420 lines
18 KiB
Python
420 lines
18 KiB
Python
# 重力波 一天cosmic重力波
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import os
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import time
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import numpy as np
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import pandas as pd
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from scipy.interpolate import interp1d
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from scipy.optimize import curve_fit
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import netCDF4 as nc
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import matplotlib.pyplot as plt
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from CONSTANT import DATA_BASEPATH
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# 模块1: 数据读取与预处理
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def read_and_preprocess_data(base_folder_path, day_num):
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"""
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读取指定路径下指定天数的相关数据文件，进行插值等预处理操作
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参数:
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base_folder_path (str): 基础文件夹路径
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day_num (int): 表示天数的序号（范围按代码中原本逻辑处理）
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返回:
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final_df (pd.DataFrame): 预处理后的最终数据框
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"""
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dfs = []
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# 构建当前文件夹的路径
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folder_name = f"atmPrf_repro2021_2008_00{day_num}" if day_num < 10 else f"atmPrf_repro2021_2008_0{day_num}"
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folder_path = os.path.join(base_folder_path, folder_name)
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# 检查文件夹是否存在
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if os.path.exists(folder_path):
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# 遍历文件夹中的文件
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for file_name in os.listdir(folder_path):
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if file_name.endswith('.0390_nc'):
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finfo = os.path.join(folder_path, file_name)
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print(f"正在处理文件: {finfo}")
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try:
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dataset = nc.Dataset(finfo, 'r')
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# 提取变量数据
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temp = dataset.variables['Temp'][:]
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altitude = dataset.variables['MSL_alt'][:]
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lat = dataset.variables['Lat'][:]
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lon = dataset.variables['Lon'][:]
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# 创建DataFrame
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df = pd.DataFrame({
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'Longitude': lon,
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'Latitude': lat,
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'Altitude': altitude,
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'Temperature': temp
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})
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dataset.close()
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# 剔除高度大于60的行
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df = df[df['Altitude'] <= 60]
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# 对每个文件的数据进行插值
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alt_interp = np.linspace(
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df['Altitude'].min(), df['Altitude'].max(), 3000)
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f_alt = interp1d(
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df['Altitude'], df['Altitude'], kind='linear', fill_value="extrapolate")
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f_lon = interp1d(
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df['Altitude'], df['Longitude'], kind='linear', fill_value="extrapolate")
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f_lat = interp1d(
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df['Altitude'], df['Latitude'], kind='linear', fill_value="extrapolate")
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f_temp = interp1d(
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df['Altitude'], df['Temperature'], kind='linear', fill_value="extrapolate")
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# 计算插值结果
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interpolated_alt = f_alt(alt_interp)
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interpolated_lon = f_lon(alt_interp)
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interpolated_lat = f_lat(alt_interp)
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interpolated_temp = f_temp(alt_interp)
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# 创建插值后的DataFrame
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interpolated_df = pd.DataFrame({
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'Altitude': interpolated_alt,
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'Longitude': interpolated_lon,
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'Latitude': interpolated_lat,
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'Temperature': interpolated_temp
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})
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# 将插值后的DataFrame添加到列表中
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dfs.append(interpolated_df)
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except Exception as e:
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print(f"处理文件 {finfo} 时出错: {e}")
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else:
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print(f"文件夹 {folder_path} 不存在。")
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# 按行拼接所有插值后的DataFrame
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final_df = pd.concat(dfs, axis=0, ignore_index=True)
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# 获取 DataFrame 的长度
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num_rows = len(final_df)
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# 生成一个每3000个数从0到2999的序列并重复
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altitude_values = np.tile(np.arange(3000), num_rows // 3000 + 1)[:num_rows]
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# 将生成的值赋给 DataFrame 的 'Altitude' 列
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final_df['Altitude'] = altitude_values
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# 摄氏度换算开尔文
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final_df['Temperature'] = final_df['Temperature'] + 273.15
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return final_df
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# 模块2: 数据筛选与网格划分
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def filter_and_grid_data(final_df, lat_range=(30, 40)):
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"""
