61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
import xarray as xr
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import pandas as pd
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import numpy as np
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from scipy.interpolate import interp1d
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from scipy.signal import lombscargle
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import os
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import glob
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from scipy.optimize import curve_fit
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import math
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from sklearn.decomposition import PCA
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import sympy as sp
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import matplotlib.pyplot as plt
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import seaborn as sns
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from matplotlib.patches import Patch
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from matplotlib.lines import Line2D
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from windrose import WindroseAxes
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from scipy.signal import lombscargle, find_peaks
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from itertools import permutations
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from itertools import product
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# 定义四舍五入函数
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def round_to_nearest_multiple(value, multiple):
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return round(value / multiple) * multiple
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def read_data(path):
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# 读取数据
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with open(path, "rb") as f:
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df = xr.open_dataset(f).to_dataframe().dropna(how="any")
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# 提取指定列
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columns_to_extract = ["alt", "press", "temp", "rh", "u", "v", "wspeed"]
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extracted_df = df[columns_to_extract].copy()
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# 进行单位转换
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extracted_df["alt"] = extracted_df["alt"] / 1000 # km
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extracted_df["rh"] = extracted_df["rh"] * 100 # %
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# 移除重复的高度值
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extracted_df = extracted_df.drop_duplicates(subset=["alt"])
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new_height = np.arange(extracted_df["alt"].min(
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), extracted_df["alt"].max() + 0.05, 0.05)
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# 将每个高度值转换为最接近0.05的整数倍，并转化为数组
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rounded_heights = [round_to_nearest_multiple(
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height, 0.05) for height in new_height]
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rounded_heights_np = np.array(rounded_heights)
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# 初始化一个新的 DataFrame 用于存储插值结果
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interpolated_data = pd.DataFrame({"alt": rounded_heights_np})
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# 对每个因变量进行线性插值
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for col in ["press", "temp", "rh", "u", "v", "wspeed"]:
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interp_func = interp1d(
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extracted_df["alt"], extracted_df[col], kind="linear", fill_value="extrapolate")
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interpolated_data[col] = interp_func(rounded_heights_np)
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return interpolated_data
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