sync

.gitignore

@@ -18,4 +18,7 @@ dist
 *.spec
 notebooks
 passcode
-data
+data
+
+res
+*.txt

CONSTANT.py

@@ -3,8 +3,8 @@ from dataclasses import dataclass
 
 @dataclass
 class DATA_BASEPATH:
-    balloon = "../data/balloon"
-    cosmic = "../data/cosmic"
-    radar = "../data/radar"
-    saber = "../data/saber"
-    tidi = "../data/tidi"
+    balloon = "./data/balloon"
+    cosmic = "./data/cosmic"
+    radar = "./data/radar"
+    saber = "./data/saber"
+    tidi = "./data/tidi"

backend.py

@@ -1,3 +1,4 @@
+import os
 import resource
 from matplotlib import pyplot as plt
 from modules import cosmic
@@ -12,7 +13,7 @@ import sys
 import matplotlib.font_manager as fm
 import ping
 
-app = Quart(__name__)
+app = Quart(__name__, static_folder="./res")
 app = cors(app, send_origin_wildcard=True, allow_origin="*")
 
 # limit the whole app not to use over 8G ram
@@ -22,29 +23,47 @@ app = cors(app, send_origin_wildcard=True, allow_origin="*")
 plt.switch_backend('agg')
 fm.fontManager.addfont("./SimHei.ttf")
 
+with open("./passcode.txt", "r") as f:
+    code = f.read()
+    if code:
+        code = code.strip()
+    else:
+        code = "0101"
+
 
 @app.before_request
 def auth():
     # check for method
     # if it is OPTIONS, do not check for auth
-    if request.method == "OPTIONS":
+    if request.method == "OPTIONS" or not request.path.startswith("/api"):
         return
-    code = request.headers.get("Authorization")
-    print(code)
-    if code != "0101":
+    if request.path.startswith("/api/ping"):
+        return
+    _code = request.headers.get("Authorization")
+    if _code != code:
         return "Unauthorized", 401
 
 
-@app.route("/ping")
-def dping():
-    return "pong"
+@app.route('/')
+async def return_index_html():
+    return await app.send_static_file("index.html")
 
 
-app.register_blueprint(balloon.balloon_module, url_prefix="/balloon")
-app.register_blueprint(radar.radar_module, url_prefix="/radar")
-app.register_blueprint(saber.saber_module, url_prefix="/saber")
-app.register_blueprint(tidi.tidi_module, url_prefix="/tidi")
-app.register_blueprint(cosmic.cosmic_module, url_prefix="/cosmic")
+@app.route("/<path:path>")
+async def index(path):
+    # check if there is path in static folder
+    # if not, return index.html
+    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")
+
+app.register_blueprint(balloon.balloon_module, url_prefix="/api/balloon")
+app.register_blueprint(radar.radar_module, url_prefix="/api/radar")
+app.register_blueprint(saber.saber_module, url_prefix="/api/saber")
+app.register_blueprint(tidi.tidi_module, url_prefix="/api/tidi")
+app.register_blueprint(cosmic.cosmic_module, url_prefix="/api/cosmic")
 # allow cors
 ping.setup_websocket(app)
 
@@ -52,10 +71,11 @@ ping.setup_websocket(app)
 if __name__ == '__main__':
     # get args '--prod'
     args = sys.argv
-    if 'prod' in args:
-        app.run("0.0.0.0", port=5000, debug=False)
-        pass
-    elif 'debug' in args:
+    print(os.getcwd())
+    # read from ENV Z_PORT
+    env_port = os.getenv("Z_PORT")
+    port = 5000 if env_port is None else int(env_port)
+    if 'debug' in args:
         app.run("0.0.0.0", port=18200, debug=True)
     else:
-        raise Exception("Invalied Mode")
+        app.run("0.0.0.0", port=port, debug=False)

modules/balloon/__init__.py

@@ -10,19 +10,13 @@ from quart import jsonify, request, send_file
 from modules.balloon.gravityw_plot_perday import render_by_mode_single
 from modules.balloon.gravityw_plot_year import get_all_modes, render_based_on_mode
 from modules.balloon.utils import *
+from quart.utils import run_sync
 
 BASE_PATH_BALLOON = DATA_BASEPATH.balloon
 
 all_year_data = pd.read_parquet(f"{BASE_PATH_BALLOON}/ballon_data_lin.parquet")
 