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对输入的数据框进行纬度筛选以及经度网格划分等操作
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参数:
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final_df (pd.DataFrame): 预处理后的完整数据框
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返回:
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latitude_filtered_df (pd.DataFrame): 经过纬度筛选和经度网格划分后的数据框
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altitude_temperature_mean (pd.DataFrame): 按高度分组求平均温度后的数据框
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"""
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# 筛选出纬度在30到40度之间的数据
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lat_begin, lat_end = lat_range
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latitude_filtered_df = final_df[(
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final_df['Latitude'] >= lat_begin) & (final_df['Latitude'] <= lat_end)]
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# 划分经度网格，20°的网格
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lon_min, lon_max = latitude_filtered_df['Longitude'].min(
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), latitude_filtered_df['Longitude'].max()
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lon_bins = np.arange(lon_min, lon_max + 20, 20) # 创建经度网格边界
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# 将数据分配到网格中
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latitude_filtered_df['Longitude_Grid'] = np.digitize(
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latitude_filtered_df['Longitude'], lon_bins) - 1
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# 对相同高度的温度取均值，忽略NaN
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altitude_temperature_mean = latitude_filtered_df.groupby(
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'Altitude')['Temperature'].mean().reset_index()
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# 重命名列，使其更具可读性
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altitude_temperature_mean.columns = ['Altitude', 'Mean_Temperature']
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return latitude_filtered_df, altitude_temperature_mean
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# 模块3: 计算wn0和初始扰动相关操作
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def calculate_wn0_and_perturbation(latitude_filtered_df, altitude_temperature_mean):
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"""
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计算wn0以及相关的温度差值等操作
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参数:
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latitude_filtered_df (pd.DataFrame): 经过纬度筛选和经度网格划分后的数据框
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altitude_temperature_mean (pd.DataFrame): 按高度分组求平均温度后的数据框
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返回:
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merged_df (pd.DataFrame): 合并相关数据后的结果数据框，包含温度差值等列
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"""
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altitude_range = range(0, 3000)
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all_heights_mean_temperature = [] # 用于存储所有高度下的温度均值结果
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for altitude in altitude_range:
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# 筛选出当前高度的所有数据
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altitude_df = latitude_filtered_df[latitude_filtered_df['Altitude'] == altitude]
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# 对Longitude_Grid同一区间的温度取均值
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temperature_mean_by_grid = altitude_df.groupby(
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'Longitude_Grid')['Temperature'].mean().reset_index()
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# 重命名列，使其更具可读性
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temperature_mean_by_grid.columns = [
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'Longitude_Grid', 'Mean_Temperature']
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# 添加高度信息列，方便后续区分不同高度的结果
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temperature_mean_by_grid['Altitude'] = altitude
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# 将当前高度的结果添加到列表中
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all_heights_mean_temperature.append(temperature_mean_by_grid)
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# 将所有高度的结果合并为一个DataFrame
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combined_mean_temperature_df = pd.concat(
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all_heights_mean_temperature, ignore_index=True)
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# 基于Altitude列合并两个DataFrame，只保留能匹配上的行
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merged_df = pd.merge(combined_mean_temperature_df,
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altitude_temperature_mean, on='Altitude', how='inner')
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# 计算差值（减去wn0的扰动）
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merged_df['Temperature_Difference'] = merged_df['Mean_Temperature_x'] - \
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merged_df['Mean_Temperature_y']
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return merged_df
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# 模块4: 高度相同下不同区间网格数据的波拟合和滤波处理
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def wave_fitting_and_filtering(merged_df):
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"""
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对合并后的数据框按高度进行波拟合以及滤波处理
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参数:
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merged_df (pd.DataFrame): 合并相关数据后的结果数据框，包含温度差值等列
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返回:
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fit_results (dict): 存储每个高度的最佳拟合参数的字典
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residuals (dict): 存储每个高度的残差值的字典
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background_temperatures (dict): 存储每个高度的背景温度的字典
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"""
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def single_harmonic(x, A, phi):
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return A * np.sin(2 * np.pi / (18 / k) * x + phi)
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fit_results = {}
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fitted_curves = {}
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residuals = {}
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background_temperatures = {}
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grouped = merged_df.groupby('Altitude')
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for altitude, group in grouped:
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y_data = group['Temperature_Difference'].values
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x_data = np.arange(len(y_data))
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wn0_data = group['Mean_Temperature_y'].values # 获取同一高度下的wn0数据
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# 检查Temperature_Difference列是否全部为NaN
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if np.all(np.isnan(y_data)):
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fit_results[altitude] = {'A': [np.nan] * 5, 'phi': [np.nan] * 5}
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fitted_curves[altitude] = [np.nan * x_data] * 5
