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我对python很陌生。在过去的两天里，我一直试图弄清楚如何使用 matplotlib 缩放 3d 图（天线辐射图案）的颜色。看起来缩放在 xyz 轴之一中起作用，但在缩放从原点（半径）开始时不起作用。非常感谢任何帮助。

这不是我的代码，但我发现它非常有用。

这是代码：

  • 值是从excel文档中读取的

  • 如您所见，我正在尝试使用此命令colors=plt.cm.jet((R)/(Rmax))，但它不起作用。

              import pandas as pd
          import numpy as np
          import matplotlib.pyplot as plt
          import mpl_toolkits.mplot3d.axes3d as axes3d

          # Read data file and plot
          df = pd.read_csv('EIRP_Data.csv') #henter data fra Excel

          theta1d = df['Theta']                  
          theta1d = np.array(theta1d);
          theta2d = theta1d.reshape([37,73]) #"Theta" kolonen blir hentet ut, satt i numpy array og gjort om til 2d array

          phi1d = df['Phi']
          phi1d = np.array(phi1d);
          phi2d = phi1d.reshape([37,73]) #"Phi" kolonen blir hentet ut, satt i numpy array og gjort om til 2d Array

          power1d = df['Power']
          power1d = np.array(power1d);
          power2d = power1d.reshape([37,73]) #"Power" kolonen blir hentet ut, satt i numpy array og gjort om til 2d array

          THETA = np.deg2rad(theta2d)
          PHI = np.deg2rad(phi2d)
          R = power2d
          Rmax = np.max(R)
          Rmin = np.min(R)
          N = R / Rmax

          #Gjør om polar til kartesisk
          X = R * np.sin(THETA) * np.cos(PHI) 
          Y = R * np.sin(THETA) * np.sin(PHI)
          Z = R * np.cos(THETA)

          fig = plt.figure()

          #plot spesifikasjoner/settings
          ax = fig.add_subplot(1,1,1, projection='3d') 
          ax.grid(True)
          ax.axis('on')
          ax.set_xlabel('X')
          ax.set_ylabel('Y')
          ax.set_zlabel('Z')
          ax.set_xticklabels([]) 
          ax.set_yticklabels([])
          ax.set_zticklabels([])

          #colors =plt.cm.jet( (X.max()-X)/float((X-X.min()).max()))
          colors =plt.cm.jet( (R)/(Rmax) )
          ax.plot_surface(
              X, Y, Z, rstride=1, cstride=1, facecolors=colors,
              linewidth=0, antialiased=True, alpha=0.5, zorder = 0.5)

          ax.view_init(azim=300, elev = 30)

          # Add Spherical Grid
          phi ,theta = np.linspace(0, 2 * np.pi, 40), np.linspace(0, np.pi, 40)
          PHI, THETA  = np.meshgrid(phi,theta)
          R = Rmax
          X = R * np.sin(THETA) * np.cos(PHI)
          Y = R * np.sin(THETA) * np.sin(PHI)
          Z = R * np.cos(THETA)

          ax.plot_wireframe(X, Y, Z, linewidth=0.5, rstride=20, cstride=20)

          plt.show()

当前情节

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1 回答 1

0

我有以下代码来考虑色标的半径。成功的方法是使用颜色图来获取标准化 R 的颜色值（这里是颜色权重）。

X = np.ones((phiSize, thetaSize))                                                                           # Prepare arrays to hold the cartesian coordinate data.
Y = np.ones((phiSize, thetaSize))
Z = np.ones((phiSize, thetaSize))
color_weight = np.ones((phiSize, thetaSize))

min_dBi = np.abs(df["dBi"].min())

for phi_idx, phi in enumerate(np.unique(df["Phi"])):
    for theta_idx, theta in enumerate(np.unique(df["Theta"])):
        e = df.query(f"Phi=={phi} and Theta=={theta}").iloc[0]["dBi"]
        e = min_dBi + e # so we dont have any negative numbers
        xe, ye, ze = sph2cart1(e, math.radians(theta), math.radians(phi))                                   # Calculate cartesian coordinates

        X[phi_idx, theta_idx] = xe                                                                                  # Store cartesian coordinates
        Y[phi_idx, theta_idx] = ye
        Z[phi_idx, theta_idx] = ze
        color_weight[phi_idx, theta_idx] = e

ax.plot_surface(X, Y, Z, color='b')                                                                         # Plot surface
plt.ylabel('Y')
plt.xlabel('X')                                                                                             # Plot formatting
plt.show()

我的数据中唯一的 phis 和 theta 的数量phisize。thetaSize我的天线的 dBi 存储在 dBi 列中的 pandas 中。

于 2020-10-05T08:53:32.833 回答