 
-def get_dataframe_between_year(start_year, end_year):
-    res = all_year_data
-    filtered_res = res[(res['file_name'].str.extract(r'LIN-(\d{4})')[0].astype(int) >= start_year) &
-                       (res['file_name'].str.extract(r'LIN-(\d{4})')[0].astype(int) <= end_year)]
-    return filtered_res
-
-
 balloon_module = Blueprint("Balloon", __name__)
 
 
@@ -60,8 +54,10 @@ async def render_full_year():
     end_year = request.args.get('end_year')
     mode = request.args.get('mode')
     season = request.args.get('season')
+    station = request.args.get('station')
+
     print(start_year, end_year)
-    df = get_dataframe_between_year(int(start_year), int(end_year))
+    df = await run_sync(get_ballon_full_df_by_year)(int(start_year), int(end_year), station, False)
     buff = render_based_on_mode(df, mode, season)
     return await send_file(buff, mimetype='image/png')
 

modules/balloon/extract_wave.py

@@ -30,6 +30,27 @@ def get_top_peaks(pgram, ff, num_peaks=3):
     top_peaks_frequencies = peak_frequencies[sorted_indices]
     top_peaks_values = peak_values[sorted_indices]
     return top_peaks_frequencies, top_peaks_values
+# 定义一个函数来计算 RSD
+
+
+def calculate_rsd(x, y, z):
+
+    # todo: 由波数计算波长
+    # lamde 垂直波长，只不过单位由10e-3 rad/m变为/km，值差不多
+    λ1 = round(1 / ((x / 2.5) * 0.4), 2)
+    λ2 = round(1 / ((y / 2.5) * 0.4), 2)
+    λ3 = round(1 / ((z / 2.5) * 0.4), 2)
+
+    # 计算均值
+    mean = round(sum([λ1, λ2, λ3]) / 3, 2)
+
+    # 计算相对标准差
+    rsd1 = abs(round(((λ1 - mean) / λ1) * 100, 2))
+    rsd2 = abs(round(((λ2 - mean) / λ2) * 100, 2))
+    rsd3 = abs(round(((λ3 - mean) / λ3) * 100, 2))
+
+    # 判断是否满足 RSD 小于 20%并返回结果和均值
+    return rsd1 < 20 and rsd2 < 20 and rsd3 < 20, mean
 
 
 def calculate_n(Av, Au, Bu, Bv):
@@ -148,29 +169,49 @@ def calculate_wave(data: pd.DataFrame, lat: float, g: float):
 
 # 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)
+    # 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)
 
-    # 由波数计算波长
-    λ1 = round(1 / ((main_frequency_temp / 2.5) * 0.4), 2)
-    λ2 = round(1 / ((main_frequency_ucmp / 2.5) * 0.4), 2)
-    λ3 = round(1 / ((main_frequency_vcmp / 2.5) * 0.4), 2)
+    # # 由波数计算波长
+    # λ1 = round(1 / ((main_frequency_temp / 2.5) * 0.4), 2)
+    # λ2 = round(1 / ((main_frequency_ucmp / 2.5) * 0.4), 2)
+    # λ3 = round(1 / ((main_frequency_vcmp / 2.5) * 0.4), 2)
 
-    mean = round(sum([λ1, λ2, λ3]) / 3, 2)
-    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))
+    # mean = round(sum([λ1, λ2, λ3]) / 3, 2)
+    # 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
+    # if not (rsd_temp < 20 and rsd_ucmp < 20 and rsd_vcmp < 20):
+    #     return []
 
-    # new
+    # begin new
+
+    peaks_ucmp_frequencies, _ = get_top_peaks(pgram_ucmp, ff)
+    peaks_vcmp_frequencies, _ = get_top_peaks(pgram_vcmp, ff)
+    peaks_temp_frequencies, _ = get_top_peaks(pgram_temp, ff)
+
+    # 获取所有可能的频率组合（9 种组合）
+    combinations = list(product(peaks_ucmp_frequencies,
+                                peaks_vcmp_frequencies,
+                                peaks_temp_frequencies))
+    result = "0"  # 初始结果为 "0"
+    for combo in combinations:
+        x, y, z = combo  # 获取每个组合的三个频率
+        valid_rsd, mean = calculate_rsd(x, y, z)  # 计算 RSD，并获取均值
+        if valid_rsd:  # 如果有任何组合满足 RSD 小于 20%，设置 result 为 "1"
+            result = "1"
+            break  # 找到一个符合条件的组合后，退出循环，避免继续检查其他组合
+    if result != "1":
+        return []
+
+    # end new
 
