176 lines
5.9 KiB
Python
176 lines
5.9 KiB
Python
# 探空气球只有重力波
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import numpy as np
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from modules.balloon.extract_wave import calculate_wave, is_terrain_wave
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def plot_once(data: pd.DataFrame, path: str, lat: float, g: float):
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wave = calculate_wave(
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data[(data["alt"] >= 15) & (data["alt"] <= 25)], lat, g)
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if len(wave) == 0:
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return []
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c = is_terrain_wave(data, lat, g)
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a, b, omega_upper, w_f, λ_z, λ_h, c_x, c_y, c_z, Ek, E_p, MFu1, MFv1, params_u, params_v, params_T = wave[
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:16]
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ucmp, vcmp, temp, height, poly_ucmp, poly_vcmp, _, poly_temp, residual_ucmp, residual_vcmp, residual_temp, u_fit, v_fit, T_fit, uh, _ = wave[
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16:]
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plt.figure(figsize=(16, 10))
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# 二阶多项式拟合曲线
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# u 风扰动量
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plt.subplot(2, 6, 1)
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plt.plot(ucmp, height, label="", linestyle="-")
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plt.plot(poly_ucmp(height), height, label="")
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plt.ylabel("Height(km)")
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plt.xlabel("Zonal wind (m/s)")
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plt.grid(True)
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# v 风扰动量
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plt.subplot(2, 6, 2)
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plt.plot(vcmp, height, label="", linestyle="-")
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plt.plot(poly_vcmp(height), height, label="")
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plt.ylabel("Height(km)")
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plt.xlabel("Meridional wind (m/s)")
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plt.grid(True)
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plt.title("观测的二阶多项式拟合", fontsize=16)
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# 温度扰动量
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plt.subplot(2, 6, 3)
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plt.plot(temp, height, label="", linestyle="-")
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plt.plot(poly_temp(height), height, label="")
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plt.ylabel("Height(km)")
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plt.xlabel("Temperature(K)")
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plt.grid(True)
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# 绘制正弦拟合图
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# u 风扰动量
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plt.subplot(2, 6, 4)
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plt.plot(residual_ucmp, height, label="", marker="o", linestyle="None")
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plt.plot(u_fit, height, label="")
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plt.ylabel("Height(km)")
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plt.xlabel("Zonal wind (m/s)")
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plt.grid(True)
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# v 风扰动量
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plt.subplot(2, 6, 5)
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plt.plot(residual_vcmp, height, label="", marker="o", linestyle="None")
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plt.plot(v_fit, height, label="")
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plt.ylabel("Height(km)")
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plt.xlabel("Meridional wind (m/s)")
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plt.grid(True)
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plt.title("扰动分量的正弦波拟合", fontsize=16)
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# 温度扰动量
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plt.subplot(2, 6, 6)
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plt.plot(residual_temp, height, label="", marker="o", linestyle="None")
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plt.plot(T_fit, height, label="")
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plt.ylabel("Height(km)")
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plt.xlabel("Temperature(K)")
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plt.grid(True)
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# 标记3个特定高度
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specified_heights = [15, 15.05, 15.1]
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markers = ["*", "D", "^"] # 星号，菱形，三角形
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# a. U-V矢量曲线
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plt.subplot(2, 2, 3)
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plt.plot(u_fit, v_fit) # 绘制U-V曲线
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for h, marker in zip(specified_heights, markers):
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index = np.abs(height - h).argmin() # 找到离指定高度最近的索引
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plt.scatter(u_fit[index], v_fit[index],
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marker=marker, s=100, label=f"{h} km")
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# 设置坐标轴范围和比例
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plt.xlim(-8, 8)
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plt.ylim(-4, 4)
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# plt.gca().set_aspect("equal") # 确保横纵坐标刻度长短一致
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plt.axvline(0, color="gray", linestyle="--")
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plt.axhline(0, color="gray", linestyle="--")
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plt.xlabel("Zonal Wind (m/s)")
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plt.ylabel("Meridional Wind (m/s)")
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plt.legend() # 显示图例
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plt.grid(True) # 显示网格线
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plt.text(0.05, 1, "(a)", transform=plt.gca().transAxes,
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fontsize=14, verticalalignment="top")
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plt.title("径向风-纬向风矢量图")
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# b. 水平风-温度的矢端曲线
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plt.subplot(2, 2, 4)
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plt.plot(uh, T_fit) # 绘制水平风-温度曲线
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for h, marker in zip(specified_heights, markers):
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index = np.abs(height - h).argmin() # 找到离指定高度最近的索引
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plt.scatter(uh[index], T_fit[index],
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marker=marker, s=100, label=f"{h} km")
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# 设置坐标轴范围和比例
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plt.xlim(-4, 4)
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plt.ylim(-2, 2)
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# plt.gca().set_aspect("equal") # 确保横纵坐标刻度长短一致
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plt.axvline(0, color="gray", linestyle="--")
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plt.axhline(0, color="gray", linestyle="--")
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plt.xlabel("Horizontal wind (m/s)")
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plt.ylabel("Temp (K)")
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plt.legend()
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plt.grid(True)
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plt.text(0.05, 1, "(b)", transform=plt.gca().transAxes,
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fontsize=14, verticalalignment="top")
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plt.title("温度-水平风矢量图")
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# 设置中文显示和负号正常显示
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plt.rcParams["font.sans-serif"] = ["SimHei"] # 显示中文
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plt.rcParams["axes.unicode_minus"] = False # 正常显示负号
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plt.tight_layout()
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# plt.savefig(path, transparent=True)
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plt.savefig(path)
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plt.close()
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"""
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地形重力波 c 是否是地形重力波
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垂直传播方向 a 1上
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水平传播方向 b degree
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本征（固有）频率 round(omega_upper, 6) rad/s
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本征周期 round(2 * np.pi / omega_upper / 3600, 2) h
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本征频率与固有频率的比值（即长短轴之比） round(w_f, 2)
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垂直波长 round(λ_z, 2) km
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水平波长 round(λ_h, 2) km
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纬向本征相速度 round(c_x, 2) m/s
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经向本征相速度 round(c_y, 2) m/s
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垂直本征相速度 round(c_z, 3) m/s
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波动能 round(Ek, 4) J/kg
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势能 round(E_p, 4) J/kg
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纬向动量通量 round(MFu1, 4) mPa
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经向动量通量 round(MFv1, 4) mPa
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纬向风扰动振幅 round(params_u, 2) m/s
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经向风扰动振幅 round(params_v, 2) m/s
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温度扰动振幅 round(params_T, 2) K
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"""
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return [
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c,
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a,
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b,
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round(omega_upper, 6),
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round(2 * np.pi / omega_upper / 3600, 2),
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round(w_f, 2),
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round(λ_z, 2),
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round(λ_h, 2),
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round(c_x, 2),
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round(c_y, 2),
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round(c_z, 3),
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round(Ek, 4),
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round(E_p, 4),
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round(MFu1, 4),
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round(MFv1, 4),
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round(params_u, 2),
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round(params_v, 2),
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round(params_T, 2),
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]
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