sync
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10
backend.py
10
backend.py
@ -1,3 +1,4 @@
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import resource
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from matplotlib import pyplot as plt
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import cosmic
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import tidi
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@ -13,9 +14,14 @@ import matplotlib.font_manager as fm
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app = Quart(__name__)
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app = cors(app, send_origin_wildcard=True, allow_origin="*")
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# limit the whole app not to use over 8G ram
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# use resource module to do this
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# resource.setrlimit(resource.RLIMIT_AS, (8 * 1024 * 1024 * 1024, -1))
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plt.switch_backend('agg')
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fm.fontManager.addfont("./SimHei.ttf")
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@app.before_request
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def auth():
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# check for method
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@ -27,10 +33,12 @@ def auth():
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if code != "0101":
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return "Unauthorized", 401
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@app.route("/ping")
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def ping():
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return "pong"
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app.register_blueprint(balloon.balloon_module, url_prefix="/balloon")
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app.register_blueprint(radar.radar_module, url_prefix="/radar")
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app.register_blueprint(saber.saber_module, url_prefix="/saber")
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@ -46,6 +54,6 @@ if __name__ == '__main__':
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app.run("0.0.0.0", port=5000, debug=False)
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pass
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elif 'debug' in args:
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app.run("0.0.0.0",port=18200,debug=True)
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app.run("0.0.0.0", port=18200, debug=True)
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else:
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raise Exception("Invalied Mode")
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@ -3,6 +3,8 @@ from io import BytesIO
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from quart import Blueprint, request, send_file
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from matplotlib import pyplot as plt
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from CONSTANT import DATA_BASEPATH
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from saber.archive.gravity_plot import do_gravity_plot
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from saber.archive.gravity_process import process_gravity_data
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from saber.process import DataProcessor
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from saber.render import Renderer
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from saber.utils import *
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@ -19,12 +21,13 @@ saber_module = Blueprint('saber', __name__)
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# lat_range=lat_range, alt_range=alt_range, lambda_range=lambda_range, lvboin=lvboin)
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renderer = Renderer()
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def get_processer():
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lat_str = request.args.get("lat_range")
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if lat_str is None:
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lat_str = "30.0,40.0"
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lat_range = tuple(map(float, lat_str.split(",")))
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alt_str = request.args.get("alt_range")
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if alt_str is None:
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alt_str = "20.0,105.0"
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@ -36,7 +39,7 @@ def get_processer():
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lvboin = request.args.get("lvboin") == "true"
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if lvboin is None:
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lvboin = True
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_p = DataProcessor(
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lat_range=lat_range, alt_range=alt_range, lambda_range=lambda_range, lvboin=lvboin)
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return _p
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@ -46,7 +49,7 @@ async def extract_payload():
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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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return await send_file(buffer, mimetype="image/png")
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return await send_file(buffer, mimetype="image/png")
