fix

modules/balloon/extract_wave.py

@@ -4,6 +4,32 @@ from scipy.optimize import curve_fit
 from scipy.signal import lombscargle, hilbert
 from sklearn.decomposition import PCA
 import sympy as sp
+from scipy.interpolate import interp1d
+from scipy.signal import lombscargle
+import os
+import glob
+from scipy.optimize import curve_fit
+import math
+from sklearn.decomposition import PCA
+import sympy as sp
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.patches import Patch
+from matplotlib.lines import Line2D
+from windrose import WindroseAxes
+from scipy.signal import lombscargle, find_peaks
+from itertools import permutations
+from itertools import product
+
+
+def get_top_peaks(pgram, ff, num_peaks=3):
+    peaks, _ = find_peaks(pgram)
+    peak_values = pgram[peaks]
+    peak_frequencies = ff[peaks]
+    sorted_indices = np.argsort(peak_values)[-num_peaks:][::-1]  # 按照峰值从大到小排序
+    top_peaks_frequencies = peak_frequencies[sorted_indices]
+    top_peaks_values = peak_values[sorted_indices]
+    return top_peaks_frequencies, top_peaks_values
 
 
 def calculate_n(Av, Au, Bu, Bv):
@@ -118,7 +144,10 @@ def calculate_wave(data: pd.DataFrame, lat: float, g: float):
     pgram_ucmp = lombscargle(height, residual_ucmp, ff)
     pgram_vcmp = lombscargle(height, residual_vcmp, ff)
 
-    # todo:我的频率等于垂直波数（单位10-3rad/m）？
+    #  BEGIN CHANGE
+
+# BACK
+    # todo: 我的频率等于垂直波数（单位10-3rad/m）？
     main_frequency_temp = round(ff[np.argmax(pgram_temp)], 2)
     main_frequency_ucmp = round(ff[np.argmax(pgram_ucmp)], 2)
     main_frequency_vcmp = round(ff[np.argmax(pgram_vcmp)], 2)
@@ -132,6 +161,11 @@ def calculate_wave(data: pd.DataFrame, lat: float, g: float):
     rsd_temp = abs(round(((λ1 - mean) / λ1) * 100, 2))
     rsd_ucmp = abs(round(((λ2 - mean) / λ2) * 100, 2))
     rsd_vcmp = abs(round(((λ3 - mean) / λ3) * 100, 2))
+    # BACK
+
+    # new
+
+    # END CHANGE
 
     if not (rsd_temp < 20 and rsd_ucmp < 20 and rsd_vcmp < 20):
         return []

modules/balloon/read_data.py

@@ -2,9 +2,25 @@ import xarray as xr
 import pandas as pd
 import numpy as np
 from scipy.interpolate import interp1d
-
+from scipy.signal import lombscargle
+import os
+import glob
+from scipy.optimize import curve_fit
+import math
+from sklearn.decomposition import PCA
+import sympy as sp
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.patches import Patch
+from matplotlib.lines import Line2D
+from windrose import WindroseAxes
+from scipy.signal import lombscargle, find_peaks
+from itertools import permutations
+from itertools import product
 
 # 定义四舍五入函数
+
+
 def round_to_nearest_multiple(value, multiple):
     return round(value / multiple) * multiple
 
@@ -24,10 +40,12 @@ def read_data(path):
 
     # 移除重复的高度值
     extracted_df = extracted_df.drop_duplicates(subset=["alt"])
-    new_height = np.arange(extracted_df["alt"].min(), extracted_df["alt"].max() + 0.05, 0.05)
+    new_height = np.arange(extracted_df["alt"].min(
+    ), extracted_df["alt"].max() + 0.05, 0.05)
 
     #  将每个高度值转换为最接近0.05的整数倍，并转化为数组
-    rounded_heights = [round_to_nearest_multiple(height, 0.05) for height in new_height]
+    rounded_heights = [round_to_nearest_multiple(
+        height, 0.05) for height in new_height]
     rounded_heights_np = np.array(rounded_heights)
 
     # 初始化一个新的 DataFrame 用于存储插值结果
@@ -35,7 +53,8 @@ def read_data(path):
 
     # 对每个因变量进行线性插值
     for col in ["press", "temp", "rh", "u", "v", "wspeed"]:
-        interp_func = interp1d(extracted_df["alt"], extracted_df[col], kind="linear", fill_value="extrapolate")
+        interp_func = interp1d(
+            extracted_df["alt"], extracted_df[col], kind="linear", fill_value="extrapolate")
         interpolated_data[col] = interp_func(rounded_heights_np)
 
     return interpolated_data

modules/cosmic/__init__.py

@@ -35,8 +35,11 @@ async def single_render():
     year = request.args.get("year", 2008)
     day = request.args.get("day", 1)
     mode = request.args.get("mode", "mean_ktemp_Nz")
+    lat_range = request.args.get("lat_range", "30.0 ~ 40.0")
+    lat_range = tuple(map(float, lat_range.split("~")))
 
