301 lines
14 KiB
Python
301 lines
14 KiB
Python
# 给重力波用的
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from dataclasses import dataclass
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from os import path
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import numpy as np
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import netCDF4 as nc
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import pandas as pd
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from scipy.optimize import curve_fit
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# ----------------------------------------------------------------------------------------------------------------------------
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# ----------------------------------------------------------------------------------------------------------------------------
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# 5---同周期下不同高度数据的BT_z背景位等指标计算 -
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# ----------------------------------------------------------------------------------------------------------------------------
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# ----------------------------------------------------------------------------------
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def power_indices(ktemp_cycles, ktemp_wn5, ktemp_ifft, altitude_min, altitude_max):
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# 定义Brunt-Väisälä频率计算函数
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def brunt_vaisala_frequency(g, BT_z, c_p, height):
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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, height)
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# 计算 Brunt-Väisälä 频率
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return np.sqrt((g / BT_z) * ((g / c_p) + dBT_z_dz))
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# 定义势能计算函数
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def calculate_gravitational_potential_energy(g, BT_z, N_z, PT_z):
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return 0.5 * ((g / N_z) ** 2) * ((PT_z / BT_z) ** 2)
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# 重力加速度
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g = 9.81
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# 空气比热容
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c_p = 1004.5
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height = np.linspace(altitude_min, altitude_max,
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ktemp_cycles.shape[1]) * 1000 # 高度
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background = ktemp_cycles - ktemp_wn5
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# 初始化结果矩阵
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N_z_matrix = []
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# 初始化结果矩阵
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PT_z_matrix = []
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# 循环处理background和filtered_perturbation所有行
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for i in range(background.shape[0]):
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BT_z = np.array(background[i])
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# 滤波后的扰动
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PT_z = np.array(ktemp_ifft[i])
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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, height)
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PT_z = calculate_gravitational_potential_energy(
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g, BT_z, N_z, PT_z) # 调用势能函数
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N_z_matrix.append(N_z)
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PT_z_matrix.append(PT_z)
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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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return ktemp_Nz, ktemp_Ptz
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# ----------------------------------------------------------------------------------------------------------------------------
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# 1---打开文件并读取不同变量数据 -
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# ----------------------------------------------------------------------------------------------------------------------------
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@dataclass
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class NcData:
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dataset: nc.Dataset
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tplatitude: np.ndarray
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tplongitude: np.ndarray
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tpaltitude: np.ndarray
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ktemp: np.ndarray
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time: np.ndarray
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date: np.ndarray
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date_time: np.ndarray
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path: str = None
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# 兼容旧代码，老的解构方式也能用
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def __iter__(self):
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return iter([self.dataset, self.tplatitude, self.tplongitude, self.tpaltitude, self.ktemp, self.time, self.date, self.date_time])
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ENABLE_SABER_FILTERING = False # 是否启用SABER数据的过滤
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def data_nc_load(file_path):
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dataset = nc.Dataset(file_path, 'r')
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# do filtering
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if ENABLE_SABER_FILTERING:
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# lon_valid = (ds.tplongitude >= 0) & (ds.tplongitude <= 360)
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lon_valid = (dataset.variables['tplongitude'][:] >= 0) & (
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dataset.variables['tplongitude'][:] <= 360)
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dataset = dataset.where(lon_valid, drop=True)
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# 第二步：纬度筛选 (-90-90)
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lat_valid = (dataset.variables['tplatitude'][:] >= -
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90) & (dataset.variables['tplatitude'][:] <= 90)
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dataset = dataset.where(lat_valid, drop=True)
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# 第三步：温度筛选 (80-1000 或 -999)
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temp_valid = ((dataset.variables['ktemp'][:] >= 80) & (
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dataset.variables['ktemp'][:] <= 1000)) | (dataset.variables['ktemp'][:] == -999)
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dataset = dataset.where(temp_valid, drop=True)