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_all_saber_files = glob.glob(f"{DATA_BASEPATH.saber}/**/**.nc", recursive=True)
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@ -78,7 +81,7 @@ async def do_plot_wave_day_fitting():
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processor = await run_sync(get_processer)()
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data = await run_sync(processor.process_day)(ncfile, int(day))
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await run_sync(renderer.plot_wave_fitting)(data, int(height))
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return await extract_payload()
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return await extract_payload()
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@saber_module.route("/render/day_fft_ifft_plot")
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@ -92,7 +95,7 @@ async def do_day_fft_ifft_plot():
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data = await run_sync(processor.process_day)(ncfile, int(day))
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# 高度滤波处理
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await run_sync(renderer.day_fft_ifft_plot)(wave_data=data, cycle_no=int(cycle_no))
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return await extract_payload()
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return await extract_payload()
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@saber_module.route("/render/day_cycle_power_wave_plot")
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@ -115,9 +118,9 @@ async def do_month_power_wave_plot():
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ncfile = await run_sync(data_nc_load)(path)
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processor = await run_sync(get_processer)()
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data =await run_sync(processor.process_month)(ncfile)
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data = await run_sync(processor.process_month)(ncfile)
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await run_sync(renderer.month_power_wave_plot)(wave_data=data, date_time=ncfile.date_time)
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return await extract_payload()
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return await extract_payload()
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@saber_module.route("/render/year_power_wave_plot")
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@ -129,3 +132,16 @@ async def do_year_power_wave_plot():
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])
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await run_sync(renderer.year_power_wave_plot)(year_wave=data)
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return await extract_payload()
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@saber_module.route("/render/gravity_year")
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async def do_gravity_year():
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year = request.args.get("year")
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T = request.args.get("T")
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year = int(year)
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T = int(T)
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if T not in [5, 10, 16]:
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raise ValueError("T must be in [5, 10, 16]")
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data = await run_sync(process_gravity_data)(year)
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await run_sync(do_gravity_plot)(data, T)
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return await extract_payload()
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@ -11,7 +11,7 @@ import matplotlib
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matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文
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matplotlib.rcParams['axes.unicode_minus'] = False # 正常显示负号
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# 读取一年的数据文件
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df = pd.read_csv(r'C:\pythonProject3\combined_data.txt', sep='\s+')
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# df = pd.read_csv(r'C:\pythonProject3\combined_data.txt', sep='\s+')
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# 设置初始参数
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# 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
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@ -26,92 +26,98 @@ bounds = (
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np.inf, np.inf, np.inf]) # 上界
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# 定义拟合函数
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def u_func(x, *params):
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a1, b1, a2, b2, a3, b3, a4, b4, a5, b5, a6, b6, a7, b7, a8, b8, a9, b9 = params
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return (
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a1 * np.sin((2 * np.pi / T) * t - 4 * x + b1)
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+ a2 * np.sin((2 * np.pi / T) * t - 3 * x + b2)
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+ a3 * np.sin((2 * np.pi / T) * t - 2 * x + b3)
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+ a4 * np.sin((2 * np.pi / T) * t - x + b4)
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+ a5 * np.sin((2 * np.pi / T) * t + b5)
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+ a6 * np.sin((2 * np.pi / T) * t + x + b6)
