Word表格原来也能求得平均值(转)

import pandas as pd import matplotlib.pyplot as plt from astropy.time import Time import numpy as np # 文件路径 file_path = '20211229_FY_2G_SX_L2.txt' # 常量定义 h = 6.626e-34 # 普朗克常数 (J·s) c = 3.0e8 # 光速 (m/s) eV_to_J = 1.602e-19 # 1 eV 转换为 J # 计算平均能量（单位：J） E_SX1 = ((1.8 + 12.9) / 2) * 1000 * eV_to_J E_SX2 = ((3.1 + 26.1) / 2) * 1000 * eV_to_J # 读取文件，指定编码并跳过非数据行 data = pd.read_csv( file_path, delim_whitespace=True, comment='#', names=["Date", "Time", "SX1", "SX2"], skiprows=8, encoding='ISO-8859-1' ) # 合并日期和时间列 data['Datetime'] = pd.to_datetime(data['Date'] + ' ' + data['Time']) # 转换 SX1 和 SX2 数据为浮点数 data['SX1'] = pd.to_numeric(data['SX1'], errors='coerce') data['SX2'] = pd.to_numeric(data['SX2'], errors='coerce') # 计算光子数密度 data['Photon_SX1'] = data['SX1'] / E_SX1 data['Photon_SX2'] = data['SX2'] / E_SX2 # 将常规的日期时间转换为儒略时间 data['Julian_Time'] = Time(data['Datetime']).jd # 设置合适的 bin 宽度 bin_width = 0.1 # 设置 bin 宽度为 0.1 天（根据数据选择合适的值） # 将时间数据分为 bins bins = np.arange(min(data['Julian_Time']), max(data['Julian_Time']), bin_width) # 计算每个 bin 中的光子数密度平均值 photon_SX1_binned = np.histogram(data['Julian_Time'], bins=bins, weights=data['Photon_SX1'])[0] / np.histogram(data['Julian_Time'], bins=bins)[0] photon_SX2_binned = np.histogram(data['Julian_Time'], bins=bins, weights=data['Photon_SX2'])[0] / np.histogram(data['Julian_Time'], bins=bins)[0] # 获取 bin 的中间值 bin_centers = 0.5 * (bins[:-1] + bins[1:]) # 归一化数据（将数据除以最大值） photon_SX1_binned /= np.max(photon_SX1_binned) photon_SX2_binned /= np.max(photon_SX2_binned) # 绘制光子数变化曲线 plt.figure(figsize=(12, 6)) plt.plot(bin_centers, photon_SX1_binned, label='Normalized Photon SX1 (PC)', color='black', alpha=0.7) plt.plot(bin_centers, photon_SX2_binned, label='Normalized Photon SX2 (PC)', color='red', alpha=0.7) plt.title('Normalized (FY-2G)', fontsize=14) plt.xlabel(' (JD)', fontsize=12) plt.ylabel('Normalized Optical Word Density', fontsize=12) plt.legend() plt.grid(alpha=0.3) plt.tight_layout() plt.show()