sync
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@ -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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@ -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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@ -19,6 +21,7 @@ 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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@ -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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@ -26,6 +26,7 @@ bounds = (
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np.inf, np.inf, np.inf]) # 上界
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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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@ -41,7 +42,6 @@ def u_func(x, *params):
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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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@ -51,7 +51,7 @@ 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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T = 16 # 设置 T，可以动态调整
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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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@ -94,13 +94,15 @@ 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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# 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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# 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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@ -112,6 +114,10 @@ for k, col in k_to_a.items():
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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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@ -20,23 +21,30 @@ 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')
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k = request.args.get('k')
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T = request.args.get('T')
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height = request.args.get('height')
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lat_range = request.args.get('lat_range')
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# like `0 ~ 5`
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year = int(year)
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k = int(k)
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T = int(T)
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height = float(height)
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lat_range = tuple(map(int, lat_range.split('~')))
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await run_sync(tidi_render)(mode, year, k, T)
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await run_sync(tidi_render)(mode, year, k, height, lat_range, T)
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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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@tidi_module.route('/render/month_stats_v1')
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async def render_stats_v1():
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year = request.args.get('year')
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@ -49,6 +57,7 @@ async def render_stats_v1():
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buffer.seek(0)
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return await send_file(buffer, mimetype="image/png")
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@tidi_module.route('/render/month_stats_v2')
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async def render_stats_v2():
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year = request.args.get('year')
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@ -3,6 +3,7 @@
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# 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
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# 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
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import os
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import pandas as pd
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import numpy as np
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from scipy.optimize import curve_fit
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@ -12,25 +13,34 @@ import matplotlib.pyplot as plt
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import matplotlib
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from CONSTANT import DATA_BASEPATH
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from tidi.staged.process import tidi_process_idk
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# 设置中文显示和负号正常显示
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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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# 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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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
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initial_guess = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
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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
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# 设置参数界限
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bounds = (
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[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
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[0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf,
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0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf], # 下界
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[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,
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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 tidi_render(mode, year, k, T=10):
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def tidi_render(
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mode,
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year,
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k,
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input_height,
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lat_range=(0, 5),
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T=10
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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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@ -40,13 +50,23 @@ def tidi_render(mode, year, k, T=10):
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raise ValueError('mode must be V_Zonal or V_Meridional')
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if k not in list(range(-4, 5)):
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raise ValueError('k must be in range(-4, 5)')
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df = pd.read_csv(f'{DATA_BASEPATH.tidi}/{year}/TIDI_{mode}_data.txt', sep='\s+')
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data_cache_path = f'{DATA_BASEPATH.tidi}/{year}/TIDI_{mode}h{input_height}r{lat_range[0]}-{lat_range[1]}_data.parquet'
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# if cache exist, read from cache
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if os.path.exists(data_cache_path):
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df = pd.read_parquet(data_cache_path)
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else:
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df = tidi_process_idk(
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year, input_height, lat_range
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)[mode]
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df.to_parquet(data_cache_path)
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# 删除有 NaN 的行
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# V_Meridional
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df = df.dropna(subset=[mode])
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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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# todo:对于5日波，滑动窗口选择15天
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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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@ -80,13 +100,15 @@ def tidi_render(mode, year, k, T=10):
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temperature = np.array(df_8[mode]) # 经向风速，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, p0=initial_guess, bounds=bounds, maxfev=50000)
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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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# 将结果转换为DataFrame
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columns = ['a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4', 'a5', 'b5', 'a6', 'b6', 'a7', 'b7', 'a8', 'b8', 'a9', 'b9']
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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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@ -122,6 +144,7 @@ def tidi_render(mode, year, k, T=10):
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# plt.show() # 显示图形
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if __name__ == '__main__':
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tidi_render('V_Zonal', 2015, 1)
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tidi_render('V_Meridional', 2015, 1)
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@ -203,7 +226,3 @@ if __name__ == '__main__':
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# # 显示图形
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# plt.tight_layout()
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# plt.show()
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@ -8,8 +8,22 @@ from CONSTANT import DATA_BASEPATH
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# 第156行代码，纬度可以自己选择，但是都是每5°做一个选择
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# 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
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fixed_height_index = 6
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HEIGHTS_CONSTANT = [
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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
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]
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def tidi_process_idk(
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year,
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input_height: float = 82.5,
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lat_range: tuple = (0, 5),
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):
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# 初始化空列表来存储每天的数据
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if lat_range[1] - lat_range[0] != 5:
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raise ValueError("lat_range must be 5°")
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if input_height not in HEIGHTS_CONSTANT:
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raise ValueError(f"input_height must be in {HEIGHTS_CONSTANT}")
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fixed_height_index = HEIGHTS_CONSTANT.index(input_height)
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height_list = []
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lat_list = []
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lon_list = []
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@ -17,7 +31,10 @@ vmeridional_list = []
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vzonal_list = []
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# 文件路径
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base_path = f"{DATA_BASEPATH.tidi}/2022/"
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base_path = f"{DATA_BASEPATH.tidi}/{year}/"
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# if dir not exist, raise error
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if not os.path.exists(base_path):
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raise FileNotFoundError(f"{base_path} not found")
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# 循环读取从第1天到第365天的数据
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for day in range(1, 366): # 365天数据，确保循环次数为365
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@ -46,7 +63,8 @@ for day in range(1, 366): # 365天数据，确保循环次数为365
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height_data = loadmat(os.path.join(base_path, height_file))
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lat_data = loadmat(os.path.join(base_path, lat_file))
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lon_data = loadmat(os.path.join(base_path, lon_file))
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vmeridional_data = loadmat(os.path.join(base_path, vmeridional_file))
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vmeridional_data = loadmat(
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os.path.join(base_path, vmeridional_file))
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vzonal_data = loadmat(os.path.join(base_path, vzonal_file))
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# 将数据转换为DataFrame
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@ -112,7 +130,6 @@ UTime_df = pd.concat(UTime_list, ignore_index=True)
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# 将UTime_df的单位从小时转换为天（除以24）
|
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UTime_df = UTime_df / 24
|
||||
|
||||
|
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# 获取固定高度下的纬度数据
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fixed_height_lat = lat_df.iloc[:, 0].values
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# 获取固定高度下的经度数据
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@ -149,13 +166,18 @@ for i in range(len(fixed_height_lat)):
|
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lat_idx = np.digitize(fixed_height_lat[i], lat_bins) - 1 # 找到纬度所在的网格索引
|
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lon_idx = np.digitize(fixed_height_lon[i], lon_bins) - 1 # 找到经度所在的网格索引
|
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grid_idx = lat_idx * 12 + lon_idx # 计算网格的唯一索引（9x12=108）
|
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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])
|
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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'])
|
||||
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)]
|
||||
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
|
||||
@ -163,26 +185,21 @@ 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}")
|
||||
|
||||
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
|
||||
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}")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user