     # END CHANGE
 
-    if not (rsd_temp < 20 and rsd_ucmp < 20 and rsd_vcmp < 20):
-        return []
-
     # 定义正弦波模型函数
+
     def sine_wave(height, A, B, omega):
         omega = 2 * np.pi / mean
         return A * np.sin(omega * height + B)

modules/balloon/gravityw_plot_perday.py

@@ -18,16 +18,16 @@ def plot_polynomial_fit(ucmp, vcmp, temp, height, poly_ucmp, poly_vcmp, poly_tem
     plt.subplot(1, 3, 1)
     plt.plot(ucmp, height, linestyle="-")
     plt.plot(poly_ucmp(height), height)
-    plt.ylabel("Height(km)")
-    plt.xlabel("Zonal wind (m/s)")
+    plt.ylabel("高度(km)")
+    plt.xlabel("纬向风(m/s)")
     plt.grid(True)
 
     # v 风扰动量
     plt.subplot(1, 3, 2)
     plt.plot(vcmp, height, linestyle="-")
     plt.plot(poly_vcmp(height), height)
-    plt.ylabel("Height(km)")
-    plt.xlabel("Meridional wind (m/s)")
+    plt.ylabel("高度(km)")
+    plt.xlabel("经向风 (m/s)")
     plt.grid(True)
 
     plt.title("观测的二阶多项式拟合", fontsize=16)
@@ -36,8 +36,8 @@ def plot_polynomial_fit(ucmp, vcmp, temp, height, poly_ucmp, poly_vcmp, poly_tem
     plt.subplot(1, 3, 3)
     plt.plot(temp, height, linestyle="-")
     plt.plot(poly_temp(height), height)
-    plt.ylabel("Height(km)")
-    plt.xlabel("Temperature(K)")
+    plt.ylabel("高度(km)")
+    plt.xlabel("温度(K)")
     plt.grid(True)
 
     plt.tight_layout()
@@ -50,16 +50,16 @@ def plot_sine_fit(residual_ucmp, residual_vcmp, residual_temp, height, u_fit, v_
     plt.subplot(1, 3, 1)
     plt.plot(residual_ucmp, height, marker="o", linestyle="None")
     plt.plot(u_fit, height)
-    plt.ylabel("Height(km)")
-    plt.xlabel("Zonal wind (m/s)")
+    plt.ylabel("高度(km)")
+    plt.xlabel("纬向风 (m/s)")
     plt.grid(True)
 
     # v 风扰动量
     plt.subplot(1, 3, 2)
     plt.plot(residual_vcmp, height, marker="o", linestyle="None")
     plt.plot(v_fit, height)
-    plt.ylabel("Height(km)")
-    plt.xlabel("Meridional wind (m/s)")
+    plt.ylabel("高度(km)")
+    plt.xlabel("经向风(m/s)")
     plt.grid(True)
 
     plt.title("扰动分量的正弦波拟合", fontsize=16)
@@ -68,8 +68,8 @@ def plot_sine_fit(residual_ucmp, residual_vcmp, residual_temp, height, u_fit, v_
     plt.subplot(1, 3, 3)
     plt.plot(residual_temp, height, marker="o", linestyle="None")
     plt.plot(T_fit, height)
-    plt.ylabel("Height(km)")
-    plt.xlabel("Temperature(K)")
+    plt.ylabel("高度(km)")
+    plt.xlabel("温度(K)")
     plt.grid(True)
 
     plt.tight_layout()
@@ -87,8 +87,8 @@ def plot_uv_vector(u_fit, v_fit, height, specified_heights, markers):
     # plt.ylim(-4, 4)
     plt.axvline(0, color="gray", linestyle="--")
     plt.axhline(0, color="gray", linestyle="--")
-    plt.xlabel("Zonal Wind (m/s)")
-    plt.ylabel("Meridional Wind (m/s)")
+    plt.xlabel("经向风 (m/s)")
+    plt.ylabel("纬向风(m/s)")
     plt.legend()
     plt.grid(True)
     plt.title("径向风-纬向风矢量图")
@@ -102,8 +102,8 @@ def plot_temp_horizontal_wind(uh, T_fit, height, specified_heights, markers):
         plt.scatter(uh[index], T_fit[index],
                     marker=marker, s=100, label=f"{h} km")
 
-    plt.xlim(-4, 4)
-    plt.ylim(-2, 2)
+    # plt.xlim(-4, 4)
+    # plt.ylim(-2, 2)
     plt.axvline(0, color="gray", linestyle="--")
     plt.axhline(0, color="gray", linestyle="--")
     plt.xlabel("Horizontal wind (m/s)")

modules/balloon/utils.py

@@ -1,4 +1,5 @@
 import glob
+import os
 import re
 
 import pandas as pd
@@ -6,6 +7,7 @@ import pandas as pd
 import time
 import logging
 
