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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)