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+ a7 * np.sin((2 * np.pi / T) * t + 2 * x + b7)
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+ a8 * np.sin((2 * np.pi / T) * t + 3 * x + b8)
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+ a9 * np.sin((2 * np.pi / T) * t + 4 * x + b9)
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)
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def do_gravity_plot(df: pd.DataFrame, T=16):
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# 定义拟合函数
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def u_func(x, *params):
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a1, b1, a2, b2, a3, b3, a4, b4, a5, b5, a6, b6, a7, b7, a8, b8, a9, b9 = params
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return (
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a1 * np.sin((2 * np.pi / T) * t - 4 * x + b1)
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+ a2 * np.sin((2 * np.pi / T) * t - 3 * x + b2)
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+ a3 * np.sin((2 * np.pi / T) * t - 2 * x + b3)
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+ a4 * np.sin((2 * np.pi / T) * t - x + b4)
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+ a5 * np.sin((2 * np.pi / T) * t + b5)
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+ a6 * np.sin((2 * np.pi / T) * t + x + b6)
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+ a7 * np.sin((2 * np.pi / T) * t + 2 * x + b7)
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+ a8 * np.sin((2 * np.pi / T) * t + 3 * x + b8)
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+ a9 * np.sin((2 * np.pi / T) * t + 4 * x + b9)
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)
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# 用于存储拟合参数结果的列表
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all_fit_results = []
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# 用于存储拟合参数结果的列表
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all_fit_results = []
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# 设置最小数据量的阈值
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min_data_points = 36
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# 设置最小数据量的阈值
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min_data_points = 36
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# 进行多个时间窗口的拟合
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# T应该取5、10、16
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# 进行多个时间窗口的拟合
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# T应该取5、10、16
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# T = 16 # 设置 T，可以动态调整
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for start_day in range(0, 365-3*T): # 最后一个窗口为[351, 366]
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end_day = start_day + 3 * T # 每个窗口的结束时间为 start_day + 3*T
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T = 16 # 设置 T，可以动态调整
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for start_day in range(0, 365-3*T): # 最后一个窗口为[351, 366]
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end_day = start_day + 3 * T # 每个窗口的结束时间为 start_day + 3*T
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# 选择当前窗口的数据
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df_8 = df[(df['Time'] >= start_day) & (df['Time'] <= end_day)]
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# 选择当前窗口的数据
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df_8 = df[(df['Time'] >= start_day) & (df['Time'] <= end_day)]
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# 检查当前窗口的数据量
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if len(df_8) < min_data_points:
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# 输出数据量不足的警告
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print(f"数据量不足，无法拟合：{start_day} 到 {end_day}，数据点数量：{len(df_8)}")
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# 将拟合参数设置为 NaN
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all_fit_results.append([np.nan] * 18)
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continue # 跳过当前时间窗口，继续下一个窗口
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# 检查当前窗口的数据量
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if len(df_8) < min_data_points:
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# 输出数据量不足的警告
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print(f"数据量不足，无法拟合：{start_day} 到 {end_day}，数据点数量：{len(df_8)}")
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# 将拟合参数设置为 NaN
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all_fit_results.append([np.nan] * 18)
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continue # 跳过当前时间窗口，继续下一个窗口
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# 提取时间、经度、温度数据
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t = np.array(df_8['Time']) # 时间
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x = np.array(df_8['Longitude_Radians']) # 经度弧度制
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temperature = np.array(df_8['Temperature']) # 温度，因变量
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# 提取时间、经度、温度数据
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t = np.array(df_8['Time']) # 时间
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x = np.array(df_8['Longitude_Radians']) # 经度弧度制
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temperature = np.array(df_8['Temperature']) # 温度，因变量
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# 用T进行拟合
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popt, pcov = curve_fit(u_func, x, temperature,
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p0=initial_guess, bounds=bounds, maxfev=50000)