-    p: CosmicGravitywPlot = await run_sync(CosmicGravitywPlot)(year=int(year), day=int(day))
+    p: CosmicGravitywPlot = await run_sync(CosmicGravitywPlot)(
+        year=int(year), day=int(day), lat_range=lat_range)
     if mode == "mean_ktemp_Nz":
         await run_sync(p.plot_results_mean_ktemp_Nz)()
     elif mode == "mean_ktemp_Ptz":
@@ -57,10 +60,15 @@ async def multiday_render():
     start_day = request.args.get("start_day", 1)
     end_day = request.args.get("end_day", 204)
     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("~")))
 
-    p: GravityMultidayPlot = await run_sync(GravityMultidayPlot)(year=int(year),
+    p: GravityMultidayPlot = await run_sync(GravityMultidayPlot)(
-                                                                 day_range=(start_day, end_day))
+        year=int(year),
-    if mode == "布伦特-维萨拉频率分布":
+        day_range=(start_day, end_day),
+        lat_range=lat_range
+    )
+    if mode == "浮力频率均值":
         await run_sync(p.plot_heatmap_tempNz)()
     elif mode == "不同高度下的逐日统计分析":
         await run_sync(p.plot_heatmap_tempPtz)()
@@ -68,6 +76,8 @@ async def multiday_render():
         await run_sync(p.plot_floatage_trend)()
     elif mode == "每月平均重力势能的折线图":
         await run_sync(p.plot_monthly_energy)()
+    elif mode == "每月平均N^2的折线图":
+        await run_sync(p.plot_monthly_tempNz)()
     else:
         raise ValueError("Invalid mode")
 

modules/cosmic/gravityw_multiday.py

@@ -21,7 +21,7 @@ plt.rcParams['font.sans-serif'] = ['Simhei']  # 设置字体为微软雅黑
 # 主循环，处理1到365个文件
 
 
-def get_multiday_data(year=2008, day_range=DAY_RANGE):
+def get_multiday_data(year=2008, day_range=DAY_RANGE, lat_range=(30, 40)):
     base_folder_path = f"{DATA_BASEPATH.cosmic}/{year}"
     all_mean_ktemp_Nz = []
     all_mean_ktemp_Ptz = []
@@ -29,7 +29,7 @@ def get_multiday_data(year=2008, day_range=DAY_RANGE):
     for file_index in range(day_begin, day_end):
         try:
             mean_ktemp_Nz, mean_ktemp_Ptz = process_single_file(
-                base_folder_path, file_index)
+                base_folder_path, file_index, lat_range)
             if mean_ktemp_Nz is not None and mean_ktemp_Ptz is not None:
                 all_mean_ktemp_Nz.append(mean_ktemp_Nz)
                 all_mean_ktemp_Ptz.append(mean_ktemp_Ptz)
@@ -83,11 +83,11 @@ def plot_heatmap(data, heights, title):
 
 
 class GravityMultidayPlot:
-    def __init__(self, year, day_range):
+    def __init__(self, year, day_range, lat_range=None):
 
         self.year = year
         df_final_mean_ktemp_Nz, df_final_mean_ktemp_Ptz, data, heights = get_multiday_data(
-            year, day_range)
+            year, day_range, lat_range)
         self.df_final_mean_ktemp_Nz = df_final_mean_ktemp_Nz
         self.df_final_mean_ktemp_Ptz = df_final_mean_ktemp_Ptz
         self.data = data
@@ -148,7 +148,6 @@ class GravityMultidayPlot:
         plt.grid(True)
         plt.xticks(rotation=45)  # 让x轴标签（月份）倾斜，以便更清晰显示
         plt.tight_layout()
-        plt.show()
 
     def plot_monthly_energy(self):
         data1 = self.data1
@@ -192,7 +191,6 @@ class GravityMultidayPlot:
         plt.grid(True)
         plt.xticks(rotation=45)  # 让x轴标签（月份）倾斜，以便更清晰显示
         plt.tight_layout()
-        plt.show()
 
     def plot_floatage_trend(self):
         data = self.data
@@ -246,7 +244,6 @@ class GravityMultidayPlot:
         # 显示网格线，增强图表可读性
         ax.grid(True)
         # 显示图形
-        plt.show()
 
     def plot_floatage_trend(self):
         data1 = self.data1
@@ -300,4 +297,3 @@ class GravityMultidayPlot:
         # 显示网格线，增强图表可读性
         ax.grid(True)
         # 显示图形
-        plt.show()

modules/cosmic/gravityw_multiday_process.py

@@ -10,7 +10,7 @@ import seaborn as sns
 import matplotlib.font_manager as fm
 
 
-def process_single_file(base_folder_path, i):
+def process_single_file(base_folder_path, i, lat_range=(30, 40)):
     # 构建当前文件夹的路径
     if i < 10:
         folder_name = f"atmPrf_repro2021_2008_00{i}"  # 一位数，前面加两个0
@@ -93,9 +93,11 @@ def process_single_file(base_folder_path, i):
         # 摄氏度换算开尔文
         final_df['Temperature'] = final_df['Temperature'] + 273.15
 