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residuals[altitude] = np.nan * x_data
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background_temperatures[altitude] = np.nan * \
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x_data # 对应高度全部为NaN时，背景温度也设为NaN
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else:
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# 替换NaN值为非NaN值的均值
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y_data = np.where(np.isnan(y_data), np.nanmean(y_data), y_data)
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# 初始化存储WN参数和曲线的列表
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wn_params = []
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wn_curves = []
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# 计算wn0（使用Mean_Temperature_y列数据）
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wn0 = wn0_data
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# 对WN1至WN5进行拟合
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for k in range(1, 6):
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# 更新单谐波函数中的k值
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def harmonic_func(x, A, phi): return single_harmonic(x, A, phi)
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# 使用curve_fit进行拟合
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popt, pcov = curve_fit(harmonic_func, x_data, y_data, p0=[
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np.nanmax(y_data) - np.nanmin(y_data), 0])
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A_fit, phi_fit = popt
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# 存储拟合结果
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wn_params.append({'A': A_fit, 'phi': phi_fit})
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# 使用拟合参数生成拟合曲线
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WN = harmonic_func(x_data, A_fit, phi_fit)
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wn_curves.append(WN)
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# 计算残差值
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y_data = y_data - WN # 使用残差值作为下一次拟合的y_data
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# 存储结果
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fit_results[altitude] = wn_params
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fitted_curves[altitude] = wn_curves
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residuals[altitude] = y_data # 最终残差值
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# 计算同一高度下的背景温度（wn0 + wn1 + wn2 + wn3 + wn4 + wn5）
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wn_sum = np.sum([wn0] + wn_curves, axis=0)
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background_temperatures[altitude] = wn_sum
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return fit_results, residuals, background_temperatures
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# 模块5: 带通滤波处理
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def bandpass_filtering(residuals):
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"""
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对残差数据进行带通滤波处理
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参数:
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residuals (dict): 存储每个高度的残差值的字典
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返回:
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result (dict): 存储滤波后每个高度结果的字典
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"""
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df = pd.DataFrame(residuals)
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# 使用前向填充（用上一个有效值填充 NaN）
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df.ffill(axis=1, inplace=True)
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result = {}
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lambda_low = 2 # 2 km
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lambda_high = 15 # 15 km
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f_low = 2 * np.pi / lambda_high
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f_high = 2 * np.pi / lambda_low
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for idx, residuals_array in df.iterrows():
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# 提取有效值
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valid_values = np.ma.masked_array(
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residuals_array, np.isnan(residuals_array))
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compressed_values = valid_values.compressed() # 去除NaN值后的数组
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N = len(compressed_values) # 有效值的数量
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# 如果有效值为空（即所有值都是NaN），则将结果设置为NaN
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if N == 0:
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result[idx] = np.full_like(residuals_array, np.nan)
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else:
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# 时间序列和频率
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dt = 0.02 # 假设的时间间隔
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n = np.arange(N)
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f = n / (N * dt)
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# 傅里叶变换
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y = np.fft.fft(compressed_values) # 使用去除NaN后的数组进行FFT
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# 频率滤波
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yy = y.copy()
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freq_filter = (f >= f_low) & (f <= f_high) # 保留指定频段
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yy[~freq_filter] = 0 # 过滤掉指定频段外的值
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# 逆傅里叶变换
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perturbation_after = np.real(np.fft.ifft(yy))
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# 将处理结果插回到result字典中
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result[idx] = perturbation_after
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return result
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# 模块6: 计算势能相关操作
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def calculate_potential_energy(background_temperatures, result, heights):
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"""
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基于背景温度、滤波后的扰动以及高度数据计算浮力频率和势能，并求平均
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参数:
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background_temperatures (dict): 存储每个高度的背景温度的字典
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result (dict): 存储滤波后每个高度结果的字典
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heights (np.ndarray): 高度数据数组
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返回:
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mean_ktemp_Nz (np.ndarray): 平均浮力频率数据
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mean_ktemp_Ptz (np.ndarray): 平均势能数据
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"""
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# 处理背景温度和扰动温度数据格式
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heights1 = list(background_temperatures.keys())
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data_length1 = len(next(iter(background_temperatures.values())))
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background_matrix = np.zeros((data_length1, len(heights1)))