+from CONSTANT import DATA_BASEPATH
 from modules.balloon.extract_wave import extract_wave, is_terrain_wave
 from modules.balloon.read_data import read_data
 
@@ -60,51 +62,81 @@ comboDate = [
 ]
 
 
+def get_dataframe_between_year(all_year_data, start_year, end_year, station):
+    res = all_year_data
+    filtered_res = res[(res['file_name'].str.extract(rf'{station}-(\d{{4}})')[0].astype(int) >= start_year) &
+                       (res['file_name'].str.extract(rf'{station}-(\d{{4}})')[0].astype(int) <= end_year)]
+    return filtered_res
+
+
 def get_ballon_files():
     try:
-        data = glob.glob("data/探空气球/**/*.nc", recursive=True)
+        data = glob.glob(f"{DATA_BASEPATH.balloon}/**/*.nc", recursive=True)
     except FileNotFoundError:
         return []
     return data
 
 
-all_ballon_files = get_ballon_files()
-
-
-def get_ballon_path_by_year(start_year, end_year):
+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"LIN-{year}" in x for year in range(
+        lambda x: any(f"{station}-{year}" in x for year in range(
             start_year, end_year + 1)),
         all_ballon_files
     ))
 
 
-def get_ballon_full_df_by_year(start_year, end_year):
+def get_ballon_full_df_by_year(start_year, end_year, station=None, ignore_cache=False):
 
     # Set up logging
     logging.basicConfig(level=logging.DEBUG,
                         format='%(asctime)s %(levelname)s: %(message)s')
 
+    if (station is None):
+        raise ValueError("Station is required")
+
+    cache_dir = f"{DATA_BASEPATH.balloon}/cache/{station}-b{start_year}-e{end_year}.parquet"
+    cache_base_dir = f"{DATA_BASEPATH.balloon}/cache/"
+    if os.path.exists(cache_dir) and not ignore_cache:
+        logging.debug(f"Reading cache from {cache_dir}")
+        return pd.read_parquet(cache_dir)
+    # check if there is any file begin with cache base dir, or say ,filename begin with station
+    # if so, read the file and return
+    if os.path.exists(cache_base_dir):
+        for file in os.listdir(cache_base_dir):
+            # should match {station}-bxxxx-exxxx.parquet, xxxx is year
+            if re.match(f"{station}-b\d{{4}}-e\d{{4}}.parquet", file):
+                # extract begin year and end year from filename
+                b_year = int(file.split("-")[1][1:])
+                e_year = int(file.split("-")[2][1:].split(".")[0])
+                logging.debug(
+                    f"Found cache file {file}, b_year={b_year}, e_year={e_year}")
+                if b_year <= start_year and e_year >= end_year:
+                    wider_df = pd.read_parquet(f"{cache_base_dir}/{file}")
+                    return get_dataframe_between_year(wider_df, start_year, end_year, station)
+
     start_time = time.time()
     logging.debug(
         f"Starting get_ballon_full_df_by_year with start_year={start_year}, end_year={end_year}")
 
     # Timing the path retrieval
     t0 = time.time()
-    paths = get_ballon_path_by_year(start_year, end_year)
+    paths = get_ballon_path_by_year(start_year, end_year, station)
+    # if no path found, raise
+    if len(paths) == 0:
+        raise ValueError(
+            f"No balloon data found for {station} between {start_year} and {end_year}")
     t1 = time.time()
     logging.debug(f"Retrieved {len(paths)} paths in {t1 - t0:.2f} seconds")
 
     # optimization: add cache. only select need to be reprocessed
-    with open("./cache/ballon_lin_has_wave", "r") as f:
-        cache_has_waves = f.readlines()
-    cache_has_waves = [x.strip() for x in cache_has_waves]
+    # with open("./cache/ballon_lin_has_wave", "r") as f:
+    #     cache_has_waves = f.readlines()
+    # cache_has_waves = [x.strip() for x in cache_has_waves]
 
     year_df = pd.DataFrame()
     for idx, file in enumerate(paths, 1):
-        if len(cache_has_waves) > 0 and file not in cache_has_waves:
-            logging.debug(f"Skipping {file} as it has no wave data")
-            continue
+
         file_start_time = time.time()
         logging.debug(f"Processing file {idx}/{len(paths)}: {file}")
 
