From f05fcd29b537a8dba10227947679f497a04fd3b1 Mon Sep 17 00:00:00 2001 From: Dustella Date: Thu, 6 Mar 2025 14:01:10 +0800 Subject: [PATCH] sync --- backend.py | 1 + build.sh | 3 + modules/balloon/__init__.py | 2 +- modules/balloon/utils.py | 7 +- modules/cosmic/__init__.py | 8 +- modules/cosmic/gravityw_multiday.py | 12 +- modules/cosmic/gravityw_perday.py | 46 ++++++- modules/cosmic/planetw_daily_process.py | 175 ++++++++++++++---------- modules/cosmic/planetw_perday.py | 6 + modules/saber/gravityw_render.py | 8 +- modules/tidi/planet_wave_daily.py | 26 +++- modules/tidi/tidi_planetw_process.py | 9 +- run.sh | 3 + 13 files changed, 211 insertions(+), 95 deletions(-) create mode 100644 build.sh create mode 100644 run.sh diff --git a/backend.py b/backend.py index a98161d..6f8f722 100644 --- a/backend.py +++ b/backend.py @@ -56,6 +56,7 @@ async def index(path): if app.static_folder: # check if the file exists if os.path.exists(os.path.join(app.static_folder, path)): + # return await app.send_static_file(path) return await app.send_static_file("index.html") diff --git a/build.sh b/build.sh new file mode 100644 index 0000000..056b5fa --- /dev/null +++ b/build.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +pyinstaller --add-data "./res/*:res" --add-data "./res/assets/*:res/assets" ./backend.py diff --git a/modules/balloon/__init__.py b/modules/balloon/__init__.py index 80a51df..e698aa0 100644 --- a/modules/balloon/__init__.py +++ b/modules/balloon/__init__.py @@ -76,7 +76,7 @@ async def render_single_path(): response_data = { "image": img_str, "metadata": { - "是否是地形波": "是" if result.is_terrain_wave else "否", + "是否是地形重力波": "是" if result.is_terrain_wave else "否", } } diff --git a/modules/balloon/utils.py b/modules/balloon/utils.py index 15602d7..e66515f 100644 --- a/modules/balloon/utils.py +++ b/modules/balloon/utils.py @@ -80,9 +80,12 @@ def get_ballon_files(): def get_ballon_path_by_year(start_year, end_year, station=None): all_ballon_files = get_ballon_files() return list(filter( - lambda x: any(f"{station}-{year}" in x for year in range( + lambda x: any(f"-{year}" in x for year in range( start_year, end_year + 1)), - all_ballon_files + + list(filter( + lambda x: f"{station}" in x, + all_ballon_files)) )) diff --git a/modules/cosmic/__init__.py b/modules/cosmic/__init__.py index eb9d910..5816493 100644 --- a/modules/cosmic/__init__.py +++ b/modules/cosmic/__init__.py @@ -27,7 +27,7 @@ async def render(): year = request.args.get("year", 2008) T = request.args.get("T", 16) k = request.args.get("k", 1) - target_h = request.args.get("target_h", 40) + target_h = request.args.get("target_h", 100) target_latitude = request.args.get("target_lat", 30) # path: str = f"{DATA_BASEPATH.cosmic}/cosmic.txt" @@ -89,6 +89,10 @@ async def single_render(): await run_sync(p.plot_results_mean_ktemp_Nz)() elif mode == "mean_ktemp_Ptz": await run_sync(p.plot_results_mean_ktemp_Ptz)() + elif mode == 'residual_mean': + await run_sync(p.plot_results_residuals_means)() + elif