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for idx, height in enumerate(heights1):
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background_matrix[:, idx] = background_temperatures[height]
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heights2 = list(result.keys())
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data_length2 = len(next(iter(result.values())))
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perturbation_matrix = np.zeros((data_length2, len(heights2)))
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for idx, height in enumerate(heights2):
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perturbation_matrix[:, idx] = result[height]
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perturbation_matrix = perturbation_matrix.T
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def brunt_vaisala_frequency(g, BT_z, c_p, heights):
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# 计算位温随高度的变化率
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dBT_z_dz = np.gradient(BT_z, heights)
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# 计算 Brunt-Väisälä 频率，根号内取绝对值
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frequency_squared = (g / BT_z) * ((g / c_p) + dBT_z_dz)
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frequency = np.sqrt(np.abs(frequency_squared))
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return frequency
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def calculate_gravitational_potential_energy(g, BT_z, N_z, PT_z):
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# 计算势能
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return 0.5 * ((g / N_z) ** 2) * ((PT_z / BT_z) ** 2)
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g = 9.81 # 重力加速度
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c_p = 1004.5 # 比热容
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N_z_matrix = []
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PT_z_matrix = []
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for i in range(background_matrix.shape[0]):
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BT_z = np.array(background_matrix[i])
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PT_z = np.array(perturbation_matrix[i]) # 滤波后的扰动
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# 调用Brunt-Väisälä频率函数
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N_z = brunt_vaisala_frequency(g, BT_z, c_p, heights)
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# 调用势能函数
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PW = calculate_gravitational_potential_energy(g, BT_z, N_z, PT_z)
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# 将结果添加到矩阵中
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N_z_matrix.append(N_z)
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PT_z_matrix.append(PW)
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ktemp_Nz = np.vstack(N_z_matrix)
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ktemp_Ptz = np.vstack(PT_z_matrix)
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mean_ktemp_Nz = np.mean(ktemp_Nz, axis=0)
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mean_ktemp_Ptz = np.mean(ktemp_Ptz, axis=0)
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# 对mean_ktemp_Ptz中的值做处理，如果超过40，除以10
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mean_ktemp_Ptz[mean_ktemp_Ptz > 40] /= 10
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return mean_ktemp_Nz, mean_ktemp_Ptz
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# 模块7 绘制重力波势能和浮力频率日图
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def plot_results_mean_ktemp_Nz(mean_ktemp_Nz, heights):
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"""
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绘制平均浮力频率和平均势能随高度变化的图像
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参数:
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mean_ktemp_Nz (np.ndarray): 平均浮力频率数据
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mean_ktemp_Ptz (np.ndarray): 平均势能数据
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heights (np.ndarray): 高度数据数组
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"""
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# 绘制平均浮力频率（mean_ktemp_Nz）随高度变化的图像
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plt.clf()
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plt.figure(figsize=(10, 6))
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plt.plot(mean_ktemp_Nz, heights / 1000) # 高度单位换算为km，方便展示
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plt.xlabel('平均浮力频率 (N_z)')
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plt.ylabel('高度(km)')
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# plt.gca().invert_yaxis() # 使高度坐标轴从上到下递增，符合常规习惯
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# plt.show()
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def plot_results_mean_ktemp_Ptz(mean_ktemp_Ptz, heights):
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"""
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# 绘制平均势能（mean_ktemp_Ptz）随高度变化的图像
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"""
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# clean the plot
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plt.clf()
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plt.figure(figsize=(10, 6))
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plt.plot(mean_ktemp_Ptz, heights / 1000) # 高度单位换算为km，方便展示
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plt.xlabel('平均势能 (PT_z)')
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plt.ylabel('高度(km)')
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# plt.gca().invert_yaxis() # 使高度坐标轴从上到下递增，符合常规习惯
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# plt.show()
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class CosmicGravitywPlot:
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def __init__(self, year, day, lat_range):
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BASE_PATH_COSMIC = DATA_BASEPATH.cosmic
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base_folder_path = f"./{BASE_PATH_COSMIC}/{year}"
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||
day_num = day
|
||
# 模块1调用
|
||
final_df = read_and_preprocess_data(base_folder_path, day_num)
|
||
# 模块2调用
|
||
latitude_filtered_df, altitude_temperature_mean = filter_and_grid_data(
|
||
final_df, lat_range)
|
||
# 模块3调用
|
||
merged_df = calculate_wn0_and_perturbation(
|
||
latitude_filtered_df, altitude_temperature_mean)
|
||
# 模块4调用
|
||
fit_results, residuals, background_temperatures = wave_fitting_and_filtering(
|
||
merged_df)
|
||
# 模块5调用
|
||
result = bandpass_filtering(residuals)
|
||
# 创建从1到80km的高度列表（按照代码中原本逻辑转换为对应单位的数组）
|
||
heights = np.linspace(0, 60000, 3000)
|
||
# 模块6调用，计算势能相关数据并获取平均浮力频率和平均势能
|
||
mean_ktemp_Nz, mean_ktemp_Ptz = calculate_potential_energy(
|
||
background_temperatures, result, heights)
|
||
# print("平均浮力频率（mean_ktemp_Nz）:", mean_ktemp_Nz)
|
||
# print("平均势能（mean_ktemp_Ptz）:", mean_ktemp_Ptz)
|
||
self.mean_ktemp_Nz = mean_ktemp_Nz
|
||
self.mean_ktemp_Ptz = mean_ktemp_Ptz
|
||
self.heights = heights
|
||
# 调用绘图模块函数进行绘图
|
||
|
||
def plot_results_mean_ktemp_Nz(self):
|
||
plot_results_mean_ktemp_Nz(self.mean_ktemp_Nz, self.heights)
|
||
|
||
def plot_results_mean_ktemp_Ptz(self):
|
||
plot_results_mean_ktemp_Ptz(self.mean_ktemp_Ptz, self.heights)
|