263 lines
9.5 KiB
Python
263 lines
9.5 KiB
Python
from io import BytesIO
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import pandas as pd
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import numpy as np
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import os
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from scipy.optimize import curve_fit
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import matplotlib.pyplot as plt
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# 解决绘图中中文不能显示的问题
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import matplotlib
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# 设置中文显示和负号正常显示
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matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文
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matplotlib.rcParams['axes.unicode_minus'] = False # 正常显示负号
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# 定义所有模型
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def tidal_model(t, v0, a1, b1, a2, b2, a3, b3, a4, b4):
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"""潮汐波模型"""
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T1, T2, T3, T4 = 6, 8, 12, 24 # 周期以小时为单位
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return (v0 + a1 * np.sin((2 * np.pi / T1) * t + b1)
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+ a2 * np.sin((2 * np.pi / T2) * t + b2)
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+ a3 * np.sin((2 * np.pi / T3) * t + b3)
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+ a4 * np.sin((2 * np.pi / T4) * t + b4))
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def planetary_model_2day(t, v0, a, b):
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"""2日行星波模型"""
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T = 48 # 周期为2天（48小时）
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return v0 + a * np.sin((2 * np.pi / T) * t + b)
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def planetary_model_5day(t, v0, a, b):
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"""5日行星波模型"""
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T = 120 # 周期为5天（120小时）
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return v0 + a * np.sin((2 * np.pi / T) * t + b)
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def planetary_model_10day(t, v0, a, b):
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"""10日行星波模型"""
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T = 240 # 周期为10天（240小时）
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return v0 + a * np.sin((2 * np.pi / T) * t + b)
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def planetary_model_16day(t, v0, a, b):
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"""16日行星波模型"""
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T = 384 # 周期为16天（384小时）
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return v0 + a * np.sin((2 * np.pi / T) * t + b)
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# 统一管理模型及其参数
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MODELS = {
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'潮汐波': {
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'function': tidal_model,
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'param_names': ['v0', 'a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4'],
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'initial_guess': [0, 1, 0, 1, 0, 1, 0, 1, 0],
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'bounds': ([-np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf],
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[np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]),
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'window': 5 # 窗口为5天
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},
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'2日行星波': {
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'function': planetary_model_2day,
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'param_names': ['v0', 'a', 'b'],
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'initial_guess': [0, 1, 0],
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'bounds': ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]),
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'window': 5 # 窗口为5天
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},
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'5日行星波': {
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'function': planetary_model_5day,
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'param_names': ['v0', 'a', 'b'],
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'initial_guess': [0, 1, 0],
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'bounds': ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]),
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'window': 15 # 窗口为15天
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},
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'10日行星波': {
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'function': planetary_model_10day,
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'param_names': ['v0', 'a', 'b'],
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'initial_guess': [0, 1, 0],
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'bounds': ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]),
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'window': 29 # 窗口为29天
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},
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'16日行星波': {
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'function': planetary_model_16day,
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'param_names': ['v0', 'a', 'b'],
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'initial_guess': [0, 1, 0],
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'bounds': ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]),
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'window': 49 # 窗口为49天
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},
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}
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ALL_MODEL_NAMES = list(MODELS.keys())
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# ----------------------------------------------------------第一大部分————————————————————————————————————————————————————
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# # 根据纬向风(u)、经向风(v),得到武汉左岭镇站/黑龙江漠河站
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# 固定年份（2022年）固定高度（90km）大气波动（6、8、12、24小时潮汐波，2日、5日、10日、16日行星波）随时间的变化
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# 参数设置，选择高度、时间、站点
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# station = '黑龙江漠河站'
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def get_df(H, year, station):
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# 文件路径设置
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file_dir = rf'./radar/data/{station}\{year}' # 数据文件路径
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output_dir = rf'./radar/out/{station}\{year}' # 参数txt、图片输出路径
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os.makedirs(output_dir, exist_ok=True) # 确保输出路径存在
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# 1-提取整个文件夹的数据
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# 获取所有 txt 文件
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file_list = [f for f in os.listdir(file_dir) if f.endswith('.txt')]
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# 初始化空的 DataFrame，用于合并所有文件的数据
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final_df = pd.DataFrame()
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# 批量处理每个 txt 文件
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for file_name in file_list:
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file_path = os.path.join(file_dir, file_name) # 拼接文件路径
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df = pd.read_csv(file_path, sep='\s+') # 读取文件
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date_part = os.path.splitext(file_name)[0][-8:] # 提取日期部分
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# 处理缺失值，将 1000000 替换为 NaN
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df.loc[df['uwind'] == 1000000, 'uwind'] = np.nan
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df.loc[df['vwind'] == 1000000, 'vwind'] = np.nan
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# 筛选出 height 列为 90km 的行
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filtered_df = df[df['height'] == H]
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# 重命名列，包含日期信息
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filtered_df = filtered_df.rename(columns={
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'uwind': f'{date_part}_uwind',
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'vwind': f'{date_part}_vwind'
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})
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# 只保留需要的列
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filtered_df = filtered_df[['height', 'time',
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f'{date_part}_uwind', f'{date_part}_vwind']]
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# 如果 final_df 为空，则直接赋值