@@ -130,11 +162,12 @@ def get_ballon_full_df_by_year(start_year, end_year):
             continue
 
         # Determine terrain wave
-        c = is_terrain_wave(data, lat, g)
-        terrain_time = time.time()
-
-        year_pattern = r"products-RS92-GDP.2-LIN-(\d{4})"
-        year = int(re.search(year_pattern, file).group(1))
+        try:
+            c = is_terrain_wave(data, lat, g)
+            terrain_time = time.time()
+        except Exception as e:
+            logging.error(f"Error determining terrain wave from {file}: {e}")
+            continue
 
         logging.debug(
             f"Determined terrain wave in {terrain_time - extract_time:.2f} seconds")
@@ -157,6 +190,15 @@ def get_ballon_full_df_by_year(start_year, end_year):
     total_time = time.time() - start_time
     logging.debug(
         f"Completed get_ballon_full_df_by_year in {total_time:.2f} seconds")
+    year_df['hori_wave_len'] = year_df['hori_wave_len'].apply(
+        lambda x: float(x) if x != ' ' else None)
+
+    # save to cache
+    # if parent folder does not exist, create it
+    if not os.path.exists(os.path.dirname(cache_dir)):
+        os.makedirs(os.path.dirname(cache_dir))
+    year_df.to_parquet(cache_dir)
+
     return year_df
 
 

modules/cosmic/__init__.py

@@ -10,6 +10,7 @@ from modules.cosmic.planetw_daily import cosmic_planetw_daily_plot
 from quart.utils import run_sync
 
 from modules.cosmic.planetw_daily_process import cosmic_planet_daily_process
+from modules.cosmic.planetw_perday import cosmic_planetw_plot_perday
 
 
 cosmic_module = Blueprint("Cosmic", __name__)
@@ -48,6 +49,31 @@ async def render():
     return await send_file(buf, mimetype="image/png")
 
 
+@cosmic_module.route('/render/planet_wave/perday')
+async def render_by_mode_single():
+    year = request.args.get("year", 2008)
+    T = request.args.get("T", 16)
+    target_h = request.args.get("target_h", 40)
+    target_latitude = request.args.get("target_lat", 30)
+
+    # path: str = f"{DATA_BASEPATH.cosmic}/cosmic.txt"
+    temp_df = await run_sync(cosmic_planet_daily_process)(
+        year=int(year),
+        target_h=int(target_h),
+        target_latitude=int(target_latitude)
+    )
+
+    await run_sync(cosmic_planetw_plot_perday)(
+        temp_df,
+        T=int(T),
+    )
+    buf = BytesIO()
+
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    return await send_file(buf, mimetype="image/png")
+
+
 @cosmic_module.route('/render/gravity_wave/perday')
 @cosmic_module.route('/render/single')
 async def single_render():

modules/cosmic/gravityw_multiday.py

@@ -66,10 +66,9 @@ def plot_heatmap(data, heights, title):
     # 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'})
-    plt.xlabel('Day')
-    plt.ylabel('Height (km)')
+    plt.xlabel('天')
+    plt.ylabel('高度 (km)')
     plt.title(title)
-    # 设置x轴的刻度，使其每30天显示一个标签
     num_days = data.shape[1]
     x_tick_positions = np.arange(0, num_days, 30)
     x_tick_labels = np.arange(0, num_days, 30)
@@ -98,13 +97,13 @@ class GravityMultidayPlot:
         # 调用函数绘制热力图
         data = self.data
         plot_heatmap(data, self.heights,
-                     'Heatmap of final_mean_ktemp_Nz（10^(-4)）')
+                     '"最终平均温度场的热图 (单位：10^(-4))，随浮力频率Nz变化')
 
     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,
-                     'Heatmap of final_mean_ktemp_Ptz(J/kg)')
+                     '最终平均动能温度-位势温度的热图 (单位：焦耳/千克)')
 
     def plot_monthly_tempNz(self):
         # ------------------------绘制月统计图---------------------------------------------------------------------------------

modules/cosmic/gravityw_perday.py

@@ -359,8 +359,8 @@ def plot_results_mean_ktemp_Nz(mean_ktemp_Nz, heights):
     plt.clf()
     plt.figure(figsize=(10, 6))
     plt.plot(mean_ktemp_Nz, heights / 1000)  # 高度单位换算为km，方便展示
-    plt.xlabel('Average (N_z)')
-    plt.ylabel('H(km)')
+    plt.xlabel('平均浮力频率 (N_z)')
+    plt.ylabel('高度(km)')
     # plt.gca().invert_yaxis()  # 使高度坐标轴从上到下递增，符合常规习惯
     # plt.show()
 
