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184
plot_spectrum_by_parameter.py
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184
plot_spectrum_by_parameter.py
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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from pathlib import Path
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
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plt.rcParams['axes.unicode_minus'] = False
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def load_and_plot_spectrum_by_parameters():
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"""
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加载数据并为每个水质参数绘制光谱曲线图
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"""
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try:
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# 数据文件路径
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data_file = Path(r"E:\code\WQ\yaobao925\spectral.csv")
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if not data_file.exists():
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print(f"错误：数据文件不存在 - {data_file}")
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return
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# 读取数据
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print("正在加载数据...")
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data = pd.read_csv(data_file)
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print(f"数据形状: {data.shape}")
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print(f"列名: {list(data.columns[:15])}...") # 显示前15个列名
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# 找到光谱数据的起始列（通常是数字列名）
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spectrum_start_idx = None
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for i, col in enumerate(data.columns):
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try:
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float(col)
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spectrum_start_idx = i
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break
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except ValueError:
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continue
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if spectrum_start_idx is None:
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print("错误：未找到光谱数据列")
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return
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print(f"光谱数据从第 {spectrum_start_idx + 1} 列开始")
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# 分离水质参数和光谱数据
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water_quality_data = data.iloc[:, :spectrum_start_idx]
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spectrum_data = data.iloc[:, spectrum_start_idx:]
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# 获取波长信息
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try:
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# 尝试直接转换为浮点数
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wavelengths = spectrum_data.columns.astype(float)
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except ValueError:
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# 如果包含字母，提取数字部分
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import re
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wavelengths = []
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for col in spectrum_data.columns:
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# 提取数字部分
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numbers = re.findall(r'\d+\.?\d*', str(col))
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if numbers:
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wavelengths.append(float(numbers[0]))
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else:
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# 如果没有数字，使用列索引
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wavelengths.append(float(len(wavelengths)))
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wavelengths = np.array(wavelengths)
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print(f"波长范围: {wavelengths.min():.1f} - {wavelengths.max():.1f} nm")
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print(f"光谱数据形状: {spectrum_data.shape}")
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print(f"水质参数: {list(water_quality_data.columns)}")
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# 过滤波长范围到374-1011nm
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wavelength_mask = (wavelengths >= 374) & (wavelengths <= 1011)
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filtered_wavelengths = wavelengths[wavelength_mask]
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filtered_spectrum_data = spectrum_data.iloc[:, wavelength_mask]
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print(f"过滤后波长范围: {filtered_wavelengths.min():.1f} - {filtered_wavelengths.max():.1f} nm")
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print(f"过滤后光谱数据形状: {filtered_spectrum_data.shape}")
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# 创建输出目录
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output_dir = Path(r'E:\code\WQ\yaobao925\plot')
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output_dir.mkdir(exist_ok=True)
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# 为每个水质参数绘制光谱图
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for param_idx, parameter_name in enumerate(water_quality_data.columns):
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print(f"\n[{param_idx+1}/{len(water_quality_data.columns)}] 处理参数: {parameter_name}")
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# 获取当前参数的数据
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parameter_values = water_quality_data[parameter_name]
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# 过滤掉空值
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valid_mask = ~parameter_values.isna()
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if valid_mask.sum() == 0:
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print(f"参数 '{parameter_name}' 没有有效数据，跳过")
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continue
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valid_param_values = parameter_values[valid_mask]
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valid_spectrum_data = filtered_spectrum_data[valid_mask]
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print(f"有效样本数: {len(valid_param_values)}")
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# 创建图形和轴
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fig, ax = plt.subplots(figsize=(12, 8))
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# 归一化参数值到[0,1]范围，用于颜色映射
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param_min = valid_param_values.min()
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param_max = valid_param_values.max()
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if param_max == param_min:
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# 如果所有值相同，使用中等颜色
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normalized_values = np.full(len(valid_param_values), 0.5)
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else:
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normalized_values = ((valid_param_values - param_min) / (param_max - param_min)).values
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# 创建蓝红颜色映射（蓝色到红色）
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colormap = plt.cm.coolwarm # 蓝色(低值)到红色(高值)
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# 绘制每条光谱曲线
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for i, (idx, spectrum) in enumerate(valid_spectrum_data.iterrows()):
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# 处理光谱数据中的空值
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spectrum_values = pd.Series(spectrum.values).fillna(0).values
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# 根据参数值确定颜色
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color = colormap(normalized_values[i])
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alpha = 0.6 if len(valid_param_values) > 50 else 0.8 # 样本多时降低透明度
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ax.plot(filtered_wavelengths, spectrum_values, color=color, alpha=alpha, linewidth=0.8)
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# 设置图形属性
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ax.set_xlabel('波长 (nm)', fontsize=12)
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ax.set_ylabel('光谱强度', fontsize=12)
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ax.set_title(f'{parameter_name} 光谱曲线图\n参数范围: {param_min:.4f} - {param_max:.4f}',
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fontsize=14, fontweight='bold')
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# 设置坐标轴范围，限制在374-1011nm
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ax.set_xlim(374, 1011)
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# 添加网格
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ax.grid(True, alpha=0.3)
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# 创建颜色条
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sm = plt.cm.ScalarMappable(cmap=colormap,
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norm=plt.Normalize(vmin=param_min, vmax=param_max))
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sm.set_array([])
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cbar = plt.colorbar(sm, ax=ax, shrink=0.8)
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cbar.set_label(f'{parameter_name} 数值', rotation=270, labelpad=20, fontsize=12)
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# 添加统计信息文本框
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stats_text = f'样本数: {len(valid_param_values)}\n'
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stats_text += f'均值: {valid_param_values.mean():.4f}\n'
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stats_text += f'标准差: {valid_param_values.std():.4f}'
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ax.text(0.02, 0.98, stats_text, transform=ax.transAxes,
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verticalalignment='top', bbox=dict(boxstyle='round',
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facecolor='wheat', alpha=0.8), fontsize=10)
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# 优化布局
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plt.tight_layout()
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# 保存图片
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# 清理参数名称，用于文件名
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safe_param_name = "".join(c for c in parameter_name if c.isalnum() or c in ('-', '_', '.')).rstrip()
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output_file = output_dir / f"{safe_param_name}_spectrum.png"
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plt.savefig(output_file, dpi=300, bbox_inches='tight')
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plt.close() # 关闭图形释放内存
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print(f"图片已保存到: {output_file}")
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print(f"\n{'='*80}")
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print(f"所有光谱图绘制完成！")
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print(f"输出目录: {output_dir}")
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print(f"{'='*80}")
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except Exception as e:
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print(f"处理过程中出现错误: {str(e)}")
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import traceback
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traceback.print_exc()
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def main():
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"""主函数"""
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load_and_plot_spectrum_by_parameters()
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if __name__ == "__main__":
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main()
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