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# 纬度数据，二维数组形状为(42820,379) 42820为事件，379则为不同高度
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tplatitude = dataset.variables['tplatitude'][:, :]
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tplongitude = dataset.variables['tplongitude'][:, :] # 经度数据
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tpaltitude = dataset.variables['tpaltitude'][:, :] # 高度，二维数组形状为(42820,379)
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time = dataset.variables['time'][:, :] # 二维数组形状为(42820,379)
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date = dataset.variables['date'][:]
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date_time = np.unique(date) # 输出数据时间信息
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ktemp = dataset.variables['ktemp'][:, :] # 温度数据，二维数组形状为(42820,379)
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return NcData(dataset, tplatitude, tplongitude, tpaltitude, ktemp, time, date, date_time, path=file_path)
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# return dataset, tplatitude, tplongitude, tpaltitude, ktemp, time, date, date_time
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# ----------------------------------------------------------------------------------------------------------------------------
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# ----------------------------------------------------------------------------------------------------------------------------
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# 2---筛选某一天、某个纬度和高度范围15个不同cycle的温度数据 -
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# ----------------------------------------------------------------------------------------------------------------------------
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# 2-1 读取某一天的所有事件及其对应的纬度数据
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def day_data_read(date, day_read, tplatitude):
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events = np.where(date == day_read)[0] # 读取筛选天的事件编号 4294-5714位置，从0开始编号
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time_events = date[date == day_read] # 读取筛选天的事件编号 4294-5714位置，从0开始编号
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latitudes = tplatitude[events, 189] # 输出每个事件中间位置 即第189个经纬度
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df = pd.DataFrame([ # 创建一个包含事件编号、纬度的列表，共1421个事件
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{'time': time, 'event': event, 'latitude': lat}
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for time, event, lat in zip(time_events, events, latitudes)])
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# print(df.head()) # 打印前几行数据以检查
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return df
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# ----------------------------------------------------------------------------------------------------------------------------
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# 2-2 将事件按照卫星轨迹周期进行输出处理，并输出落在纬度范围内的每个周期的事件的行号
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def data_cycle_identify(df, latitude_min, latitude_max):
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cycles = [] # 存储每个周期的事件编号列表
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# 存储当前周期的事件编号
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current_cycle_events = []
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prev_latitude = None
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# 遍历DataFrame中的每一行以识别周期和筛选事件
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for index, row in df.iterrows():
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current_event = int(row['event'])
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current_latitude = row['latitude']
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if prev_latitude is not None and prev_latitude < 0 and current_latitude >= 0: # 检查是否是新周期的开始（纬度从负变正，且首次变正）
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# 重置当前周期的事件编号列表
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current_cycle_events = []
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if latitude_min <= current_latitude <= latitude_max: # 如果事件的纬度在指定范围内，添加到当前周期的事件编号列表
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current_cycle_events.append(current_event)
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if prev_latitude is not None and prev_latitude >= 0 and current_latitude < 0: # 检查是否是周期的结束（纬度从正变负）
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# 添加当前周期的事件编号列表到周期列表
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if current_cycle_events: # 确保当前周期有事件编号
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cycles.append(current_cycle_events)
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current_cycle_events = [] # 重置当前周期的事件编号列表
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prev_latitude = current_latitude
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if current_cycle_events: # 处理最后一个周期，如果存在的话
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cycles.append(current_cycle_events)
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print(f"一天周期为 {len(cycles)}")
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for cycle_index, cycle in enumerate(cycles, start=0):
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# 屏幕显示每个循环周期的事件
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print(f"周期 {cycle_index} 包含事件个数: {len(cycle)} 具体事件为: {cycle} ")
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return cycles
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# ----------------------------------------------------------------------------------------------------------------------------
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# 2-3---按照循环周期合并同周期数据，并输出处理后的温度数据、对应的高度数据
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def data_cycle_generate(cycles, ktemp, tpaltitude, altitude_min, altitude_max):
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if not cycles: # 如果周期列表为空，跳过当前迭代
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return None, None
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ktemp_cycles = [] # 初始化列表存储每个周期的温度
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altitude_cycles = [] # 初始化每个循环周期的高度数据
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for event in cycles:
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ktemp_cycles_events = np.array(ktemp[event, :]) # 获取每个周期各个事件的ktemp数据
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ktemp_cycles_events[
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np.logical_or(ktemp_cycles_events == -999, ktemp_cycles_events > 999)] = np.nan # 缺失值处理，避免影响结果
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ktemp_cycles_mean = np.nanmean(
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ktemp_cycles_events, axis=0) # 对所有周期的 ktemp 数据取均值
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altitude_cycles_mean = tpaltitude[event[0], :] # 使用第一个的高度来表征所有的
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altitude_indices = np.where((altitude_cycles_mean >= altitude_min) & (
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altitude_cycles_mean <= altitude_max))[0]
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ktemp_cycles.append(np.array(ktemp_cycles_mean[altitude_indices]))
