195 lines
8.1 KiB
Python
195 lines
8.1 KiB
Python
# 原始文件 saber行星波处理.py
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# 高度、纬度可以指定，得到指定高度纬度下的数据，高度在101行，一般输入70、90、110，纬度在115行，纬度一般输入-20、30、60
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import os
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import netCDF4 as nc
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import numpy as np
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import netCDF4 as nc
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import numpy as np
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import matplotlib.pyplot as plt
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from datetime import datetime, timedelta
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import pandas as pd
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from scipy.optimize import curve_fit
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from scipy.optimize import least_squares
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from CONSTANT import DATA_BASEPATH
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# 定义月份的英文名称
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months = [
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"January", "February", "March", "April", "May", "June",
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"July", "August", "September", "October", "November", "December"
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]
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def saber_planetw_process(
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year,
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target_h=70,
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target_lat=60
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):
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# 定义文件路径模板和年份
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base_path = DATA_BASEPATH.saber + \
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"/{year}/SABER_Temp_O3_{month}{year}_v2.0.nc"
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cache_path = DATA_BASEPATH.saber + \
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f"/{year}/gravity_result.parquet"
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if os.path.exists(cache_path):
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# load from cache
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return pd.read_parquet(cache_path)
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# 初始化空数组来存储拼接后的数据
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tplongitude = None
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tplatitude = None
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ktemp = None
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tpaltitude = None
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date = None
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tpSolarLT = None
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# 循环遍历每个月
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for month_name in months:
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# 构造文件路径
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file_path = base_path.format(month=month_name, year=year)
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# 打开文件并读取数据
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dataset = nc.Dataset(file_path, 'r')
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tplongitude_month = dataset.variables['tplongitude'][:]
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tplatitude_month = dataset.variables['tplatitude'][:]
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ktemp_month = dataset.variables['ktemp'][:]
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tpaltitude_month = dataset.variables['tpaltitude'][:]
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date_month = dataset.variables['date'][:]
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tpSolarLT_month = dataset.variables['tpSolarLT'][:]
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dataset.close()
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# 动态获取两个数组的较小维度，并截取较大的数组以匹配较小的维度
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if tplatitude is None:
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# 如果是第一个月，初始化数组
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tplongitude = tplongitude_month
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tplatitude = tplatitude_month
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ktemp = ktemp_month
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tpaltitude = tpaltitude_month
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date = date_month
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tpSolarLT = tpSolarLT_month
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else:
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min_dim = min(tplatitude.shape[1], tplatitude_month.shape[1])
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if tplatitude.shape[1] < tplatitude_month.shape[1]:
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tplatitude_month = tplatitude_month[:, :min_dim]
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ktemp_month = ktemp_month[:, :min_dim]
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tpaltitude_month = tpaltitude_month[:, :min_dim]
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tpSolarLT_month = tpSolarLT_month[:, :min_dim]
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tplongitude_month = tplongitude_month[:, :min_dim]
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else:
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tplatitude = tplatitude[:, :min_dim]
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ktemp = ktemp[:, :min_dim]
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tpaltitude = tpaltitude[:, :min_dim]
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tpSolarLT = tpSolarLT[:, :min_dim]
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tplongitude = tplongitude[:, :min_dim]
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# 拼接数据
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tplongitude = np.concatenate(
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(tplongitude, tplongitude_month), axis=0)
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tplatitude = np.concatenate((tplatitude, tplatitude_month), axis=0)
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ktemp = np.concatenate((ktemp, ktemp_month), axis=0)
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tpaltitude = np.concatenate((tpaltitude, tpaltitude_month), axis=0)
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date = np.concatenate((date, date_month), axis=0)
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tpSolarLT = np.concatenate((tpSolarLT, tpSolarLT_month), axis=0)
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# ----------数据处理------------------------------------------------------
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# 时间处理
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time_hour = tpSolarLT / 1000 / 3600
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# 假设date的格式为YYYYDDD，其中DDD是一年中的第几天
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dates = [datetime.strptime(f'{d:07d}', '%Y%j') for d in date]