@@ -374,8 +374,8 @@ def plot_results_mean_ktemp_Ptz(mean_ktemp_Ptz, heights):
     plt.clf()
     plt.figure(figsize=(10, 6))
     plt.plot(mean_ktemp_Ptz, heights / 1000)  # 高度单位换算为km，方便展示
-    plt.xlabel('Average (PT_z)')
-    plt.ylabel('H (km)')
+    plt.xlabel('平均势能 (PT_z)')
+    plt.ylabel('高度(km)')
     # plt.gca().invert_yaxis()  # 使高度坐标轴从上到下递增，符合常规习惯
     # plt.show()
 

modules/cosmic/planetw_daily.py

@@ -108,7 +108,7 @@ def cosmic_planetw_daily_plot(
     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')
-    plt.xlabel('Day')
+    plt.xlabel('天')
     plt.ylabel('振幅')
 
     # 设置横坐标的动态调整

modules/cosmic/planetw_daily_process.py

@@ -24,7 +24,7 @@ def cosmic_planet_daily_process(
         return pd.read_parquet(cache_path)
 
     # 遍历文件夹序号1到365
-    for i in range(1, 365):
+    for i in range(1, 165):
         # 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
@@ -39,49 +39,63 @@ def cosmic_planet_daily_process(
         # 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 file_name.endswith('.0390_nc'):
-                    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)
-                    except Exception as e:
-                        print(f"处理文件 {finfo} 时出错: {e}")
+                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:
             print(f"文件夹 {folder_path} 不存在。")
 
@@ -125,8 +139,11 @@ def cosmic_planet_daily_process(
         return total_minutes / 1440
 
     # 应用函数转换
-    closest_per_day['Time'] = closest_per_day['Datetime_str'].apply(
-        convert_to_days)
+    try:
+        closest_per_day['Time'] = closest_per_day['Datetime_str'].apply(
+            convert_to_days)
+    except Exception as e:
+        print(e)
     # 时间t
     day_data = closest_per_day['Time']
     # 计算背景温度，时间Day和弧度经度的函数

modules/cosmic/planetw_perday.py

@@ -0,0 +1,79 @@
+import pandas as pd
+import numpy as np
+from scipy.optimize import curve_fit
+import matplotlib.pyplot as plt
+# 解决绘图中中文不能显示的问题
+import matplotlib
+matplotlib.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文
+matplotlib.rcParams['axes.unicode_minus'] = False  # 正常显示负号
+# 读取一年的数据文件
+# 设置初始参数
+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
+
+# 设置参数界限
+bounds = (
+    [0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf,
+        0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf],  # 下界
+    [np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf,
+        np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]  # 上界
+)
+# 定义拟合函数
+
+
+def cosmic_planetw_plot_perday(
+    df: pd.DataFrame,
+    T=16,
+
+):
+
+    def u_func(x, *params, t):
+        a1, b1, a2, b2, a3, b3, a4, b4, a5, b5, a6, b6, a7, b7, a8, b8, a9, b9 = params
+        return (
+            a1 * np.sin((2 * np.pi / T) * t - 4 * x + b1) +
+            a2 * np.sin((2 * np.pi / T) * t - 3 * x + b2) +
+            a3 * np.sin((2 * np.pi / T) * t - 2 * x + b3) +
+            a4 * np.sin((2 * np.pi / T) * t - x + b4) +
+            a5 * np.sin((2 * np.pi / T) * t + b5) +
+            a6 * np.sin((2 * np.pi / T) * t + x + b6) +
+            a7 * np.sin((2 * np.pi / T) * t + 2 * x + b7) +
+            a8 * np.sin((2 * np.pi / T) * t + 3 * x + b8) +
+            a9 * np.sin((2 * np.pi / T) * t + 4 * x + b9)
+        )
+
+    # 设置最小数据量的阈值
+    min_data_points = 36
+
+    # 进行多个时间窗口的拟合
+    for start_day in range(0, 365 - 3 * T):  # 最后一个窗口为[351, 366]
+        end_day = start_day + 3 * T  # 每个窗口的结束时间为 start_day + 3*T
+
+        # 选择当前窗口的数据
+        df_8 = df[(df['Time'] >= start_day) & (df['Time'] <= end_day)]
+
+        # 检查当前窗口的数据量
+        if len(df_8) < min_data_points:
+            print(f"数据量不足，无法拟合：{start_day} 到 {end_day}，数据点数量：{len(df_8)}")
+            continue  # 跳过当前时间窗口，继续下一个窗口
+
+        # 提取时间、经度、温度数据
+        t = np.array(df_8['Time'])  # 时间
+        x = np.array(df_8['Longitude_Radians'])  # 经度弧度制
+        temperature = np.array(df_8['Temperature'])  # 温度，因变量
+
+        # 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)
+
+        # 绘制拟合曲线
+        t_fixed = (start_day + end_day) / 2  # 窗口的中间时间
+        x_fit = np.linspace(min(x), max(x), 100)  # 生成用于绘制拟合曲线的x值
+        y_fit = u_func(x_fit, *popt, t=t_fixed)  # 计算拟合曲线的y值
+
+        plt.figure(figsize=(10, 6))
+        plt.plot(x_fit, y_fit, label='拟合曲线', color='red', linewidth=2)
+        plt.xlabel('经度（弧度制）')
+        plt.ylabel('温度')
+        plt.title(f'时间窗口：{start_day} 到 {end_day}')
+        plt.legend()
+        plt.grid(True)

