选.csv"
+    chla_csv = r"D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\不去耀斑index.csv"
+    plot_spectra(spectral_csv, chla_csv)

data_spectral.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+
+# 读取CSV文件
+file_path = r"D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\筛选.csv" # 替换为你的文件路径
+df = pd.read_csv(file_path)
+
+# 获取第一列名（时间戳列）
+timestamp_col = df.columns[0]
+
+# 解析时间戳列（注意格式：2025/7/7 15:03）
+df[timestamp_col] = pd.to_datetime(df[timestamp_col], format='%Y-%m-%d %H:%M:%S')
+
+# 提取日期列（用于分组）
+df['Date'] = df[timestamp_col].dt.date
+
+# 获取波长数据（第一行，跳过时间戳列）
+# 尝试将列名转换为浮点数，如果失败则保持原样
+wavelengths = []
+for col in df.columns[1:-1]:
+    try:
+        wavelengths.append(float(col))
+    except ValueError:
+        wavelengths.append(col)
+
+# 按日期分组计算平均光谱
+daily_avg = df.groupby('Date')[df.columns[1:-1]].mean()
+
+# 设置绘图参数
+plt.figure(figsize=(12, 8))
+plt.title('Daily Average Spectral Reflectance', fontsize=16)
+plt.xlabel('Wavelength (nm)', fontsize=14)
+plt.ylabel('Reflectance', fontsize=14)
+plt.grid(True, alpha=0.3)
+
+# 为每条曲线生成不同的颜色
+colors = plt.cm.viridis(np.linspace(0, 1, len(daily_avg)))
+
+# 绘制每条平均光谱曲线
+for i, (date, row) in enumerate(daily_avg.iterrows()):
+    # 将日期格式化为更易读的形式
+    formatted_date = date.strftime('%Y-%m-%d')
+
+    # 绘制光谱曲线
+    plt.plot(wavelengths, row.values,
+             label=formatted_date,
+             color=colors[i],
+             linewidth=2)
+
+# 添加图例
+plt.legend(title='Date', bbox_to_anchor=(1.05, 1), loc='upper left')
+
+# 优化布局
+plt.tight_layout()
+
+# 保存图像
+plt.savefig(r'D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\2025-07-11_2025-07-21\plot\daily_average_spectra.png', dpi=300, bbox_inches='tight')
+
+
+
+# 输出每日平均光谱数据到CSV
+# daily_avg.to_csv('D:\WQ\zhanghuilai\hyperspectral-inversion\data\input\一代高光谱\daily_average_spectra.csv')
+print("每日平均光谱数据已保存到 daily_average_spectra.csv")
+print("光谱图已保存到 daily_average_spectra.png")

exportDSData2csv.py

@@ -0,0 +1,238 @@
+import struct
+import pandas as pd
+import csv
+import argparse
+import sys
+
+
+def parse_metadata(hex_data):
+    """
+    解析元数据部分（前112个字符）
+    元数据结构：
+        type (1字节)        : 位置 0-1
+        direction (1字节)   : 位置 2-3
+        tuigan_stat (1字节) : 位置 4-5
+        year (1字节)        : 位置 6-7
+        month (1字节)       : 位置 8-9
+        day (1字节)         : 位置 10-11
+        hour (1字节)        : 位置 12-13
+        minute (1字节)      : 位置 14-15
+        second (1字节)      : 位置 16-17
+        NCa (1字节)         : 位置 18-19
+        Ncb (1字节)         : 位置 20-21
+        NCC (1字节)         : 位置 22-23
+        shutter_time (4字节): 位置 24-31
+        index (8字节)       : 位置 32-47
+        temperature (32字节): 位置 48-111 (8个float，每个4字节)
+    """
+    # 验证元数据长度
+    if len(hex_data) < 112:
+        raise ValueError(f"元数据长度不足112个字符，实际长度: {len(hex_data)}")
+
+    # 解析单字节整数 (u_int8_t)
+    def parse_u8(hex_str):
+        return int(hex_str, 16)
+
+    # 解析多字节整数 (小端序)
+    def parse_u32(hex_str):
+        return struct.unpack('<I', bytes.fromhex(hex_str))[0]
+
+    def parse_u64(hex_str):