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# 用T进行拟合
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popt, pcov = curve_fit(u_func, x, temperature,
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p0=initial_guess, bounds=bounds, maxfev=50000)
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# 将拟合结果添加到列表中
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all_fit_results.append(popt)
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# 将拟合结果添加到列表中
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all_fit_results.append(popt)
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# 将结果转换为DataFrame
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columns = ['a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4',
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'a5', 'b5', 'a6', 'b6', 'a7', 'b7', 'a8', 'b8', 'a9', 'b9']
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fit_df = pd.DataFrame(all_fit_results, columns=columns) # fit_df即为拟合的参数汇总
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# 将结果转换为DataFrame
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columns = ['a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4',
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'a5', 'b5', 'a6', 'b6', 'a7', 'b7', 'a8', 'b8', 'a9', 'b9']
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fit_df = pd.DataFrame(all_fit_results, columns=columns) # fit_df即为拟合的参数汇总
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# -------------------------------画图----------------------------
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# a1-a9,对应波数-4、-3、-2、-1、0、1、2、3、4的行星波振幅
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a_columns = ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9']
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k_values = list(range(-4, 5)) # 从 -4 到 4
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# -------------------------------画图----------------------------
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# a1-a9,对应波数-4、-3、-2、-1、0、1、2、3、4的行星波振幅
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a_columns = ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9']
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k_values = list(range(-4, 5)) # 从 -4 到 4
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# 创建一个字典映射 k 值到 a_columns
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k_to_a = {f'k={k}': a for k, a in zip(k_values, a_columns)}
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# 创建一个字典映射 k 值到 a_columns
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k_to_a = {f'k={k}': a for k, a in zip(k_values, a_columns)}
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# 获取索引并转换为 numpy 数组
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x_values = fit_df.index.to_numpy()
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# 获取索引并转换为 numpy 数组
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x_values = fit_df.index.to_numpy()
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# 创建一个包含多个子图的图形
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fig, axs = plt.subplots(len(k_to_a), 1, figsize=(10, 2 * len(k_to_a)))
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# 对每一列生成独立的图
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for k, col in k_to_a.items():
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plt.figure(figsize=(8, 6)) # 创建新的图形
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plt.plot(x_values, fit_df[col].values)
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plt.title(f'{k} 振幅图')
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plt.xlabel('Day')
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plt.ylabel('振幅')
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# 对每一列生成独立的子图
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for ax, (k, col) in zip(axs, k_to_a.items()):
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ax.plot(x_values, fit_df[col].values)
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ax.set_title(f'{k} 振幅图')
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ax.set_xlabel('Day')
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ax.set_ylabel('振幅')
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# 设置横坐标的动态调整
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adjusted_x_values = x_values + (3 * T + 1) / 2
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if len(adjusted_x_values) > 50:
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step = 30
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tick_positions = adjusted_x_values[::step] # 选择每30个点
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tick_labels = [f'{int(val)}' for val in tick_positions]
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else:
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tick_positions = adjusted_x_values
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tick_labels = [f'{int(val)}' for val in tick_positions]
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# 设置横坐标的动态调整
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adjusted_x_values = x_values + (3 * T + 1) / 2
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if len(adjusted_x_values) > 50:
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step = 30
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tick_positions = adjusted_x_values[::step] # 选择每30个点
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tick_labels = [f'{int(val)}' for val in tick_positions]
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else:
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tick_positions = adjusted_x_values
