247 lines
9.1 KiB
Python
247 lines
9.1 KiB
Python
from dataclasses import dataclass
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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from CONSTANT import DATA_BASEPATH
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from saber.utils import *
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# lat_range=(latitude_min, latitude_max),
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# alt_range=(altitude_min, altitude_max),
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# lambda_range=(lamda_low, lamda_high),
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@dataclass
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class WaveData:
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cycles: Optional[np.ndarray] = None
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wn0: Optional[np.ndarray] = None
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fit_wn: List[Optional[np.ndarray]] = None # 存储wn1-wn5的拟合结果
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wn: List[Optional[np.ndarray]] = None # 存储wn1-wn5
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fft: Optional[np.ndarray] = None
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fft_filtered: Optional[np.ndarray] = None
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ifft: Optional[np.ndarray] = None
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Nz: Optional[float] = None
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Ptz: Optional[float] = None
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lat_range: Tuple[float, float] = None
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alt_range: Tuple[float, float] = None
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lambda_range: Tuple[float, float] = None
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# dump to dict
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def to_dict(self) -> Dict:
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"""将数据转换为字典格式，与原代码保持兼容"""
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result = {
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"ktemp_cycles_mon_list": self.cycles,
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"ktemp_wn0_mon_list": self.wn0,
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"ktemp_fit_wn1_mon_list": self.fit_wn[0],
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"ktemp_wn1_mon_list": self.wn[0],
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"ktemp_fit_wn2_mon_list": self.fit_wn[1],
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"ktemp_wn2_mon_list": self.wn[1],
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"ktemp_fit_wn3_mon_list": self.fit_wn[2],
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"ktemp_wn3_mon_list": self.wn[2],
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"ktemp_fit_wn4_mon_list": self.fit_wn[3],
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"ktemp_wn4_mon_list": self.wn[3],
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"ktemp_fit_wn5_mon_list": self.fit_wn[4],
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"ktemp_wn5_mon_list": self.wn[4],
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"ktemp_fft_mon_list": self.fft,
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"ktemp_fft_lvbo_mon_list": self.fft_filtered,
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"ktemp_ifft_mon_list": self.ifft,
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"ktemp_Nz_mon_list": self.Nz,
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"ktemp_Ptz_mon_list": self.Ptz,
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"lat_range": self.lat_range,
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"alt_range": self.alt_range,
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"lambda_range": self.lambda_range
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}
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return result
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# load from dict
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@classmethod
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def from_dict(cls, data: Dict):
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wave_data = cls()
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wave_data.cycles = data["ktemp_cycles_mon_list"]
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wave_data.wn0 = data["ktemp_wn0_mon_list"]
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wave_data.fit_wn = [
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data[f"ktemp_fit_wn{i+1}_mon_list"] for i in range(5)]
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wave_data.wn = [data[f"ktemp_wn{i+1}_mon_list"] for i in range(5)]
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wave_data.fft = data["ktemp_fft_mon_list"]
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wave_data.fft_filtered = data["ktemp_fft_lvbo_mon_list"]
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wave_data.ifft = data["ktemp_ifft_mon_list"]
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wave_data.Nz = data["ktemp_Nz_mon_list"]
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wave_data.Ptz = data["ktemp_Ptz_mon_list"]
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wave_data.lat_range = data["lat_range"]
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wave_data.alt_range = data["alt_range"]
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wave_data.lambda_range = data["lambda_range"]
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return wave_data
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@dataclass
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class YearlyData:
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date_times: List[datetime]
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wave_data: List[WaveData]
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def to_dict(self) -> Dict:
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"""将数据转换为字典格式，与原代码保持兼容"""
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result = {
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"date_time_list": self.date_times,
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"ktemp_cycles_mon_list": [],
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"altitude_cycles_mon_list": [],
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"ktemp_wn0_mon_list": [],
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"ktemp_fit_wn1_mon_list": [],
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"ktemp_wn1_mon_list": [],
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"ktemp_fit_wn2_mon_list": [],
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"ktemp_wn2_mon_list": [],
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"ktemp_fit_wn3_mon_list": [],
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"ktemp_wn3_mon_list": [],
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"ktemp_fit_wn4_mon_list": [],
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"ktemp_wn4_mon_list": [],
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"ktemp_fit_wn5_mon_list": [],
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"ktemp_wn5_mon_list": [],
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"ktemp_fft_mon_list": [],
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"ktemp_fft_lvbo_mon_list": [],
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"ktemp_ifft_mon_list": [],
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"ktemp_Nz_mon_list": [],
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"ktemp_Ptz_mon_list": [],
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"alt_range": [],
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}
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for data in self.wave_data:
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result["ktemp_cycles_mon_list"].append(data.cycles)
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result["ktemp_wn0_mon_list"].append(data.wn0)
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for i in range(5):
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result[f"ktemp_fit_wn{i+1}_mon_list"].append(data.fit_wn[i])
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result[f"ktemp_wn{i+1}_mon_list"].append(data.wn[i])
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result["ktemp_fft_mon_list"].append(data.fft)
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result["ktemp_fft_lvbo_mon_list"].append(data.fft_filtered)
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result["ktemp_ifft_mon_list"].append(data.ifft)
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result["ktemp_Nz_mon_list"].append(data.Nz)
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result["ktemp_Ptz_mon_list"].append(data.Ptz)
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result["alt_range"].append(data.alt_range)