mode == 'background_temp': + await run_sync(p.plot_results_background_temp)() else: raise ValueError("Invalid mode") @@ -103,7 +107,7 @@ async def single_render(): async def multiday_render(): year = request.args.get("year", 2008) start_day = request.args.get("start_day", 1) - end_day = request.args.get("end_day", 204) + end_day = request.args.get("end_day", 365) mode = request.args.get("mode", "位温分布") lat_range = request.args.get("lat_range", "30.0 ~ 40.0") lat_range = tuple(map(float, lat_range.split("~"))) diff --git a/modules/cosmic/gravityw_multiday.py b/modules/cosmic/gravityw_multiday.py index 95ff830..0d49514 100644 --- a/modules/cosmic/gravityw_multiday.py +++ b/modules/cosmic/gravityw_multiday.py @@ -11,7 +11,7 @@ import matplotlib.font_manager as fm from CONSTANT import DATA_BASEPATH from modules.cosmic.gravityw_multiday_process import process_single_file -DAY_RANGE = (0, 204) +DAY_RANGE = (0, 365) # 设置支持中文的字体 plt.rcParams['axes.unicode_minus'] = False # 正常显示负号 @@ -65,7 +65,7 @@ def plot_heatmap(data, heights, title): plt.figure(figsize=(10, 8)) # 绘制热力图，数据中的行代表高度，列代表天数 sns.heatmap(data, cmap='coolwarm', xticklabels=1, - yticklabels=50, cbar_kws={'label': 'Value'}) + yticklabels=50, cbar_kws={'label': '强度'}) plt.xlabel('天') plt.ylabel('高度 (km)') plt.title(title) @@ -97,13 +97,13 @@ class GravityMultidayPlot: # 调用函数绘制热力图 data = self.data plot_heatmap(data, self.heights, - '"最终平均温度场的热图 (单位：10^(-4))，随浮力频率Nz变化') + '年重力势能逐日随高度变化热力图(单位：10^(-4))') def plot_heatmap_tempPtz(self): # -------------绘制重力势能年统计图------------------------------------------------ data = self.df_final_mean_ktemp_Ptz.T plot_heatmap(data, self.heights, - '最终平均动能温度-位势温度的热图 (单位：焦耳/千克)') + '年浮力频率的平方逐日随高度变化热力图(单位：焦耳/千克)') def plot_monthly_tempNz(self): # ------------------------绘制月统计图--------------------------------------------------------------------------------- @@ -139,9 +139,9 @@ class GravityMultidayPlot: plt.plot(averaged_by_rows_df.columns, averaged_by_rows_df.mean( axis=0), marker='o', color='b', label='平均值') # 添加标题和标签 - plt.title('每月平均N^2的折线图') + plt.title('每月浮力频率的折线图') plt.xlabel('月份') - plt.ylabel('N^2(10^-4)') + plt.ylabel('浮力频率(10^-4)') plt.legend() # 显示图形 plt.grid(True) diff --git a/modules/cosmic/gravityw_perday.py b/modules/cosmic/gravityw_perday.py index 7989e51..b2c1939 100644 --- a/modules/cosmic/gravityw_perday.py +++ b/modules/cosmic/gravityw_perday.py @@ -231,7 +231,13 @@ def wave_fitting_and_filtering(merged_df): # 计算同一高度下的背景温度（wn0 + wn1 + wn2 + wn3 + wn4 + wn5） wn_sum = np.sum([wn0] + wn_curves, axis=0) background_temperatures[altitude] = wn_sum - return fit_results, residuals, background_temperatures + # 计算均值 + residuals_means = np.array( + [np.mean(residuals[altitude]) for altitude in residuals]) + background_temperatures_means = np.array( + [np.mean(background_temperatures[altitude]) for altitude in background_temperatures]) + + return fit_results, residuals, background_temperatures, residuals_means, background_temperatures_means # 模块5: 带通滤波处理 @@ -361,6 +367,7 @@ def plot_results_mean_ktemp_Nz(mean_ktemp_Nz, heights): plt.plot(mean_ktemp_Nz, heights / 1000) # 高度单位换算为km，方便展示 plt.xlabel('平均浮力频率 (N_z)') plt.ylabel('高度(km)') + plt.title('平均浮力频率随高度变化') # plt.gca().invert_yaxis() # 使高度坐标轴从上到下递增，符合常规习惯 # plt.show() @@ -376,6 +383,31 @@ def plot_results_mean_ktemp_Ptz(mean_ktemp_Ptz, heights): plt.plot(mean_ktemp_Ptz, heights / 1000) # 高度单位换算为km，方便展示 plt.xlabel('平均势能 (PT_z)') plt.ylabel('高度(km)') + plt.title('平均势能随高度变化') + # plt.gca().invert_yaxis() # 使高度坐标轴从上到下递增，符合常规习惯 + # plt.show() + + +# 模块8 绘制残差温度值曲线图和背景温度曲线图 +def plot_results1(residuals_means, heights): + + plt.close() + + plt.figure(figsize=(10, 6)) + plt.plot(residuals_means, heights / 1000) # 高度单位换算为km，方便展示 + plt.title("残差温度值曲线图") + plt.xlabel('残差(开尔文)') + plt.ylabel('高度(千米)') + # plt.gca().invert_yaxis() # 使高度坐标轴从上到下递增，符合常规习惯 + # plt.show() + + +def plot_results_background_temp(background_temperatures_means, heights): + plt.figure(figsize=(10, 6)) + plt.plot(background_temperatures_means, heights / 1000) # 高度单位换算为km，方便展示 + plt.title("背景温度曲线图") + plt.xlabel('背景温度(开尔文)') + plt.ylabel('高度(千米)') # plt.gca().invert_yaxis() # 使高度坐标轴从上到下递增，符合常规习惯 # plt.show() @@ -396,7 +428,7 @@ class CosmicGravitywPlot: merged_df = calculate_wn0_and_perturbation( latitude_filtered_df, altitude_temperature_mean) # 模块4调用 - fit_results, residuals, background_temperatures = wave_fitting_and_filtering( + fit_results, residuals, background_temperatures, residuals_means, background_temperatures_means = wave_fitting_and_filtering( merged_df) # 模块5调用 result = bandpass_filtering(residuals) @@ -410,6 +442,9 @@ class CosmicGravitywPlot: self.mean_ktemp_Nz = mean_ktemp_Nz self.mean_ktemp_Ptz = mean_ktemp_Ptz self.heights = heights + + self.residuals_means = residuals_means + self.background_temperatures_means = background_temperatures_means # 调用绘图模块函数进行绘图 def plot_results_mean_ktemp_Nz(self): @@ -417,3 +452,10 @@ class CosmicGravitywPlot: def plot_results_mean_ktemp_Ptz(self): plot_results_mean_ktemp_Ptz(self.mean_ktemp_Ptz, self.heights) + + def plot_results_residuals_means(self): + plot_results1(self.residuals_means, self.heights) + + def plot_results_background_temp(self): + plot_results_background_temp( + self.background_temperatures_means, self.heights) diff --git a/modules/cosmic/planetw_daily_process.py b/modules/cosmic/planetw_daily_process.py index 3587dd1..e885087 100644 --- a/modules/cosmic/planetw_daily_process.py +++ b/modules/cosmic/planetw_daily_process.py @@ -1,4 +1,5 @@ # 高度和target_latitude可以自己选择，纬度可以选+-30，+-60，高度可以每10km +import logging import netCDF4 as nc import pandas as pd import numpy as np @@ -9,6 +10,87 @@ import matplotlib.pyplot as plt from CONSTANT import DATA_BASEPATH +def _process_per_folder(base_folder_path, i, target_h): + # 根据i的值调整文件夹名称 + if i < 10: + folder_name = f"atmPrf_repro2021_2008_00{i}" # 一位数，前面加两个0 + elif i < 100: + folder_name = f"atmPrf_repro2021_2008_0{i}" # 两位数，前面加一个0 + else: + folder_name = f"atmPrf_repro2021_2008_{i}" # 三位数，不加0 + + # 构建当前文件夹的路径 + folder_path = os.path.join(base_folder_path, folder_name) + + # 检查文件夹是否存在 + if not os.path.exists(folder_path): + print(f"文件夹 {folder_path} 不存在。") + return + + # 遍历文件夹中的文件 + result_folder_level = [] + folder_level_cache_path = f"{folder_path}/planet_{target_h}.parquet" + if os.path.exists(folder_level_cache_path): + cache_content = pd.read_parquet(folder_level_cache_path) + # logging.info(f"读取缓存文件 {folder_level_cache_path}") + # dfs.append(cache_content) + return [cache_content] + + logging.info(f"处理文件夹 {folder_path}") + for file_name in os.listdir(folder_path): + if not file_name.endswith('.0390_nc'): + continue + + finfo = os.path.join(folder_path, file_name) + try: + dataset = nc.Dataset(finfo, 'r') + # 提取变量数据 + temp = dataset.variables['Temp'][:] + altitude = dataset.variables['MSL_alt'][:] + lat = dataset.variables['Lat'][:] + lon = dataset.variables['Lon'][:] + # 读取month, hour, minute + month = dataset.month + hour = dataset.hour + minute = dataset.minute + # 将i的值作为day的值 + day = i + # 将day, hour, minute拼接成一个字符串 + datetime_str = f"{day:03d}{hour:02d}{minute:02d}" + # 确保所有变量都是一维数组 + temp = np.squeeze(temp) + altitude = np.squeeze(altitude) + lat = np.squeeze(lat) + lon = np.squeeze(lon) + # 检查所有数组的长度是否相同 + assert len(temp) == len(altitude) == len(lat) == len( + lon), "Arrays must be the same length" + # 创建DataFrame，并将datetime_str作为第一列添加 + df = pd.DataFrame({ + 'Datetime_str': datetime_str, + 'Longitude': lon, + 'Latitude': lat, + 'Altitude': altitude, + 'Temperature': temp + }) + dataset.close() + # 仅筛选高度 + + df_filtered = df[(df['Altitude'] >= target_h - 0.5) + & (df['Altitude'] <= target_h + 0.5)] + result_folder_level.append(df_filtered) + except Exception as e: + print(f"处理文件 {finfo} 时出错: {e}") + # save to parquet + # merge them to a big dataframe, save to parquet + result_folder_level = pd.concat( + result_folder_level, axis=0, ignore_index=True) + logging.info(f"保存缓存文件 {folder_level_cache_path}") + logging.info(f"result_folder_length: {result_folder_level.__len__()}") + result_folder_level.to_parquet(folder_level_cache_path) + return [result_folder_level] + + def cosmic_planet_daily_process( year=2008, target_latitude=30, @@ -37,73 +119,21 @@ def cosmic_planet_daily_process( folder_path = os.path.join(base_folder_path, folder_name) # 检查文件夹是否存在 - if os.path.exists(folder_path): - # 遍历文件夹中的文件 - folder_level_cache_path = f"{folder_path}/planet_{target_h}.parquet" - if os.path.exists(folder_level_cache_path): - result_folder_level = pd.read_parquet(folder_level_cache_path) - dfs.append(result_folder_level) - continue - result_folder_level = [] - for file_name in os.listdir(folder_path): - - if not file_name.endswith('.0390_nc'): - continue - - finfo = os.path.join(folder_path, file_name) - print(f"正在处理文件: {finfo}") - try: - dataset = nc.Dataset(finfo, 'r') - # 提取变量数据 - temp = dataset.variables['Temp'][:] - altitude = dataset.variables['MSL_alt'][:] - lat = dataset.variables['Lat'][:] - lon = dataset.variables['Lon'][:] - # 读取month, hour, minute - month = dataset.month - hour = dataset.hour - minute = dataset.minute - # 将i的值作为day的值 - day = i - # 将day, hour, minute拼接成一个字符串 - datetime_str = f"{day:03d}{hour:02d}{minute:02d}" - # 确保所有变量都是一维数组 - temp = np.squeeze(temp) - altitude = np.squeeze(altitude) - lat = np.squeeze(lat) - lon = np.squeeze(lon) - # 检查所有数组的长度是否相同 - assert len(temp) == len(altitude) == len(lat) == len( - lon), "Arrays must be the same length" - # 创建DataFrame，并将datetime_str作为第一列添加 - df = pd.DataFrame({ - 'Datetime_str': datetime_str, - 'Longitude': lon, - 'Latitude': lat, - 'Altitude': altitude, - 'Temperature': temp - }) - dataset.close() - # 仅筛选高度 - - df_filtered = df[(df['Altitude'] >= target_h - 0.5) - & (df['Altitude'] <= target_h + 0.5)] - dfs.append(df_filtered) - result_folder_level.append(df_filtered) - except Exception as e: - print(f"处理文件 {finfo} 时出错: {e}") - # save to parquet - result_folder_level = pd.concat( - result_folder_level, axis=0, ignore_index=True) - result_folder_level.to_parquet(folder_level_cache_path) - else: + if not os.path.exists(folder_path): print(f"文件夹 {folder_path} 不存在。") + continue + + # 遍历文件夹中的文件 + result_folder_level = _process_per_folder( + base_folder_path, i, target_h) + + if result_folder_level is not None: + dfs.extend(result_folder_level) # 按行拼接所有DataFrame final_df = pd.concat(dfs, axis=0, ignore_index=True) # 假设 final_df 是你的大 DataFrame - final_df.to_csv("output365.txt", index=False, sep=",") # 逗号分隔 - print("数据已保存为 output365.txt") + # 计算差值找到最接近的索引来筛选纬度为30的数据 # 目标值 @@ -113,7 +143,7 @@ def cosmic_planet_daily_process( def find_closest_row(group): return group.loc[group['Latitude_diff'].idxmin()] - + logging.info(f"final_df: {final_df.head()}") # 使用groupby按Day分组并找到每个分组的最接近的记录 closest_per_day = final_df.groupby('Datetime_str', as_index=False).apply( find_closest_row, include_groups=False) @@ -128,15 +158,18 @@ def cosmic_planet_daily_process( def convert_to_days(datetime_str): # 获取日期、小时和分钟 - dd = int(datetime_str[:3]) # 日期部分 (DDD) - hh = int(datetime_str[3:5]) # 小时部分 (HH) - mm = int(datetime_str[5:]) # 分钟部分 (MM) + try: + dd = int(datetime_str[:3]) # 日期部分 (DDD) + hh = int(datetime_str[3:5]) # 小时部分 (HH) + mm = int(datetime_str[5:]) # 分钟部分 (MM) - # 计算总分钟数 - total_minutes = dd * 1440 + hh * 60 + mm + # 计算总分钟数 + total_minutes = dd * 1440 + hh * 60 + mm - # 转换为天数 - return total_minutes / 1440 + # 转换为天数 + return total_minutes / 1440 + except Exception as e: + logging.error(f"转换失败: {datetime_str}") # 应用函数转换 try: @@ -145,7 +178,7 @@ def cosmic_planet_daily_process( except Exception as e: print(e) # 时间t - day_data = closest_per_day['Time'] + # day_data = closest_per_day['Time'] # 计算背景温度，时间Day和弧度经度的函数 background_temperature = closest_per_day['Temperature'].mean() # 计算温度扰动 diff --git a/modules/cosmic/planetw_perday.py b/modules/cosmic/planetw_perday.py index 74a140f..bd3fa2f 100644 --- a/modules/cosmic/planetw_perday.py +++ b/modules/cosmic/planetw_perday.py @@ -61,6 +61,12 @@ def