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altitude_cycles.append(
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np.array(altitude_cycles_mean[altitude_indices]))
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min_length = 157 # min(len(arr) for arr in ktemp_cycles) # 找到最短列表的长度
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ktemp_cycles = np.vstack([arr[:min_length]
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for arr in ktemp_cycles]) # 创建新的列表，将每个子列表截断为最短长度
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altitude_cycles = np.vstack([arr[:min_length] for arr in altitude_cycles])
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return ktemp_cycles, altitude_cycles
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# ----------------------------------------------------------------------------------------------------------------------------
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# ----------------------------------------------------------------------------------------------------------------------------
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# 4---高度相同下不同循环周期数据的波拟合和滤波处理 -
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# ----------------------------------------------------------------------------------------------------------------------------
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# 对输入数据按照行（即纬向）进行波数为k的滤波,数据为15*157
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def fit_wave(ktemp_wn0, k):
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wave_fit = []
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def single_harmonic(x, A, phi, C, k):
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return A * np.sin(2 * np.pi / (15/k) * x + phi) + C
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# 数据转置并对每行进行操作，按照同高度数据进行处理
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for rtemp in ktemp_wn0.T:
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# 为当前高度层创建索引数组
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indices = np.arange(rtemp.size)
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def fit_temp(x, A, phi, C): return single_harmonic(
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x, A, phi, C, k) # 定义只拟合 A, phi, C 的 lambda 函数，k 固定
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params, params_covariance = curve_fit(
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fit_temp, indices, rtemp) # 使用 curve_fit 进行拟合
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# 提取拟合参数 A, phi, C
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A, phi, C = params
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# 使用拟合参数计算 wn3
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rtemp1 = single_harmonic(indices, A, phi, C, k)
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# 存储拟合参数和拟合曲线
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wave_fit.append(np.array(rtemp1))
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wave_fit = np.vstack(wave_fit)
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wave_fit = wave_fit.T
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wave_wn = ktemp_wn0-wave_fit
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return wave_fit, wave_wn
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# ----------------------------------------------------------------------------------------------------------------------------
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# ----------------------------------------------------------------------------------------------------------------------------
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# 4---同周期下不同高度数据的波拟合和滤波处理 -
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# ----------------------------------------------------------------------------------------------------------------------------
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# 对输入数据按照列（即纬向）进行滤波，滤除波长2和15km以内的波, 数据为15*157
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def fft_ifft_wave(ktemp_wn5, lamda_low, lamda_high, altitude_min, altitude_max, lvboin):
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ktemp_fft = []
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ktemp_ifft = []
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ktemp_fft_lvbo = []
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for rtemp in ktemp_wn5:
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# 采样点数或长度
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N = len(rtemp)
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# 采样时间间隔，其倒数等于采用频率，以1km为标准尺度等同于1s，假设波的速度为1km/s
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dt = (altitude_max-altitude_min)/(N-1)
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# 时间序列索引
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n = np.arange(N)
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# # t = altitude_min + n * dt # 时间向量
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# t = np.round(np.linspace(altitude_min, altitude_max, N),2)
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# 频率索引向量
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f = n / (N * dt)
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# 对输入信号进行傅里叶变换
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y = np.fft.fft(rtemp)
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# 定义波长滤波范围（以频率计算） # 高频截止频率
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f_low = 2*np.pi / lamda_high
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f_high = 2*np.pi / lamda_low
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# f_low = 1 / lamda_high # 定义波长滤波范围（以频率计算） # 高频截止频率
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# f_high = 1 / lamda_low # 低频截止频率
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# 创建滤波后的频域信号
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yy = y.copy()
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# 使用逻辑索引过滤特定频段（未确定）
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if lvboin:
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freq_filter = (f > f_low) & (f < f_high) # 创建逻辑掩码
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else:
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freq_filter = (f < f_low) | (f > f_high) # 创建逻辑掩码
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yy[freq_filter] = 0 # 过滤掉指定频段
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yy_ifft = np.real(np.fft.ifft(yy))
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ktemp_fft.append(y)
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# 存储拟合参数和拟合曲线
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ktemp_ifft.append(np.array(yy_ifft))
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ktemp_fft_lvbo.append(yy)
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ktemp_fft = np.vstack(ktemp_fft)
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ktemp_ifft = np.vstack(ktemp_ifft)
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ktemp_fft_lvbo = np.vstack(ktemp_fft_lvbo)
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return ktemp_fft, ktemp_fft_lvbo, ktemp_ifft
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