235 lines
9.4 KiB
Python
235 lines
9.4 KiB
Python
import glob
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from io import BytesIO
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import os
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import re
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import seaborn as sns
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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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matplotlib.rcParams['font.sans-serif'] = ['SimHei']
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matplotlib.rcParams['axes.unicode_minus'] = False
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# ------------------------------ 模型定义 ------------------------------
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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(t, v0, a, b, period):
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"""行星波模型"""
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return v0 + a * np.sin((2 * np.pi / period) * t + b)
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# ------------------------------ 数据处理工具函数 ------------------------------
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def preprocess_data(file_list, year, month_range=(1,12)):
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def filter_given_month(month_range, file_list):
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begin_month, end_month = month_range
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get_date_pattern = r'_(\d{8})\.txt'
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filtered_file_list = []
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for file in file_list:
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date = re.search(get_date_pattern, file).group(1)
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month = int(date[4:6])
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if month >= begin_month and month <= end_month:
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filtered_file_list.append(file)
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return filtered_file_list
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if file_list == []:
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raise ValueError("No data files found.")
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file_list = filter_given_month(month_range, file_list)
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"""读取数据文件并合并成完整的 DataFrame"""
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# file_list = [f for f in os.listdir(file_dir) if f.endswith('.txt')]
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final_df = pd.DataFrame()
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for file_path in file_list:
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file_name = file_path.split('/')[-1]
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df = pd.read_csv(file_path, sep='\s+')
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# 替换异常值为 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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# 筛选所需列并重命名
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date_part = os.path.splitext(file_name)[0][-8:]
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df = df[['height', 'time', 'uwind', 'vwind']].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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if final_df.empty:
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final_df = df
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else:
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final_df = pd.merge(
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final_df, df, on=['height', 'time'], how='outer')
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# 补全缺失列并排序
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begin_month, end_month = month_range
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# calculate begin and end date
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begin_date = f'{year}-{begin_month:02d}-01'
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end_date = f'{year}-{end_month+1:02d}-01' if end_month < 12 else f'{year}-12-31'
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all_dates = pd.date_range(
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begin_date, end_date).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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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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final_df = final_df[['height', 'time'] + sorted(expected_columns)]
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return final_df
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# ------------------------------ 拟合工具函数 ------------------------------
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def fit_wind_speed(data, model_func, initial_guess, bounds, window, num_params):
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"""对风速数据进行滑动窗口拟合"""
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# replace "_uwind" and "_vwind" in date column
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data["date"] = data["date"].str.replace(
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"_uwind", "").str.replace("_vwind", "")
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data["date"] = pd.to_datetime(data["date"])
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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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params = []
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for date in unique_dates:
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start_date = pd.to_datetime(date) - pd.Timedelta(days=(window - 1) / 2)
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end_date = pd.to_datetime(date) + pd.Timedelta(days=(window - 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 = window_data['wind_speed'].values
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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) < num_params * 2:
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params.append([None] * len(initial_guess))
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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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params.append([None] * len(initial_guess))
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params_df = pd.DataFrame(params, columns=[
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"v0"] + [f"param_{i}" for i in range(1, len(initial_guess))], index=unique_dates)
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return params_df
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# ------------------------------ 热力图绘制函数 ------------------------------
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def plot_heatmaps(result_dict, param, title, vmax):
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"""绘制热力图"""
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fig, axes = plt.subplots(1, 2, figsize=(18, 8))
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fig.suptitle(title, fontsize=20)
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for i, (wind_type, data) in enumerate(result_dict.items()):
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heatmap_data = data[param].unstack(level=0)