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tick_labels = [f'{int(val)}' for val in tick_positions]
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plt.xticks(ticks=tick_positions, labels=tick_labels)
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ax.set_xticks(tick_positions)
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ax.set_xticklabels(tick_labels)
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plt.show() # 显示图形
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plt.tight_layout()
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# plt.show()
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# plt.show() # 显示图形
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@ -1,11 +1,13 @@
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# 高度、纬度可以指定，得到指定高度纬度下的数据，高度在101行，一般输入70、90、110，纬度在115行，纬度一般输入-20、30、60
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import os
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import netCDF4 as nc
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import numpy as np
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import netCDF4 as nc
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import numpy as np
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import matplotlib.pyplot as plt
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from datetime import datetime, timedelta
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import pandas as pd
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from scipy.optimize import curve_fit
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from scipy.optimize import least_squares
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@ -23,6 +25,13 @@ def process_gravity_data(year):
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base_path = DATA_BASEPATH.saber + \
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"/{year}/SABER_Temp_O3_{month}{year}_v2.0.nc"
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cache_path = DATA_BASEPATH.saber + \
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f"/{year}/gravity_result.parquet"
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if os.path.exists(cache_path):
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# load from cache
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return pd.read_parquet(cache_path)
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# 初始化空数组来存储拼接后的数据
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tplongitude = None
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tplatitude = None
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@ -165,7 +174,12 @@ def process_gravity_data(year):
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combined_data = np.column_stack(
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(closest_longitudes_radians, closest_times, closest_temperatures))
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return combined_data
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df = pd.DataFrame(combined_data, columns=[
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"Longitude_Radians", "Time", "Temperature"])
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# dump to cache
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df.to_parquet(cache_path)
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return df
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# 将数据保存为txt文件
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@ -11,6 +11,7 @@ from tidi.staged.plot import tidi_render
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tidi_module = Blueprint("Tidi", __name__)
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@tidi_module.route('/metadata')
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def get_all_years():
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res = glob.glob(f"{DATA_BASEPATH.tidi}/**/**.txt", recursive=True)
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@ -20,28 +21,35 @@ def get_all_years():
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"path": res
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}
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@tidi_module.route('/render/wave')
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async def render_wave():
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mode = request.args.get('mode')
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year = request.args.get('year')
|
||||
k = request.args.get('k')
|
||||
T = request.args.get('T')
|
||||
height = request.args.get('height')
|
||||
lat_range = request.args.get('lat_range')
|
||||
# like `0 ~ 5`
|
||||
year = int(year)
|
||||
k = int(k)
|
||||
T = int(T)
|
||||
|
||||
await run_sync(tidi_render)(mode, year, k, T)
|
||||
height = float(height)
|
||||
lat_range = tuple(map(int, lat_range.split('~')))
|
||||
|
||||
await run_sync(tidi_render)(mode, year, k, height, lat_range, T)
|
||||
buffer = BytesIO()
|
||||
plt.savefig(buffer, format="png")
|
||||
buffer.seek(0)
|
||||
|
||||
return await send_file(buffer, mimetype="image/png")
|
||||
|
||||
return await send_file(buffer, mimetype="image/png")
|
||||
|
||||
|
||||
@tidi_module.route('/render/month_stats_v1')
|
||||
async def render_stats_v1():
|
||||
year = request.args.get('year')
|
||||
year = int(year)
|
||||
|
||||
|
||||
plotter = await run_sync(TidiPlotv2)(year)
|
||||
await run_sync(plotter.plot_v1)()
|
||||
buffer = BytesIO()
|
||||
@ -49,11 +57,12 @@ async def render_stats_v1():
|
||||
buffer.seek(0)
|
||||
return await send_file(buffer, mimetype="image/png")
|
||||
|
||||
|
||||
@tidi_module.route('/render/month_stats_v2')
|
