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