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# 创建一个辅助函数来调整time_hour
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def adjust_time_hour(time_hour, dates):
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# 初始化一个和time_hour形状相同的数组，用于存储调整后的时间
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adjusted_time_hour = np.zeros_like(time_hour)
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# 获取第一个日期作为参考点
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first_date = dates[0]
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# 遍历dates和time_hour，根据日期调整时间
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for i, (dt, th) in enumerate(zip(dates, time_hour)):
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# 计算当前日期和第一个日期之间的天数差
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delta_days = (dt - first_date).days
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# 根据天数差调整时间
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adjusted_time_hour[i] = th + delta_days * 24
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return adjusted_time_hour
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# 调整time_hour
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adjusted_time_hour = adjust_time_hour(time_hour, dates)
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# 将小时转换为天
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adjusted_time_day = adjusted_time_hour / 24.0 # 转换为天
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# 找到第一个事件中与70最接近的高度的索引
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# 输入高度
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first_event_altitudes = tpaltitude[0]
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diff = np.abs(first_event_altitudes - target_h)
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closest_height_index = np.argmin(diff)
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# 读取每个事件对应closest_height_index下的纬度、tplongitude和adjusted_time_day数据
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selected_latitude = tplatitude[:, closest_height_index]
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selected_tplongitude = tplongitude[:, closest_height_index]
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selected_adjusted_time_day = adjusted_time_day[:, closest_height_index]
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selected_ktemp = ktemp[:, closest_height_index]
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# 将时间数据转换为整数天数，方便按天分组
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int_days = np.array([int(day) for day in selected_adjusted_time_day])
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# 1. 创建经度分组（0到360°，每20°一个区间）
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longitude_bins = np.arange(0, 361, 20)
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# 2. 创建纬度目标值
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# 假设目标纬度为60°
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# 3. 用于存储结果
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closest_event_indices = []
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# 4. 遍历每天的所有数据（按天分组），并查找与每个经度区间最接近的经度事件索引
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for day in np.unique(int_days): # 遍历每个独特的天数
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# 获取当前天数下的所有经度和纬度
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current_day_indices = np.where(int_days == day)[0]
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current_longitudes = selected_tplongitude[current_day_indices]
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current_latitudes = selected_latitude[current_day_indices]
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# 初始化列表用于存储该天的最接近经度和纬度的索引
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closest_indices_for_day = []
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# 遍历每个经度区间（20°为一个区间）
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for lon_bin_start in longitude_bins[:-1]:
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lon_bin_end = lon_bin_start + target_lat
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# 找到当前经度区间内的经度
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indices_in_bin = np.where((current_longitudes >= lon_bin_start) & (
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current_longitudes < lon_bin_end))[0]
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# 如果有经度落入该区间，找到与目标纬度20°最接近的纬度
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if len(indices_in_bin) > 0:
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# 计算纬度与目标纬度的差值
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latitudes_in_bin = current_latitudes[indices_in_bin]
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diff_latitude = np.abs(latitudes_in_bin - target_lat)
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# 找到与目标纬度最接近的索引
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closest_latitude_index = indices_in_bin[np.argmin(
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diff_latitude)]
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# 将该经度区间和最接近纬度的索引添加到结果中
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closest_indices_for_day.append(
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current_day_indices[closest_latitude_index])
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# 将该天的所有最接近的经度和纬度索引添加到总结果中
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closest_event_indices.extend(closest_indices_for_day)
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# 4. 获取这些事件的相关信息
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closest_latitudes = selected_latitude[closest_event_indices]
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closest_longitudes = selected_tplongitude[closest_event_indices]
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closest_times = selected_adjusted_time_day[closest_event_indices]
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closest_temperatures = selected_ktemp[closest_event_indices]
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# 5. 将经度从度数转换为弧度制
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closest_longitudes_radians = np.radians(closest_longitudes)
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# 将三列数据组合成一个二维数组
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combined_data = np.column_stack(
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(closest_longitudes_radians, closest_times, closest_temperatures))
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df = pd.DataFrame(combined_data, columns=[
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"Longitude_Radians", "Time", "Temperature"])
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# dump to cache
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df.to_parquet(cache_path)
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return df
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# 将数据保存为txt文件
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# np.savetxt('combined_data.txt', combined_data, fmt='%f', delimiter='\t',
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# header="Longitude_Radians\tTime\tTemperature", comments='')
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# print("数据已保存为 'combined_data.txt'")
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