||||
async def render_stats_v2():
|
||||
year = request.args.get('year')
|
||||
year = int(year)
|
||||
|
||||
|
||||
plotter = await run_sync(TidiPlotv2)(year)
|
||||
await run_sync(plotter.plot_month)()
|
||||
buffer = BytesIO()
|
||||
|
||||
@ -1,8 +1,9 @@
|
||||
# 行星波
|
||||
|
||||
#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
|
||||
# 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 os
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from scipy.optimize import curve_fit
|
||||
@ -12,25 +13,34 @@ import matplotlib.pyplot as plt
|
||||
import matplotlib
|
||||
|
||||
from CONSTANT import DATA_BASEPATH
|
||||
from tidi.staged.process import tidi_process_idk
|
||||
# 设置中文显示和负号正常显示
|
||||
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 = [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
|
||||
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
|
||||
|
||||
[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 tidi_render(mode, year, k, T=10):
|
||||
def tidi_render(
|
||||
mode,
|
||||
year,
|
||||
k,
|
||||
input_height,
|
||||
lat_range=(0, 5),
|
||||
T=10
|
||||
):
|
||||
# 用于存储拟合参数结果的列表
|
||||
all_fit_results = []
|
||||
|
||||
@ -38,27 +48,37 @@ def tidi_render(mode, year, k, T=10):
|
||||
min_data_points = 36
|
||||
if mode != 'V_Zonal' and mode != 'V_Meridional':
|
||||
raise ValueError('mode must be V_Zonal or V_Meridional')
|
||||
if k not in list(range(-4, 5)):
|
||||
if k not in list(range(-4, 5)):
|
||||
raise ValueError('k must be in range(-4, 5)')
|
||||
df = pd.read_csv(f'{DATA_BASEPATH.tidi}/{year}/TIDI_{mode}_data.txt', sep='\s+')
|
||||
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):
|
||||
df = pd.read_parquet(data_cache_path)
|
||||
else:
|
||||
df = tidi_process_idk(
|
||||
year, input_height, lat_range
|
||||
)[mode]
|
||||
df.to_parquet(data_cache_path)
|
||||
|
||||
# 删除有 NaN 的行
|
||||
# V_Meridional
|
||||
df = df.dropna(subset=[mode])
|
||||
# 进行多个时间窗口的拟合
|
||||
#T应该取5、10、16
|
||||
# T应该取5、10、16
|
||||
# todo:对于5日波，滑动窗口选择15天
|
||||
|
||||
def u_func(x, *params):
|
||||
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)
|
||||
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)
|
||||
)
|
||||
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
|
||||
@ -80,17 +100,19 @@ def tidi_render(mode, year, k, T=10):
|
||||
temperature = np.array(df_8[mode]) # 经向风速，因变量
|
||||
|
||||
# 用T进行拟合
|
||||
popt, pcov = curve_fit(u_func, x, temperature, p0=initial_guess, bounds=bounds, maxfev=50000)
|
||||
popt, pcov = curve_fit(u_func, x, temperature,
|
||||
p0=initial_guess, bounds=bounds, maxfev=50000)
|
||||
|
||||
# 将拟合结果添加到列表中
|
||||
all_fit_results.append(popt)
|
||||
|
||||
# 将结果转换为DataFrame
|
||||
columns = ['a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4', 'a5', 'b5', 'a6', 'b6', 'a7', 'b7', 'a8', 'b8', 'a9', 'b9']
|
||||
columns = ['a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4',
|
||||
'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
|
||||
|
||||
# -------------------------------画图----------------------------
|
||||
#a1-a9,对应波数-4、-3、-2、-1、0、1、2、3、4的行星波振幅
|
||||
# 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']
|
||||
k_values = list(range(-4, 5)) # 从 -4 到 4
|
||||
|
||||
@ -121,7 +143,8 @@ def tidi_render(mode, year, k, T=10):
|
||||
plt.xticks(ticks=tick_positions, labels=tick_labels)
|
||||
|
||||
# plt.show() # 显示图形
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
tidi_render('V_Zonal', 2015, 1)
|
||||
tidi_render('V_Meridional', 2015, 1)
|
||||
@ -203,7 +226,3 @@ if __name__ == '__main__':
|
||||
# # 显示图形
|
||||
# plt.tight_layout()
|
||||
# plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -5,184 +5,201 @@ import pandas as pd
|
||||
|
||||
from CONSTANT import DATA_BASEPATH
|
||||
|
||||
#第156行代码，纬度可以自己选择，但是都是每5°做一个选择
|
||||
#70km-120km每2.5km一个，共21个，第六个就是82.5km高度的情况，高度也可以自己选择
|
||||
# 第156行代码，纬度可以自己选择，但是都是每5°做一个选择
|
||||
# 70km-120km每2.5km一个，共21个，第六个就是82.5km高度的情况，高度也可以自己选择
|
||||
|
||||
fixed_height_index = 6
|
||||
# 初始化空列表来存储每天的数据
|
||||
height_list = []
|
||||
lat_list = []
|
||||
lon_list = []
|
||||
vmeridional_list = []
|
||||
vzonal_list = []
|
||||
|
||||
# 文件路径
|
||||
base_path = f"{DATA_BASEPATH.tidi}/2022/"
|
||||
|
||||
# 循环读取从第1天到第365天的数据
|
||||
for day in range(1, 366): # 365天数据，确保循环次数为365
|
||||
# 构建文件名（使用三位数格式）
|
||||
day_str = f"{day:03d}" # 格式化数字为三位数，前面补0
|
||||
height_file = f"{day_str}_Height.mat"
|
||||
lat_file = f"{day_str}_Lat.mat"
|
||||
lon_file = f"{day_str}_Lon.mat"
|
||||
vmeridional_file = f"{day_str}_VMerdional.mat"
|
||||
vzonal_file = f"{day_str}_Vzonal.mat"
|
||||
|
||||
# 检查文件是否存在
|
||||
try:
|
||||
if not os.path.exists(os.path.join(base_path, height_file)):
|
||||
raise FileNotFoundError(f"{height_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, lat_file)):
|
||||
raise FileNotFoundError(f"{lat_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, lon_file)):
|
||||
raise FileNotFoundError(f"{lon_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, vmeridional_file)):
|
||||
raise FileNotFoundError(f"{vmeridional_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, vzonal_file)):
|
||||
raise FileNotFoundError(f"{vzonal_file} not found")
|
||||
|
||||
# 读取.mat文件
|
||||
height_data = loadmat(os.path.join(base_path, height_file))
|
||||
lat_data = loadmat(os.path.join(base_path, lat_file))
|
||||
lon_data = loadmat(os.path.join(base_path, lon_file))
|
||||
vmeridional_data = loadmat(os.path.join(base_path, vmeridional_file))
|
||||
vzonal_data = loadmat(os.path.join(base_path, vzonal_file))
|
||||
|
||||
# 将数据转换为DataFrame
|
||||
height_df = pd.DataFrame(height_data['Height'])
|
||||