+        return struct.unpack('<Q', bytes.fromhex(hex_str))[0]
+
+    # 解析浮点数 (小端序)
+    def parse_float(hex_str):
+        return struct.unpack('<f', bytes.fromhex(hex_str))[0]
+
+    # 按位置解析各个字段
+    metadata = {
+        'type': parse_u8(hex_data[0:2]),
+        'direction': parse_u8(hex_data[2:4]),
+        'tuigan_stat': parse_u8(hex_data[4:6]),
+        'year': parse_u8(hex_data[6:8]),
+        'month': parse_u8(hex_data[8:10]),
+        'day': parse_u8(hex_data[10:12]),
+        'hour': parse_u8(hex_data[12:14]),
+        'minute': parse_u8(hex_data[14:16]),
+        'second': parse_u8(hex_data[16:18]),
+        'NCa': parse_u8(hex_data[18:20]),
+        'Ncb': parse_u8(hex_data[20:22]),
+        'NCC': parse_u8(hex_data[22:24]),
+        'shutter_time': parse_u32(hex_data[24:32]),  # 4字节
+        'index': parse_u64(hex_data[32:48]),  # 8字节
+        'temperature': []
+    }
+
+    # 解析8个温度值 (每个4字节)
+    for i in range(8):
+        start = 48 + i * 8
+        end = start + 8
+        temp_val = parse_float(hex_data[start:end])
+        metadata['temperature'].append(temp_val)
+
+    return metadata
+
+
+def parse_spectral_data(hex_data):
+    """解析光谱数据部分 (2048个浮点数)"""
+    if len(hex_data) < 16384:
+        raise ValueError(f"光谱数据长度不足16384个字符，实际长度: {len(hex_data)}")
+
+    spectral = []
+    for i in range(2048):
+        start = i * 8
+        end = start + 8
+        chunk = hex_data[start:end]
+        try:
+            byte_data = bytes.fromhex(chunk)
+            float_val = struct.unpack('<f', byte_data)[0]
+            spectral.append(float_val)
+        except Exception as e:
+            spectral.append(f"Error: {str(e)}")
+
+    return spectral
+
+
+def parse_full_line(hex_line, row_index):
+    """
+    解析完整的一行数据
+    格式: 元数据(112字符) + 光谱数据(16384字符) = 16496字符
+    """
+    hex_line = hex_line.strip()
+
+    # 验证总长度
+    if len(hex_line) != 16496:
+        raise ValueError(f"行 {row_index + 1}: 数据长度应为16496字符，实际为{len(hex_line)}")
+
+    try:
+        # 解析元数据 (前112字符)
+        metadata = parse_metadata(hex_line[:112])
+
+        # 解析光谱数据 (后16384字符)
+        spectral = parse_spectral_data(hex_line[112:112 + 16384])
+
+        # 创建结果字典
+        result = {
+            'type': metadata['type'],
+            'direction': metadata['direction'],
+            'year': metadata['year'],
+            'month': metadata['month'],
+            'day': metadata['day'],
+            'hour': metadata['hour'],
+            'minute': metadata['minute'],
+            'second': metadata['second'],
+            'shutter_time': metadata['shutter_time'],
+            'index': metadata['index'],
+        }
+
+        # 添加温度列
+        for i, temp in enumerate(metadata['temperature']):
+            result[f'temp_{i}'] = temp
+
+        # 添加光谱列
+        for i, spec in enumerate(spectral):
+            result[f'spec_{i}'] = spec
+
+        return result
+
+    except Exception as e:
+        # 返回错误信息
+        return {
+            'row_index': row_index + 1,
+            'error': f"解析失败: {str(e)}"
+        }