modules/saber/__init__.py

@@ -3,6 +3,7 @@ from io import BytesIO
 from quart import Blueprint, request, send_file
 from matplotlib import pyplot as plt
 from CONSTANT import DATA_BASEPATH
+from modules.saber.planetw_perday import saber_planetw_plot_perday
 from modules.saber.planetw_plot import saber_planetw_plot
 from modules.saber.planetw_process import saber_planetw_process
 from modules.saber.gravityw_process import SaberGravitywProcessor
@@ -164,3 +165,24 @@ async def do_gravity_year():
     )
     await run_sync(saber_planetw_plot)(data, T, k=int(k))
     return await extract_payload()
+
+
+@saber_module.route("/render/planet_wave/per_day")
+async def do_pl_year():
+    year = request.args.get("year")
+    T = request.args.get("T")
+    # k = request.args.get("k")
+    H = request.args.get("H")
+    lat_target = request.args.get("lat_target")
+    start_day = request.args.get("start_day", 0)
+    year = int(year)
+    T = int(T)
+    if T not in [5, 10, 16]:
+        raise ValueError("T must be in [5, 10, 16]")
+    data = await run_sync(saber_planetw_process)(
+        year,
+        target_h=int(H),
+        target_lat=int(lat_target)
+    )
+    await run_sync(saber_planetw_plot_perday)(data, T, int(start_day))
+    return await extract_payload()

modules/saber/gravityw_render.py

@@ -157,14 +157,14 @@ class SaberGravitywRenderer:
         plt.subplot(1, 2, 1)
         plt.plot(x1[::-1], y, label='原始信号')
         plt.title('（a）Nz')
-        plt.xlabel('Nz', labelpad=10)  # 增加标签间距
+        plt.xlabel('势能', 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('Ptz', labelpad=10)  # 增加标签间距
+        plt.xlabel('浮力频率', 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