lat_df = pd.DataFrame(lat_data['Lat'])
|
||||
lon_df = pd.DataFrame(lon_data['Lon'])
|
||||
vmeridional_df = pd.DataFrame(vmeridional_data['VMerdional'])
|
||||
vzonal_df = pd.DataFrame(vzonal_data['Vzonal'])
|
||||
|
||||
# 将每天的数据添加到列表中
|
||||
height_list.append(height_df)
|
||||
lat_list.append(lat_df)
|
||||
lon_list.append(lon_df)
|
||||
vmeridional_list.append(vmeridional_df)
|
||||
vzonal_list.append(vzonal_df)
|
||||
except FileNotFoundError as e:
|
||||
print(f"Error: {e}")
|
||||
continue # 跳过当前文件，继续处理其他文件
|
||||
|
||||
# 合并所有天的数据
|
||||
height_df = pd.concat(height_list, ignore_index=True)
|
||||
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
|
||||
vzonal_df = pd.concat(vzonal_list, axis=1) # 按列合并
|
||||
|
||||
# 初始化空列表来存储每天的数据
|
||||
UTime_list = []
|
||||
# 初始化累加的时间偏移量
|
||||
time_offset = 0
|
||||
|
||||
# 循环读取从第1天到第365天的数据
|
||||
for day in range(1, 366): # 365天数据
|
||||
# 构建文件名（使用三位数格式）
|
||||
day_str = f"{day:03d}" # 格式化数字为三位数，前面补0
|
||||
UTime_file = f"{day_str}_UTime.mat"
|
||||
|
||||
# 检查UTime文件是否存在
|
||||
try:
|
||||
if not os.path.exists(os.path.join(base_path, UTime_file)):
|
||||
raise FileNotFoundError(f"{UTime_file} not found")
|
||||
|
||||
# 读取.mat文件
|
||||
UTime_data = loadmat(os.path.join(base_path, UTime_file))
|
||||
|
||||
# 将数据转换为DataFrame
|
||||
UTime_df = pd.DataFrame(UTime_data['UTime'])
|
||||
|
||||
# 对UTime进行累加处理
|
||||
UTime_df += time_offset
|
||||
|
||||
# 将每天的数据添加到列表中
|
||||
UTime_list.append(UTime_df)
|
||||
|
||||
# 更新时间偏移量，为下一天增加24小时
|
||||
time_offset += 24
|
||||
except FileNotFoundError as e:
|
||||
print(f"Error: {e}")
|
||||
continue # 跳过当前文件，继续处理其他文件
|
||||
|
||||
# 合并所有天的数据
|
||||
UTime_df = pd.concat(UTime_list, ignore_index=True)
|
||||
# 将UTime_df的单位从小时转换为天（除以24）
|
||||
UTime_df = UTime_df / 24
|
||||
HEIGHTS_CONSTANT = [
|
||||
70, 72.5, 75, 77.5, 80, 82.5, 85, 87.5, 90, 92.5, 95, 97.5, 100, 102.5, 105, 107.5, 110, 112.5, 115, 117.5, 120
|
||||
]
|
||||
|
||||
|
||||
# 获取固定高度下的纬度数据
|
||||
fixed_height_lat = lat_df.iloc[:, 0].values
|
||||
# 获取固定高度下的经度数据
|
||||
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
|
||||
# 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）
|
||||
fixed_height_utime = UTime_df.iloc[:, 0].values
|
||||
# 将这些数据整合到一个新的DataFrame（可选操作，方便后续查看等）
|
||||
combined_data = pd.DataFrame({
|
||||
'Latitude': fixed_height_lat,
|
||||
'Longitude': fixed_height_lon,
|
||||
'V_Meridional': fixed_height_vmeridional,
|
||||
'V_Zonal': fixed_height_vzonal,
|
||||
'UTime': fixed_height_utime
|
||||
})
|
||||
def tidi_process_idk(
|
||||
year,
|
||||
input_height: float = 82.5,
|
||||
lat_range: tuple = (0, 5),
|
||||
):
|
||||
# 初始化空列表来存储每天的数据
|
||||
if lat_range[1] - lat_range[0] != 5:
|
||||
raise ValueError("lat_range must be 5°")
|
||||
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)
|
||||
height_list = []
|
||||
lat_list = []
|
||||
lon_list = []
|
||||
vmeridional_list = []
|
||||
vzonal_list = []
|
||||
|
||||
# 简单打印下整合后的数据前几行查看下内容
|
||||
print(combined_data)
|
||||
# 文件路径
|
||||
base_path = f"{DATA_BASEPATH.tidi}/{year}/"
|
||||
# if dir not exist, raise error
|
||||
if not os.path.exists(base_path):
|
||||
raise FileNotFoundError(f"{base_path} not found")
|
||||
|
||||
# 确定纬度和经度的范围
|
||||
lat_min, lat_max = np.min(fixed_height_lat), np.max(fixed_height_lat)
|
||||
lon_min, lon_max = np.min(fixed_height_lon), np.max(fixed_height_lon)
|
||||
# 循环读取从第1天到第365天的数据
|
||||
for day in range(1, 366): # 365天数据，确保循环次数为365
|
||||
# 构建文件名（使用三位数格式）
|
||||
day_str = f"{day:03d}" # 格式化数字为三位数，前面补0
|
||||
height_file = f"{day_str}_Height.mat"
|
||||
lat_file = f"{day_str}_Lat.mat"
|
||||
lon_file = f"{day_str}_Lon.mat"
|
||||
vmeridional_file = f"{day_str}_VMerdional.mat"
|
||||
vzonal_file = f"{day_str}_Vzonal.mat"
|
||||
|
||||
# 计算网格的边界
|
||||
lat_bins = np.linspace(lat_min, lat_max, 10) # 纬度分为6个网格，需要7个边界
|
||||
lon_bins = np.linspace(lon_min, lon_max, 13) # 经度分为20个网格，需要21个边界
|
||||
# 检查文件是否存在
|
||||
try:
|
||||
if not os.path.exists(os.path.join(base_path, height_file)):
|
||||
raise FileNotFoundError(f"{height_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, lat_file)):
|
||||
raise FileNotFoundError(f"{lat_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, lon_file)):
|
||||
raise FileNotFoundError(f"{lon_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, vmeridional_file)):
|
||||
raise FileNotFoundError(f"{vmeridional_file} not found")
|
||||
if not os.path.exists(os.path.join(base_path, vzonal_file)):
|
||||
raise FileNotFoundError(f"{vzonal_file} not found")
|
||||
|
||||
# 将数据分配到网格中
|
||||
grid_data = []
|
||||
for i in range(len(fixed_height_lat)):
|
||||
lat_idx = np.digitize(fixed_height_lat[i], lat_bins) - 1 # 找到纬度所在的网格索引
|
||||
lon_idx = np.digitize(fixed_height_lon[i], lon_bins) - 1 # 找到经度所在的网格索引
|
||||
grid_idx = lat_idx * 12 + lon_idx # 计算网格的唯一索引（9x12=108）
|
||||
grid_data.append([fixed_height_lat[i], fixed_height_lon[i], fixed_height_vmeridional[i], fixed_height_vzonal[i], fixed_height_utime[i], grid_idx])
|
||||
# 将网格数据转换为DataFrame
|
||||
grid_df = pd.DataFrame(grid_data, columns=['Latitude', 'Longitude', 'V_Meridional', 'V_Zonal', 'UTime', 'Grid_Index'])
|
||||
# 打印网格数据的前几行查看
|
||||
print(grid_df.head())
|
||||
# 筛选纬度在 0 到 5° 范围内的数据
|
||||
filtered_df = grid_df[(grid_df['Latitude'] >= 0) & (grid_df['Latitude'] <= 5)]
|
||||
# 将经度从度转换为弧度
|
||||
filtered_df['Longitude_Radians'] = np.radians(filtered_df['Longitude'])
|
||||
# 选择需要输出的列
|
||||
output_columns = ['Longitude_Radians', 'UTime', 'V_Zonal']
|
||||
# 将数据输出为txt文件
|
||||
output_file = base_path+'TIDI_V_Zonal_data.txt'#经向风速分量
|
||||
filtered_df[output_columns].to_csv(output_file, sep='\t', index=False)
|
||||
