Python，球形打印-颜色缩放

我对python很陌生。在过去的两天里，我一直在想如何用matplotlib缩放3d绘图（天线辐射方向图）的颜色。看起来缩放在其中一个xyz轴上起作用，但在缩放从原点（半径）开始时不起作用。非常感谢您的帮助

这不是我的代码，但我发现它非常有用

代码如下：

• 价值观​​从excel文档中读取

• 正如您所看到的，我正在尝试使用这个命令

  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()
  

---

我有下面的代码来考虑颜色比例的半径。实现这一技巧的方法是使用colormap获得规格化R的颜色值（这里是颜色权重）

使用

phisize

和

thetaSize

我的数据中唯一的phis和theta的数量。 我的天线的dBi存储在dBi列中的熊猫中

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()