Python 3D绘图与数据，错误发生

Question

我尝试使用(x,y,z)绘制带有手动输入数据ax.plot_surface的3D图表。即使我使用了我在网上找到的类似代码，我仍然会遇到一些错误。

"Warning (from warnings module):
  File "D:\Program Files (x86)\Python\Python36\lib\site-packages\numpy\core\_methods.py", line 29
    return umr_minimum(a, axis, None, out, keepdims)
RuntimeWarning: invalid value encountered in reduce

Warning (from warnings module):
  File "D:\Program Files (x86)\Python\Python36\lib\site-packages\numpy\core\_methods.py", line 26
    return umr_maximum(a, axis, None, out, keepdims)
RuntimeWarning: invalid value encountered in reduce

Warning (from warnings module):
  File "D:\Program Files (x86)\Python\Python36\lib\site-packages\matplotlib\colors.py", line 489
    np.copyto(xa, -1, where=xa < 0.0)
RuntimeWarning: invalid value encountered in less"

即使出现此错误，也可以绘制图表。但它全是黑色的。不知何故，colorbar与z值不匹配。

任何人都可以帮我解决这个问题吗？感谢您的帮助。

这是我使用的代码（具体代码如下所示）：

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
from matplotlib.mlab import griddata
import numpy as np
import scipy.interpolate
from matplotlib.ticker import LinearLocator, FormatStrFormatter

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x = [1043.797,621.694,203.275,-213.783,-627.143,-1045.474,-1045.474,-628.403,-213.783,0.42,203.278,621.697,1043.801,1042.545,621.701,203.282,0.426,-213.778,-628.397,-1045.467,-0.834,1043.804,621.701,203.292,0.434,-213.77,-628.393,-1045.462,-1045.464,-628.395,-213.772,-0.829,203.29,621.707,1043.812,1043.807,621.706,203.287,-213.775,-628.398,-1045.466]

y = [-1210.936,-1211.146,-1210.931,-1210.819,-1210.916,-1210.916,-727.082,-726.768,-726.776,-726.883,-726.887,-727.101,-726.68,-242.741,-243.059,-242.846,-242.841,-242.732,-242.723,-243.037,19.801,241.133,241.025,241.248,241.148,241.154,241.167,241.07,725.216,725.208,724.565,725.401,724.976,724.97,724.975,1209.226,1209.324,1209.328,1209.338,1209.559,1209.254]

z = [3753.086,4054.802,4101.778,4064.706,3844.414,3614.887,4156.525,4184.521,4284.536,4269.797,4273.816,4298.024,4264.16,4224.935,4188.664,4200.863,4210.243,4164.851,4143.223,4148.073,3980.13,4094.025,4203.862,4260.099,4238.935,4233.248,4186.161,4072.293,4021.05,4311.022,4351.636,4359.61,4385.24,4382.892,4169.055,3927.979,4226.974,4237.096,4180.779,4082.677,3739.785]

x=np.asarray(x)
y=np.asarray(y)
N = 100
xi = np.linspace(x.min(), x.max(), N)
yi = np.linspace(y.min(), y.max(), N)
zi = scipy.interpolate.griddata((x, y), z, (xi[None,:], yi[:,None]), 
method='cubic')
xi, yi = np.meshgrid(xi,yi)
surf = ax.plot_surface(xi, yi, zi, cmap=plt.cm.hot)
plt.show()

Answer 1

由于网格上的插值，所得到的数组的最外点是nan（即第一列和最后一列以及第一行和最后一行）。虽然可以忽略nan值用于绘图，但遗憾的是它们不能用于生成着色。为了能够使用色图，应该提供没有nan值的数组（严格地说，这仅适用于3D图）。

虽然通常有几个选项，例如替换值和屏蔽，但最简单的方法是从绘图中省略行和列。即而不是ax.plot_surface(xi, yi, zi, cmap="hot")你可以使用

ax.plot_surface(xi[1:-1,1:-1], yi[1:-1,1:-1], zi[1:-1,1:-1], cmap="hot")

完整示例：

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x = [1043.797,621.694,203.275,-213.783,-627.143,-1045.474,-1045.474,-628.403,-213.783,0.42,203.278,621.697,1043.801,1042.545,621.701,203.282,0.426,-213.778,-628.397,-1045.467,-0.834,1043.804,621.701,203.292,0.434,-213.77,-628.393,-1045.462,-1045.464,-628.395,-213.772,-0.829,203.29,621.707,1043.812,1043.807,621.706,203.287,-213.775,-628.398,-1045.466]

y = [-1210.936,-1211.146,-1210.931,-1210.819,-1210.916,-1210.916,-727.082,-726.768,-726.776,-726.883,-726.887,-727.101,-726.68,-242.741,-243.059,-242.846,-242.841,-242.732,-242.723,-243.037,19.801,241.133,241.025,241.248,241.148,241.154,241.167,241.07,725.216,725.208,724.565,725.401,724.976,724.97,724.975,1209.226,1209.324,1209.328,1209.338,1209.559,1209.254]

z = [3753.086,4054.802,4101.778,4064.706,3844.414,3614.887,4156.525,4184.521,4284.536,4269.797,4273.816,4298.024,4264.16,4224.935,4188.664,4200.863,4210.243,4164.851,4143.223,4148.073,3980.13,4094.025,4203.862,4260.099,4238.935,4233.248,4186.161,4072.293,4021.05,4311.022,4351.636,4359.61,4385.24,4382.892,4169.055,3927.979,4226.974,4237.096,4180.779,4082.677,3739.785]

x=np.asarray(x)
y=np.asarray(y)
N = 100
xi = np.linspace(x.min(), x.max(), N)
yi = np.linspace(y.min(), y.max(), N)
zi = scipy.interpolate.griddata((x, y), z, (xi[None,:], yi[:,None]), 
                                        method='cubic')

xi, yi = np.meshgrid(xi,yi)

surf = ax.plot_surface(xi[1:-1,1:-1], yi[1:-1,1:-1], zi[1:-1,1:-1], cmap=plt.cm.hot)
plt.show()