# 打印结果确认
|
||||
print(f"数据已保存到 {output_file}")
|
||||
# 选择需要输出的列
|
||||
output_columns1 = ['Longitude_Radians', 'UTime', 'V_Meridional']
|
||||
# 将数据输出为txt文件
|
||||
output_file1 = base_path+'TIDI_V_Meridional_data.txt'#纬向风速分量
|
||||
filtered_df[output_columns1].to_csv(output_file1, sep='\t', index=False)
|
||||
# 打印结果确认
|
||||
print(f"数据已保存到 {output_file1}")
|
||||
# 读取.mat文件
|
||||
height_data = loadmat(os.path.join(base_path, height_file))
|
||||
lat_data = loadmat(os.path.join(base_path, lat_file))
|
||||
lon_data = loadmat(os.path.join(base_path, lon_file))
|
||||
vmeridional_data = loadmat(
|
||||
os.path.join(base_path, vmeridional_file))
|
||||
vzonal_data = loadmat(os.path.join(base_path, vzonal_file))
|
||||
|
||||
# 将数据转换为DataFrame
|
||||
height_df = pd.DataFrame(height_data['Height'])
|
||||
lat_df = pd.DataFrame(lat_data['Lat'])
|
||||
lon_df = pd.DataFrame(lon_data['Lon'])
|
||||
vmeridional_df = pd.DataFrame(vmeridional_data['VMerdional'])
|
||||
vzonal_df = pd.DataFrame(vzonal_data['Vzonal'])
|
||||
|
||||
# 将每天的数据添加到列表中
|
||||
height_list.append(height_df)
|
||||
lat_list.append(lat_df)
|
||||
lon_list.append(lon_df)
|
||||
vmeridional_list.append(vmeridional_df)
|
||||
vzonal_list.append(vzonal_df)
|
||||
except FileNotFoundError as e:
|
||||
print(f"Error: {e}")
|
||||
continue # 跳过当前文件，继续处理其他文件
|
||||
|
||||
# 合并所有天的数据
|
||||
height_df = pd.concat(height_list, ignore_index=True)
|
||||
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
|
||||
vzonal_df = pd.concat(vzonal_list, axis=1) # 按列合并
|
||||
|
||||
# 初始化空列表来存储每天的数据
|
||||
UTime_list = []
|
||||
# 初始化累加的时间偏移量
|
||||
time_offset = 0
|
||||
|
||||
# 循环读取从第1天到第365天的数据
|
||||
for day in range(1, 366): # 365天数据
|
||||
# 构建文件名（使用三位数格式）
|
||||
day_str = f"{day:03d}" # 格式化数字为三位数，前面补0
|
||||
UTime_file = f"{day_str}_UTime.mat"
|
||||
|
||||
# 检查UTime文件是否存在
|
||||
try:
|
||||
if not os.path.exists(os.path.join(base_path, UTime_file)):
|
||||
raise FileNotFoundError(f"{UTime_file} not found")
|
||||
|
||||
# 读取.mat文件
|
||||
UTime_data = loadmat(os.path.join(base_path, UTime_file))
|
||||
|
||||
# 将数据转换为DataFrame
|
||||
UTime_df = pd.DataFrame(UTime_data['UTime'])
|
||||
|
||||
# 对UTime进行累加处理
|
||||
UTime_df += time_offset
|
||||
|
||||
# 将每天的数据添加到列表中
|
||||
UTime_list.append(UTime_df)
|
||||
|
||||
# 更新时间偏移量，为下一天增加24小时
|
||||
time_offset += 24
|
||||
except FileNotFoundError as e:
|
||||
print(f"Error: {e}")
|
||||
continue # 跳过当前文件，继续处理其他文件
|
||||
|
||||
# 合并所有天的数据
|
||||
UTime_df = pd.concat(UTime_list, ignore_index=True)
|
||||
# 将UTime_df的单位从小时转换为天（除以24）
|
||||
UTime_df = UTime_df / 24
|
||||
|
||||
# 获取固定高度下的纬度数据
|
||||
fixed_height_lat = lat_df.iloc[:, 0].values
|
||||
# 获取固定高度下的经度数据
|
||||
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
|
||||
# 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）
|
||||
fixed_height_utime = UTime_df.iloc[:, 0].values
|
||||
# 将这些数据整合到一个新的DataFrame（可选操作，方便后续查看等）
|
||||
combined_data = pd.DataFrame({
|
||||
'Latitude': fixed_height_lat,
|
||||
'Longitude': fixed_height_lon,
|
||||
'V_Meridional': fixed_height_vmeridional,
|
||||
'V_Zonal': fixed_height_vzonal,
|
||||
'UTime': fixed_height_utime
|
||||
})
|
||||
|
||||
# 简单打印下整合后的数据前几行查看下内容
|
||||
print(combined_data)
|
||||
|
||||
# 确定纬度和经度的范围
|
||||
lat_min, lat_max = np.min(fixed_height_lat), np.max(fixed_height_lat)
|
||||
lon_min, lon_max = np.min(fixed_height_lon), np.max(fixed_height_lon)
|
||||
|
||||
# 计算网格的边界
|
||||
lat_bins = np.linspace(lat_min, lat_max, 10) # 纬度分为6个网格，需要7个边界
|
||||
lon_bins = np.linspace(lon_min, lon_max, 13) # 经度分为20个网格，需要21个边界
|
||||
|
||||
# 将数据分配到网格中
|
||||
grid_data = []
|
||||
for i in range(len(fixed_height_lat)):
|
||||
lat_idx = np.digitize(fixed_height_lat[i], lat_bins) - 1 # 找到纬度所在的网格索引
|
||||
lon_idx = np.digitize(fixed_height_lon[i], lon_bins) - 1 # 找到经度所在的网格索引
|
||||
grid_idx = lat_idx * 12 + lon_idx # 计算网格的唯一索引（9x12=108）
|
||||
grid_data.append([fixed_height_lat[i], fixed_height_lon[i], fixed_height_vmeridional[i],
|
||||
fixed_height_vzonal[i], fixed_height_utime[i], grid_idx])
|
||||
# 将网格数据转换为DataFrame
|
||||
grid_df = pd.DataFrame(grid_data, columns=[
|
||||
'Latitude', 'Longitude', 'V_Meridional', 'V_Zonal', 'UTime', 'Grid_Index'])
|
||||
# 打印网格数据的前几行查看
|
||||
print(grid_df.head())
|
||||
# 筛选纬度在 0 到 5° 范围内的数据
|
||||
lat_range_down, lat_range_upper = lat_range
|
||||
|
||||
filtered_df = grid_df[(grid_df['Latitude'] >= lat_range_down) &
|
||||
(grid_df['Latitude'] <= lat_range_upper)]
|
||||
# 将经度从度转换为弧度
|
||||
filtered_df['Longitude_Radians'] = np.radians(filtered_df['Longitude'])
|
||||
# 选择需要输出的列
|
||||
output_columns = ['Longitude_Radians', 'UTime', 'V_Zonal']
|
||||
# 将数据输出为txt文件
|
||||
output_file = base_path+'TIDI_V_Zonal_data.txt' # 经向风速分量
|
||||
filtered_df[output_columns].to_csv(output_file, sep='\t', index=False)
|
||||
|
||||
zonal_data = filtered_df[output_columns]
|
||||
# 选择需要输出的列
|
||||
output_columns1 = ['Longitude_Radians', 'UTime', 'V_Meridional']
|
||||
# 将数据输出为txt文件
|
||||
merdi_data = filtered_df[output_columns1]
|
||||
|
||||
return {
|
||||
"V_Zonal": zonal_data,
|
||||
"V_Meridional": merdi_data
|
||||
}
|
||||
# output_file1 = base_path+'TIDI_V_Meridional_data.txt' # 纬向风速分量
|
||||
# final_result.to_csv(output_file1, sep='\t', index=False)
|
||||
# # 打印结果确认
|
||||
# print(f"数据已保存到 {output_file1}")
|
||||
|
||||
|
||||
# # 对网格数据按照Grid_Index进行分组，并计算每个网格的统计数据
|
||||
@ -209,4 +226,4 @@ print(f"数据已保存到 {output_file1}")
|
||||
# print("无法拟合行星波")
|
||||
# else:
|
||||
# # 打印筛选后的网格数据的前几行查看
|
||||
# print(filtered_grid_stats.head())
|
||||
# print(filtered_grid_stats.head())
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user