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if final_df.empty:
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final_df = filtered_df
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else:
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# 按 'height' 和 'time' 列对齐合并
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final_df = pd.merge(final_df, filtered_df, on=[
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'height', 'time'], how='outer')
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# 生成完整日期范围的列名，补全缺失的天，使得平年365，闰年366
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all_dates = pd.date_range(
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f'{year}-01-01', f'{year}-12-31').strftime('%Y%m%d')
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expected_columns = [f'{date}_uwind' for date in all_dates] + \
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[f'{date}_vwind' for date in all_dates]
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# 确保 final_df 包含所有日期的列，缺失列补为 NaN
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for col in expected_columns:
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if col not in final_df.columns:
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final_df[col] = np.nan
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# 按列名排序（确保日期顺序）
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final_df = final_df[['height', 'time'] + sorted(expected_columns)]
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return final_df
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# 此时，final_df已经是90km包含完整天的分析数据---------------------------------------------------------
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# 通用函数：处理风速数据
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def final_plot_v1(wind_type, year, H, model_name, station):
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final_df = get_df(H, year, station)
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"""
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处理风速数据并针对多种模型进行拟合和绘图。
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"""
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wind_data = final_df[[col for col in final_df.columns if wind_type in col]]
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wind_data.columns = [col[:8] for col in wind_data.columns]
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result = wind_data.to_numpy().T.flatten()
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column_names = np.repeat(wind_data.columns.to_numpy(), 24)
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combined = pd.DataFrame(np.vstack((column_names, result)))
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dates = combined.iloc[0].values
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wind_speed = combined.iloc[1].values
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date_series = pd.to_datetime(dates)
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data = pd.DataFrame({'date': date_series, 'wind_speed': wind_speed})
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data.set_index('date', inplace=True)
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unique_dates = np.unique(data.index.date)
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if model_name not in MODELS:
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raise ValueError(f"Model {model_name} not found")
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return
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model_info = MODELS[model_name]
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params = []
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model_func = model_info['function']
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param_names = model_info['param_names']
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initial_guess = model_info['initial_guess']
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bounds = model_info['bounds']
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window_days = model_info['window'] # 获取窗口长度
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for date in unique_dates:
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# 根据模型窗口调整时间范围
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start_date = pd.to_datetime(
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date) - pd.Timedelta(days=(window_days - 1) / 2)
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end_date = pd.to_datetime(
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date) + pd.Timedelta(days=(window_days - 1) / 2)
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window_data = data[start_date:end_date]
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t = np.arange(len(window_data)) + 1
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y = pd.to_numeric(window_data['wind_speed'], errors='coerce')
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valid_mask = ~np.isnan(y)
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t = t[valid_mask]
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y = y[valid_mask]
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if len(y) < len(initial_guess) * 6:
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print(
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f"Not enough valid data after filtering for date: {date}")
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params.append([None] * len(param_names))
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continue
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try:
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popt, _ = curve_fit(
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model_func, t, y, p0=initial_guess, bounds=bounds)
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params.append(popt)
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except RuntimeError:
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print(f"Fitting failed for date: {date}")
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params.append([None] * len(param_names))
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# 保存参数
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params_df = pd.DataFrame(
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params, columns=param_names, index=unique_dates)
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# file_name = f'{year}年_{wind_type}_{H // 1000}km_{model_name}_强度.txt'
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# params_df.to_csv(os.path.join(output_dir, file_name),
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# sep='\t', index=True, header=True)
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# 绘图
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if 'a1' in params_df.columns: # 处理潮汐波多分量
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plt.figure(figsize=(12, 6))
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for i, T in enumerate([6, 8, 12, 24], start=1):
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plt.plot(params_df[f'a{i}'], label=f'{T}h', linewidth=2)
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else: # 处理行星波单分量
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plt.figure(figsize=(12, 6))
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plt.plot(
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params_df['a'], label=f'{model_name}', color='orange', linewidth=2)
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plt.title(
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f'{year}年{wind_type}_{H // 1000}km_{model_name}_强度', fontsize=16)
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plt.xlabel('日期', fontsize=14)
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plt.ylabel('幅度 (m/s)', fontsize=14)
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plt.xticks(rotation=45)
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plt.legend()
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plt.tick_params(axis='both', labelsize=12) # 调整刻度字体大小
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plt.tight_layout()
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# picture_file_name = f'{year}年_{wind_type}_{H // 1000}km_{model_name}_强度.png'
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# plt.savefig(os.path.join(output_dir, picture_file_name))
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# plt.show()
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buffer = BytesIO()
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plt.savefig(buffer, format='png')
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buffer.seek(0)
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plt.close()
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return buffer
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# 处理纬向风和经向风，适配所有模型
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if __name__ == "__main__":
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pass
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# final_plot_v1('uwind', year, H, "")
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# final_plot_v1('vwind', year, H, "")
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