modules/saber/planetw_perday.py

@@ -0,0 +1,86 @@
+import pandas as pd
+import numpy as np
+from scipy.optimize import curve_fit
+import matplotlib.pyplot as plt
+# 解决绘图中中文不能显示的问题
+import matplotlib
+matplotlib.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文
+matplotlib.rcParams['axes.unicode_minus'] = False  # 正常显示负号
+# 读取一年的数据文件
+# 设置初始参数
+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
+
+# 设置参数界限
+bounds = (
+    [0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf,
+        0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf],  # 下界
+    [np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf,
+        np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]  # 上界
+)
+# 定义拟合函数
+
+
+def saber_planetw_plot_perday(
+    df: pd.DataFrame,
+    T=16,
+    start_day=0
+
+):
+
+    def u_func(x, *params, t):
+        a1, b1, a2, b2, a3, b3, a4, b4, a5, b5, a6, b6, a7, b7, a8, b8, a9, b9 = params
+        return (
+            a1 * np.sin((2 * np.pi / T) * t - 4 * x + b1) +
+            a2 * np.sin((2 * np.pi / T) * t - 3 * x + b2) +
+            a3 * np.sin((2 * np.pi / T) * t - 2 * x + b3) +
+            a4 * np.sin((2 * np.pi / T) * t - x + b4) +
+            a5 * np.sin((2 * np.pi / T) * t + b5) +
+            a6 * np.sin((2 * np.pi / T) * t + x + b6) +
+            a7 * np.sin((2 * np.pi / T) * t + 2 * x + b7) +
+            a8 * np.sin((2 * np.pi / T) * t + 3 * x + b8) +
+            a9 * np.sin((2 * np.pi / T) * t + 4 * x + b9)
+        )
+
+    # 设置最小数据量的阈值
+    min_data_points = 36
+
+    # 进行多个时间窗口的拟合
+    # for start_day in range(0, 365 - 3 * T):  # 最后一个窗口为[351, 366]
+    end_day = start_day + 3 * T  # 每个窗口的结束时间为 start_day + 3*T
+
+    # 选择当前窗口的数据
+    df_8 = df[(df['Time'] >= start_day) & (df['Time'] <= end_day)]
+
+    # 检查当前窗口的数据量
+    if len(df_8) < min_data_points:
+        print(f"数据量不足，无法拟合：{start_day} 到 {end_day}，数据点数量：{len(df_8)}")
+        plt.figure(figsize=(10, 6))
+        # write nothing but a title
+        plt.title(f'时间窗口：{start_day} 到 {end_day}')
+        plt.grid(True)
+        # add a label, says unable to fit
+        plt.text(0.5, 0.5, "数据量不足，无法拟合", ha='center', va='center',
+                 transform=plt.gca().transAxes, fontsize=20)
+        return
+    # 提取时间、经度、温度数据
+    t = np.array(df_8['Time'])  # 时间
+    x = np.array(df_8['Longitude_Radians'])  # 经度弧度制
+    temperature = np.array(df_8['Temperature'])  # 温度，因变量
+
+    # 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)
+
+    # 绘制拟合曲线
+    t_fixed = (start_day + end_day) / 2  # 窗口的中间时间
+    x_fit = np.linspace(min(x), max(x), 100)  # 生成用于绘制拟合曲线的x值
+    y_fit = u_func(x_fit, *popt, t=t_fixed)  # 计算拟合曲线的y值
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(x_fit, y_fit, label='拟合曲线', color='red', linewidth=2)
+    plt.xlabel('经度（弧度制）')
+    plt.ylabel('温度')
+    plt.title(f'时间窗口：{start_day} 到 {end_day}')
+    plt.legend()
+    plt.grid(True)

modules/saber/planetw_plot.py

@@ -105,7 +105,7 @@ def saber_planetw_plot(
     col = k_to_a[f'k={k}']
     plt.plot(x_values, fit_df[col].values)
     plt.suptitle(f'k = {k} 振幅图')
-    plt.xlabel('Day')
+    plt.xlabel('天')
     plt.ylabel('振幅')
 
     # 设置横坐标的动态调整

modules/saber/planetw_process.py

@@ -23,7 +23,7 @@ months = [
 def saber_planetw_process(
     year,
     target_h=70,
-        target_lat=60
+    target_lat=60
 ):
     # 定义文件路径模板和年份
 

modules/tidi/gravity_wave_year.py

@@ -869,8 +869,8 @@ class TidiGravityWPlotMonthly:
                    labels=months[::interval], rotation=45)  # rotation旋转可不加
 
         # 添加轴标签
-        plt.xlabel('Month')  # X轴标签
-        plt.ylabel('Height')  # Y轴标签
+        plt.xlabel('月')  # X轴标签
+        plt.ylabel('高度')  # Y轴标签
 
         # 显示图形
         # plt.show()
@@ -920,9 +920,9 @@ class TidiGravityWPlotMonthly:
         plt.plot(months, monthly_average, marker='o', linestyle='-', color='b')
 
         # 添加标题和标签
-        plt.title("Monthly Average ENERGY(log)")
-        plt.xlabel("Month")
-        plt.ylabel("Average Energy")
+        plt.title("月平均能量(结果取log)")
+        plt.xlabel("月")
+        plt.ylabel("平均能量")
         # 显示图表
         plt.xticks(rotation=45)  # 让月份标签更清晰可读
         plt.grid(True)

modules/tidi/planet_wave_daily.py

@@ -127,7 +127,7 @@ def TidiPlotPlanetWDaily(
     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')
-    plt.xlabel('Day')
+    plt.xlabel('天')
     plt.ylabel('振幅')
 
     # 设置横坐标的动态调整

ping.py

@@ -19,7 +19,7 @@ class WebSocketConfig:
 
 def setup_websocket(
     app: Quart,
-    url_prefix: str = '/ping/ws',
+    url_prefix: str = '/api/ping/ws',
     config: Optional[WebSocketConfig] = None
 ) -> None:
     """