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Dustella 2025-03-06 14:01:10 +08:00
parent 05a2ea6c16
commit f05fcd29b5
Signed by: Dustella
GPG Key ID: 35AA0AA3DC402D5C
13 changed files with 211 additions and 95 deletions
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1
backend.py
View File

@ -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")

3
build.sh Normal file
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@ -0,0 +1,3 @@
#!/bin/bash
pyinstaller --add-data "./res/*:res" --add-data "./res/assets/*:res/assets" ./backend.py

2
modules/balloon/__init__.py
View File

@ -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",
}
}

7
modules/balloon/utils.py
View File

@ -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))
))

8
modules/cosmic/__init__.py
View File

@ -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("~")))

12
modules/cosmic/gravityw_multiday.py
View File

@ -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)

46
modules/cosmic/gravityw_perday.py
View File

@ -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)

175
modules/cosmic/planetw_daily_process.py
View File

@ -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

6
modules/cosmic/planetw_perday.py
View File

@ -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)

8
modules/saber/gravityw_render.py
View File

@ -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

26
modules/tidi/planet_wave_daily.py
View File

@ -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')

9
modules/tidi/tidi_planetw_process.py
View File

@ -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)

3
run.sh Normal file
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@ -0,0 +1,3 @@
#!/bin/bash
./dist/backend/backend