+
+
+def calculate_wavelengths(a1, a2, a3, a4):
+    """计算2048个通道的波长值"""
+    wavelengths = []
+    for x in range(2048):
+        wl = ((a1 * x + a2) * x + a3) * x + a4
+        wavelengths.append(round(wl, 2))  # 四舍五入到小数点后两位
+    return wavelengths
+
+
+def process_csv_to_csv(input_csv_path, output_csv_path):
+    """
+    处理CSV文件并将解析结果保存为CSV
+    """
+    try:
+        # 读取CSV文件，跳过第一行标题行
+        df = pd.read_csv(input_csv_path, sep=',', encoding='utf-8', skiprows=1)
+        df = df.dropna(subset=['DL_Data_Total'])
+        df.columns = df.columns.str.strip()
+
+        if "DL_Data_Total" not in df.columns:
+            raise ValueError("找不到列：DL_Data_Total")
+
+        # 检查必需的系数列
+        required_coeffs = ['a1_dec', 'a2_dec', 'a3_dec', 'a4_dec']
+        missing = [col for col in required_coeffs if col not in df.columns]
+        if missing:
+            raise ValueError(f"缺少必需的系数列: {', '.join(missing)}")
+
+        # 从第三行（第一个数据行，索引位置2）获取系数
+        coeff_row = df.iloc[2]  # 第三行对应索引2
+        a1 = coeff_row['a1_dec']
+        a2 = coeff_row['a2_dec']
+        a3 = coeff_row['a3_dec']
+        a4 = coeff_row['a4_dec']
+
+        # 计算所有通道的波长
+        wavelengths = calculate_wavelengths(a1, a2, a3, a4)
+
+        hex_column = df["DL_Data_Total"].dropna()
+
+        # 准备结果列表
+        results = []
+
+        # 处理每一行数据（从第三行开始）
+        for idx in range(2, len(hex_column)):
+            hex_str = str(hex_column.iloc[idx])
+            result = parse_full_line(hex_str, idx)
+            results.append(result)
+
+        # 转换为DataFrame
+        result_df = pd.DataFrame(results)
+
+        # 添加时间戳
+        if "timestamp" in df.columns:
+            timestamp = df["timestamp"].iloc[2:len(results) + 2].reset_index(drop=True)
+            result_df.insert(0, "timestamp", timestamp)
+        else:
+            print("警告: 未找到timestamp列，跳过时间戳添加")
+
+        # 重命名光谱列：用波长值替换spec_X
+        spec_columns = [col for col in result_df.columns if col.startswith('spec_')]
+        if len(spec_columns) != 2048:
+            print(f"警告: 找到 {len(spec_columns)} 个光谱列，但需要2048列")
+        else:
+            # 按通道索引排序
+            spec_columns_sorted = sorted(spec_columns, key=lambda x: int(x.split('_')[1]))
+            # 创建列名映射 {旧列名: 新列名}
+            column_mapping = {old: f"{wl}" for old, wl in zip(spec_columns_sorted, wavelengths)}
+            result_df.rename(columns=column_mapping, inplace=True)
+
+        # 保存为CSV
+        result_df.to_csv(output_csv_path, index=False)
+        print(f"转换完成，结果已保存到：{output_csv_path}")
+        return result_df
+
+    except Exception as e:
+        print(f"处理过程中发生错误: {str(e)}", file=sys.stderr)
+        sys.exit(1)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='解析高光谱CSV数据')
+    parser.add_argument('--input', required=True, help='输入CSV文件路径')
+    parser.add_argument('--output', required=True, help='输出CSV文件路径')
+
+    args = parser.parse_args()
+
+    print(f"开始处理: {args.input}")
+    process_csv_to_csv(args.input, args.output)
+
+
+if __name__ == "__main__":
+    main()