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heatmap_data.index = pd.to_datetime(heatmap_data.index)
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heatmap_data = heatmap_data.iloc[:, ::-1]
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heatmap_data.index = heatmap_data.index.strftime('%Y-%m-%d')
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sns.heatmap(
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heatmap_data.T,
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cmap="viridis",
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vmax=vmax,
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cbar_kws={'label': '振幅 (m/s)'},
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yticklabels=[
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f'{height / 1000:.1f}' for height in heatmap_data.columns],
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ax=axes[i]
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)
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axes[i].set_title(f'{wind_type}振幅变化', fontsize=14)
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axes[i].set_xlabel('日期', fontsize=12)
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axes[i].set_ylabel('高度 (km)', fontsize=12)
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plt.tight_layout(rect=[0, 0, 1, 0.95])
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# plt.show()
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# ------------------------------ 主逻辑 ------------------------------
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def final_plot_v2(year, station, model_name, month_range=(1,12)):
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"""
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主逻辑函数，用于处理数据并绘制选定模型的热力图。
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参数:
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- year: int，年份
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- station: str，站点名称
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- model_name: str，模型名称（如 '潮汐波', '2日行星波', '5日行星波', '10日行星波', '16日行星波'）
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"""
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# 配置文件路径
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file_dir = rf'./radar/data/{station}/{year}'
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# output_dir = rf'./radar/tmp\{station}\{year}\时空'
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# os.makedirs(output_dir, exist_ok=True)
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# 数据预处理
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file_list = glob.glob(file_dir + '/**/*.txt', recursive=True)
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final_df = preprocess_data(file_list, year, month_range)
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uwind_data = final_df[["height", "time"] +
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[col for col in final_df.columns if "_uwind" in col]]
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vwind_data = final_df[["height", "time"] +
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[col for col in final_df.columns if "_vwind" in col]]
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heights = uwind_data["height"].unique()
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# 模型配置
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models = {
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"潮汐波": {"func": tidal_model, "params": 9, "initial": [0, 1, 0, 1, 0, 1, 0, 1, 0], "bounds": ([-np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf], [np.inf] * 9), "window": 5},
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"2日行星波": {"func": lambda t, v0, a, b: planetary_model(t, v0, a, b, 48), "params": 3, "initial": [0, 1, 0], "bounds": ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]), "window": 5},
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"5日行星波": {"func": lambda t, v0, a, b: planetary_model(t, v0, a, b, 120), "params": 3, "initial": [0, 1, 0], "bounds": ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]), "window": 15},
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"10日行星波": {"func": lambda t, v0, a, b: planetary_model(t, v0, a, b, 240), "params": 3, "initial": [0, 1, 0], "bounds": ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]), "window": 29},
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"16日行星波": {"func": lambda t, v0, a, b: planetary_model(t, v0, a, b, 384), "params": 3, "initial": [0, 1, 0], "bounds": ([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]), "window": 49}
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}
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# 检查模型名称是否有效
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if model_name not in models:
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raise ValueError(
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f"无效的模型名称: '{model_name}'。可选模型为: {list(models.keys())}")
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# 获取模型配置
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config = models[model_name]
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# 拟合纬向风和经向风
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u_result_dict, v_result_dict = {}, {}
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for height in heights:
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# 纬向风数据
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height_data_u = uwind_data[uwind_data["height"] == height].melt(
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id_vars=["time", "height"], var_name="date", value_name="wind_speed")
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u_result_dict[height] = fit_wind_speed(
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height_data_u, config["func"], config["initial"], config["bounds"], config["window"], config["params"])
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# 经向风数据
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height_data_v = vwind_data[vwind_data["height"] == height].melt(
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id_vars=["time", "height"], var_name="date", value_name="wind_speed")
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v_result_dict[height] = fit_wind_speed(
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height_data_v, config["func"], config["initial"], config["bounds"], config["window"], config["params"])
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# 汇总结果
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u_final_result = pd.concat(u_result_dict, names=["height", "date"])
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v_final_result = pd.concat(v_result_dict, names=["height", "date"])
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# 绘制热力图
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plot_heatmaps(
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{"纬向风": u_final_result, "经向风": v_final_result},
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param="param_1", # 振幅参数
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title=f'{year}年{month_range[0]}-{month_range[1]}月{model_name}振幅时空变化',
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vmax=100,
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)
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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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