+        lat_begin, lat_end = lat_range
+
         # 筛选出纬度在30到40度之间的数据
         latitude_filtered_df = final_df[(
-            final_df['Latitude'] >= 30) & (final_df['Latitude'] <= 40)]
+            final_df['Latitude'] >= lat_begin) & (final_df['Latitude'] <= lat_end)]
 
         # 划分经度网格，20°的网格
         lon_min, lon_max = latitude_filtered_df['Longitude'].min(

modules/cosmic/gravityw_perday.py

@@ -100,7 +100,7 @@ def read_and_preprocess_data(base_folder_path, day_num):
 
 
 # 模块2: 数据筛选与网格划分
-def filter_and_grid_data(final_df):
+def filter_and_grid_data(final_df, lat_range=(30, 40)):
     """
     对输入的数据框进行纬度筛选以及经度网格划分等操作
 
@@ -112,8 +112,9 @@ def filter_and_grid_data(final_df):
     altitude_temperature_mean (pd.DataFrame): 按高度分组求平均温度后的数据框
     """
     # 筛选出纬度在30到40度之间的数据
+    lat_begin, lat_end = lat_range
     latitude_filtered_df = final_df[(
-        final_df['Latitude'] >= 30) & (final_df['Latitude'] <= 40)]
+        final_df['Latitude'] >= lat_begin) & (final_df['Latitude'] <= lat_end)]
     # 划分经度网格，20°的网格
     lon_min, lon_max = latitude_filtered_df['Longitude'].min(
     ), latitude_filtered_df['Longitude'].max()
@@ -381,7 +382,7 @@ def plot_results_mean_ktemp_Ptz(mean_ktemp_Ptz, heights):
 
 class CosmicGravitywPlot:
 
-    def __init__(self, year, day):
+    def __init__(self, year, day, lat_range):
         BASE_PATH_COSMIC = DATA_BASEPATH.cosmic
 
         base_folder_path = f"./{BASE_PATH_COSMIC}/{year}"
@@ -390,7 +391,7 @@ class CosmicGravitywPlot:
         final_df = read_and_preprocess_data(base_folder_path, day_num)
         # 模块2调用
         latitude_filtered_df, altitude_temperature_mean = filter_and_grid_data(
-            final_df)
+            final_df, lat_range)
         # 模块3调用
         merged_df = calculate_wn0_and_perturbation(
             latitude_filtered_df, altitude_temperature_mean)

modules/saber/__init__.py

@@ -24,10 +24,14 @@ renderer = SaberGravitywRenderer()
 
 def get_gravityw_processer():
     lat_str = request.args.get("lat_range")
+    # if found ~ in str, replace it with ,
+    if "~" in lat_str:
+        lat_str = lat_str.replace("~", ",")
     if lat_str is None:
         lat_str = "30.0,40.0"
+    print(lat_str)
     lat_range = tuple(map(float, lat_str.split(",")))
-
+    print(lat_range)
     alt_str = request.args.get("alt_range")
     if alt_str is None:
         alt_str = "20.0,105.0"

modules/saber/planetw_plot.py

@@ -104,9 +104,9 @@ def saber_planetw_plot(
     # 对每一列生成独立的子图
     col = k_to_a[f'k={k}']
     plt.plot(x_values, fit_df[col].values)
-    plt.set_title(f'k = {k} 振幅图')
+    plt.suptitle(f'k = {k} 振幅图')
-    plt.set_xlabel('Day')
+    plt.xlabel('Day')
-    plt.set_ylabel('振幅')
+    plt.ylabel('振幅')
 
     # 设置横坐标的动态调整
     adjusted_x_values = x_values + (3 * T + 1) / 2
@@ -118,8 +118,7 @@ def saber_planetw_plot(
         tick_positions = adjusted_x_values
         tick_labels = [f'{int(val)}' for val in tick_positions]
 
-    plt.set_xticks(tick_positions)
+    plt.xticks(tick_positions, tick_labels)
-    plt.set_xticklabels(tick_labels)
 
     plt.tight_layout()
     # plt.show()