cosmic_planetw_plot_perday( x = np.array(df_8['Longitude_Radians']) # 经度弧度制 temperature = np.array(df_8['Temperature']) # 温度，因变量 + # `tempeture` 为因变量，`x` 为自变量，`t` 为参数 + # tempeture is possible to contain NaNs, so we need to drop them + mask = ~np.isnan(temperature) + x = x[mask] + temperature = temperature[mask] + # 用T进行拟合 popt, pcov = curve_fit(lambda x, *params: u_func(x, *params, t=t), x, temperature, p0=initial_guess, bounds=bounds, maxfev=50000) diff --git a/modules/saber/gravityw_render.py b/modules/saber/gravityw_render.py index 52ccc95..4b1839c 100644 --- a/modules/saber/gravityw_render.py +++ b/modules/saber/gravityw_render.py @@ -156,15 +156,15 @@ class SaberGravitywRenderer: # 原始信号的时间序列 plt.subplot(1, 2, 1) plt.plot(x1[::-1], y, label='原始信号') - plt.title('（a）Nz') - plt.xlabel('势能', labelpad=10) # 增加标签间距 + # plt.title('（a）Nz') + plt.xlabel('浮力频率平方', labelpad=10) # 增加标签间距 plt.ylabel('高度 (km)', labelpad=10) # 增加标签间距 # 原始信号的时间序列 plt.subplot(1, 2, 2) plt.plot(x2[::-1], y, label='原始信号') - plt.title('（b）Ptz') - plt.xlabel('浮力频率', labelpad=10) # 增加标签间距 + # plt.title('（b）Ptz') + plt.xlabel('重力势能', labelpad=10) # 增加标签间距 plt.ylabel('高度 (km)', labelpad=10) # 增加标签间距 # 调整子图之间的边距 diff --git a/modules/tidi/planet_wave_daily.py b/modules/tidi/planet_wave_daily.py index 981b23a..404087f 100644 --- a/modules/tidi/planet_wave_daily.py +++ b/modules/tidi/planet_wave_daily.py @@ -3,6 +3,7 @@ # 此代码是对数据处理后的txt数据进行行星波参数提取绘图 # 2006_TIDI_V_Meridional_data.txt也可以做同样处理，一个是经向风提取的行星波，一个是纬向风的 +import logging import os import pandas as pd import numpy as np @@ -22,7 +23,7 @@ matplotlib.rcParams['axes.unicode_minus'] = False # 正常显示负号 # initial_guess = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] # v0, a1, b1, a2, b2, a3, b3 initial_guess = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # 9个 a 和 9个 b 参数 - +logging.basicConfig(level=logging.INFO) # 设置参数界限 bounds = ( [0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, @@ -31,8 +32,6 @@ bounds = ( np.inf, np.inf, np.inf]) # 上界 -# 定义拟合函数 - def TidiPlotPlanetWDaily( mode, year, @@ -53,12 +52,16 @@ def TidiPlotPlanetWDaily( data_cache_path = f'{DATA_BASEPATH.tidi}/{year}/TIDI_{mode}h{input_height}r{lat_range[0]}-{lat_range[1]}_data.parquet' # if cache exist, read from cache if os.path.exists(data_cache_path): + logging.info(f'loading data from cache: {data_cache_path}') df = pd.read_parquet(data_cache_path) + logging.info(df.head()) else: + # 读取一年的数据文件 + logging.info(f'loading data from raw data: {year}') df = extract_tidi_planetw_data( year, input_height, lat_range )[mode] - df.to_parquet(data_cache_path) + # df.to_parquet(data_cache_path) # 删除有 NaN 的行 # V_Meridional @@ -89,7 +92,8 @@ def TidiPlotPlanetWDaily( # 检查当前窗口的数据量 if len(df_8) < min_data_points: # 输出数据量不足的警告 - print(f"数据量不足，无法拟合：{start_day} 到 {end_day}，数据点数量：{len(df_8)}") + logging.debug( + f"数据量不足，无法拟合：{start_day} 到 {end_day}，数据点数量：{len(df_8)}") # 将拟合参数设置为 NaN all_fit_results.append([np.nan] * 18) continue # 跳过当前时间窗口，继续下一个窗口 @@ -111,6 +115,10 @@ def TidiPlotPlanetWDaily( 'a5', 'b5', 'a6', 'b6', 'a7', 'b7', 'a8', 'b8', 'a9', 'b9'] fit_df = pd.DataFrame(all_fit_results, columns=columns) # fit_df即为拟合的参数汇总 + # 如果df 长度为0，说明没有数据，直接返回 + + logging.info(fit_df.head()) + # -------------------------------画图---------------------------- # a1-a9,对应波数-4、-3、-2、-1、0、1、2、3、4的行星波振幅 a_columns = ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9'] @@ -122,8 +130,14 @@ def TidiPlotPlanetWDaily( # 获取索引并转换为 numpy 数组 x_values = fit_df.index.to_numpy() - # 对每一列生成独立的图 col = k_to_a[f'k={k}'] + if fit_df[col].isna().all(): + # 画一张图，就写一行字，“数组不足以拟合” + plt.figure(figsize=(8, 6)) # 创建新的图形 + plt.text(0.5, 0.5, '数组不足以拟合', fontsize=24, ha='center') + plt.axis('off') + return + # 对每一列生成独立的图 plt.figure(figsize=(8, 6)) # 创建新的图形 plt.plot(x_values, fit_df[col].values) plt.title(f'k={k} 振幅图') diff --git a/modules/tidi/tidi_planetw_process.py b/modules/tidi/tidi_planetw_process.py index 1b9f6a7..dd56865 100644 --- a/modules/tidi/tidi_planetw_process.py +++ b/modules/tidi/tidi_planetw_process.py @@ -1,5 +1,6 @@ # 原始文件 TIDI行星波数据处理.py +import logging import os import numpy as np from scipy.io import loadmat @@ -26,6 +27,7 @@ def extract_tidi_planetw_data( if input_height not in HEIGHTS_CONSTANT: raise ValueError(f"input_height must be in {HEIGHTS_CONSTANT}") fixed_height_index = HEIGHTS_CONSTANT.index(input_height) + logging.info(f"fixed_height_index: {fixed_height_index}") height_list = [] lat_list = [] lon_list = [] @@ -74,6 +76,7 @@ def extract_tidi_planetw_data( lat_df = pd.DataFrame(lat_data['Lat']) lon_df = pd.DataFrame(lon_data['Lon']) vmeridional_df = pd.DataFrame(vmeridional_data['VMerdional']) + # logging.info(f"vmeridional_df: {vmeridional_df}") vzonal_df = pd.DataFrame(vzonal_data['Vzonal']) # 将每天的数据添加到列表中 @@ -91,7 +94,9 @@ def extract_tidi_planetw_data( lat_df = pd.concat(lat_list, ignore_index=True) lon_df = pd.concat(lon_list, ignore_index=True) vmeridional_df = pd.concat(vmeridional_list, axis=1) # 按列合并 + logging.info(f"vmeridional_df shape: {vmeridional_df.shape}") vzonal_df = pd.concat(vzonal_list, axis=1) # 按列合并 + logging.info(f"vzonal_df shape: {vzonal_df.shape}") # 初始化空列表来存储每天的数据 UTime_list = [] @@ -138,6 +143,8 @@ def extract_tidi_planetw_data( fixed_height_lon = lon_df.iloc[:, 0].values # 获取固定高度下的经向风数据 fixed_height_vmeridional = vmeridional_df.iloc[fixed_height_index, :].values + logging.info( + f"fixed_height_vmeridional shape: {fixed_height_vmeridional.shape}") # 获取固定高度下的纬向风数据 fixed_height_vzonal = vzonal_df.iloc[fixed_height_index, :].values # 获取对应的世界时数据（这里直接使用整个UTime数据，你可以根据实际情况判断是否需要处理下格式等） @@ -152,7 +159,7 @@ def extract_tidi_planetw_data( }) # 简单打印下整合后的数据前几行查看下内容 - print(combined_data) + print(combined_data["V_Meridional"].unique()) # 确定纬度和经度的范围 lat_min, lat_max = np.min(fixed_height_lat), np.max(fixed_height_lat) diff --git a/run.sh b/run.sh new file mode 100644 index 0000000..309574a --- /dev/null +++ b/run.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +./dist/backend/backend