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return result
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# load from dict
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@classmethod
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def from_dict(cls, data: Dict):
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wave_data = []
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for i in range(len(data["date_time_list"])):
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wave_data.append(WaveData())
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wave_data[-1].cycles = data["ktemp_cycles_mon_list"][i]
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wave_data[-1].wn0 = data["ktemp_wn0_mon_list"][i]
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wave_data[-1].fit_wn = [
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data[f"ktemp_fit_wn{i+1}_mon_list"][i] for i in range(5)]
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wave_data[-1].wn = [data[f"ktemp_wn{i+1}_mon_list"][i]
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for i in range(5)]
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wave_data[-1].fft = data["ktemp_fft_mon_list"][i]
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wave_data[-1].fft_filtered = data["ktemp_fft_lvbo_mon_list"][i]
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wave_data[-1].ifft = data["ktemp_ifft_mon_list"][i]
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wave_data[-1].Nz = data["ktemp_Nz_mon_list"][i]
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wave_data[-1].Ptz = data["ktemp_Ptz_mon_list"][i]
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return cls(date_times=data["date_time_list"], wave_data=wave_data)
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class DataProcessor:
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def __init__(self, lat_range: Tuple[float, float],
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alt_range: Tuple[float, float],
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lambda_range: Tuple[float, float],
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lvboin: float):
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self.lat_min, self.lat_max = lat_range
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self.alt_min, self.alt_max = alt_range
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self.lambda_low, self.lambda_high = lambda_range
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self.lvboin = lvboin
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def process_day(self, ncdata, day_read) -> Optional[WaveData]:
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# 读取数据
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dataset, tplatitude, tplongitude, tpaltitude, ktemp, time, date, date_time = ncdata
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df = day_data_read(date, day_read, tplatitude)
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cycles = data_cycle_identify(df, self.lat_min, self.lat_max)
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if not cycles:
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return None
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# 生成周期数据
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ktemp_cycles, alt_cycles = data_cycle_generate(
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cycles, ktemp, tpaltitude, self.alt_min, self.alt_max)
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if ktemp_cycles is None or alt_cycles is None:
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return None
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wave_data = WaveData()
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wave_data.cycles = ktemp_cycles
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# 计算波数分析
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wave_data.wn0 = ktemp_cycles - np.mean(ktemp_cycles, axis=0)
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wave_data.fit_wn = []
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wave_data.wn = []
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temp = wave_data.wn0
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# 循环计算波数1-5
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for i in range(1, 6):
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fit, residual = fit_wave(temp, i)
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wave_data.fit_wn.append(fit)
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wave_data.wn.append(residual)
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temp = residual
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# FFT分析
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wave_data.fft, wave_data.fft_filtered, wave_data.ifft = fft_ifft_wave(
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wave_data.wn[-1], self.lambda_low, self.lambda_high,
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self.alt_min, self.alt_max, self.lvboin)
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# 计算指数
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wave_data.Nz, wave_data.Ptz = power_indices(
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wave_data.cycles, wave_data.wn[-1], wave_data.ifft,
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self.alt_min, self.alt_max)
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# ranges for
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wave_data.lat_range = (self.lat_min, self.lat_max)
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wave_data.alt_range = (self.alt_min, self.alt_max)
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wave_data.lambda_range = (self.lambda_low, self.lambda_high)
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return wave_data
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def process_month(self, ncdata: NcData) -> List[Optional[WaveData]]:
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monthly_data = []
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for day_read in ncdata.date_time:
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print(f"Processing date: {day_read}")
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day_result = self.process_day(
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ncdata, day_read)
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if day_result is not None:
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monthly_data.append(day_result)
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return monthly_data
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def process_year(self, ncdata: List[NcData]) -> YearlyData:
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date_times = []
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wave_data = []
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for ncfile in ncdata:
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print(f"Processing file: {ncfile.path}")
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monthly_results = self.process_month(ncfile)
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date_times.extend(ncfile.date_time)
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wave_data.extend(monthly_results)
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return YearlyData(date_times=date_times, wave_data=wave_data)
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if __name__ == "__main__":
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year = 2018
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path = f"{DATA_BASEPATH.saber}/2012/SABER_Temp_O3_April2012_v2.0.nc"
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# 初始化某一天、某个纬度、高度范围等参数
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latitude_min = 30.0
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latitude_max = 40.0
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altitude_min = 20.0
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altitude_max = 105.0
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lamda_low = 2
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lamda_high = 15
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lvboin = True
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processor = DataProcessor(
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lat_range=(latitude_min, latitude_max),
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alt_range=(altitude_min, altitude_max),
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lambda_range=(lamda_low, lamda_high),
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lvboin=lvboin
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)
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# 处理月度数据
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monthly_results = processor.process_month(path)
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print(monthly_results)
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