matplotlib表面图隐藏了散点，这些散点应该在前面

Question

还有另一个关于matplotlib 3d曲面的问题...我有代码可以向matplotlib曲面图添加一个散点。

我遇到的问题是，无论从哪个角度观看，该点都会始终在表面后方出现总是。

如果我使用3条短线标记一个（公认的丑陋）版本来标记同一点，则它是可见的。

我已经关闭了depthshade功能，不是吗。谁能解释正在发生的事情以及如何纠正它？这是代码的简化版本：

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

df = pd.DataFrame({10: {10: 1,15: 1,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   15: {10: 4,15: 1,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   20: {10: 6,15: 3,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   25: {10: 7,15: 5,20: 3,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   30: {10: 9,15: 6,20: 4,25: 3,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   35: {10: 10,15: 7,20: 5,25: 4,30: 2,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   40: {10: 11,15: 8,20: 6,25: 4,30: 3,35: 2,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   45: {10: 12,15: 9,20: 7,25: 5,30: 4,35: 3,40: 2,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   50: {10: 13,15: 9,20: 7,25: 6,30: 5,35: 4,40: 3,45: 2,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   55: {10: 14,15: 10,20: 8,25: 7,30: 5,35: 4,40: 3,45: 3,50: 2,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   60: {10: 15,15: 11,20: 9,25: 7,30: 6,35: 5,40: 4,45: 3,50: 3,55: 2,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   65: {10: 16,15: 12,20: 9,25: 8,30: 6,35: 5,40: 5,45: 4,50: 3,55: 2,60: 2,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   70: {10: 17,15: 12,20: 10,25: 8,30: 7,35: 6,40: 5,45: 4,50: 4,55: 3,60: 2,65: 2,70: 1,75: 1,80: 1,85: 1,90: 1},
                   75: {10: 18,15: 13,20: 10,25: 9,30: 7,35: 6,40: 5,45: 5,50: 4,55: 3,60: 3,65: 2,70: 2,75: 1,80: 1,85: 1,90: 1},
                   80: {10: 19,15: 14,20: 11,25: 9,30: 8,35: 7,40: 6,45: 5,50: 4,55: 4,60: 3,65: 3,70: 2,75: 2,80: 1,85: 1,90: 1},
                   85: {10: 21,15: 14,20: 11,25: 10,30: 8,35: 7,40: 6,45: 6,50: 5,55: 4,60: 4,65: 3,70: 3,75: 2,80: 2,85: 1,90: 1},
                   90: {10: 23,15: 15,20: 12,25: 10,30: 9,35: 8,40: 7,45: 6,50: 5,55: 5,60: 4,65: 3,70: 3,75: 3,80: 2,85: 2,90: 1}})




xv, yv = np.meshgrid(df.index, df.columns)
ma = np.nanmax(df.values)
norm = matplotlib.colors.Normalize(vmin = 0, vmax = ma, clip = True)

fig = plt.figure(1)
ax = Axes3D(fig)
surf = ax.plot_surface(yv,xv,df, cmap='viridis_r', linewidth=0.3,
                       alpha = 0.8, edgecolor = 'k', norm=norm)
ax.scatter(25,35,4, c='k', depthshade=False, alpha = 1, s=100)

fig = plt.figure(2)
ax = Axes3D(fig)
surf = ax.plot_surface(yv,xv,df, cmap='viridis_r', linewidth=0.3,
                       alpha = 0.8, edgecolor = 'k', norm=norm)
line1_x = [25,25]
line1_y = [35,35]
line1_z = [3,5]

line2_x = [25,25]
line2_y = [33,37]
line2_z = [4,4]

line3_x = [23,27]
line3_y = [35,35]
line3_z = [4,4]

ax.plot(line1_x, line1_y, line1_z, alpha = 1, linewidth = 1, color='k')
ax.plot(line2_x, line2_y, line2_z, alpha = 1, linewidth = 1, color='k')
ax.plot(line3_x, line3_y, line3_z, alpha = 1, linewidth = 1, color='k')
plt.show()

Answer 1

好的，根据上面Mr T的评论，似乎没有直接的处理方法。但是，对于我要执行的操作，有一种解决方法（突出显示表面上的特定点）。使用matplotlib.patches和mpl_toolkits.mplot3d.art3d模块，可以在图形上的适当位置绘制一个圆，而这似乎不受同一问题的影响。

修改后的代码为：

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D, art3d
from matplotlib.patches import Circle
import numpy as np

df = pd.DataFrame({10: {10: 1,15: 1,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   15: {10: 4,15: 1,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   20: {10: 6,15: 3,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   25: {10: 7,15: 5,20: 3,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   30: {10: 9,15: 6,20: 4,25: 3,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   35: {10: 10,15: 7,20: 5,25: 4,30: 2,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   40: {10: 11,15: 8,20: 6,25: 4,30: 3,35: 2,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   45: {10: 12,15: 9,20: 7,25: 5,30: 4,35: 3,40: 2,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   50: {10: 13,15: 9,20: 7,25: 6,30: 5,35: 4,40: 3,45: 2,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   55: {10: 14,15: 10,20: 8,25: 7,30: 5,35: 4,40: 3,45: 3,50: 2,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   60: {10: 15,15: 11,20: 9,25: 7,30: 6,35: 5,40: 4,45: 3,50: 3,55: 2,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   65: {10: 16,15: 12,20: 9,25: 8,30: 6,35: 5,40: 5,45: 4,50: 3,55: 2,60: 2,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   70: {10: 17,15: 12,20: 10,25: 8,30: 7,35: 6,40: 5,45: 4,50: 4,55: 3,60: 2,65: 2,70: 1,75: 1,80: 1,85: 1,90: 1},
                   75: {10: 18,15: 13,20: 10,25: 9,30: 7,35: 6,40: 5,45: 5,50: 4,55: 3,60: 3,65: 2,70: 2,75: 1,80: 1,85: 1,90: 1},
                   80: {10: 19,15: 14,20: 11,25: 9,30: 8,35: 7,40: 6,45: 5,50: 4,55: 4,60: 3,65: 3,70: 2,75: 2,80: 1,85: 1,90: 1},
                   85: {10: 21,15: 14,20: 11,25: 10,30: 8,35: 7,40: 6,45: 6,50: 5,55: 4,60: 4,65: 3,70: 3,75: 2,80: 2,85: 1,90: 1},
                   90: {10: 23,15: 15,20: 12,25: 10,30: 9,35: 8,40: 7,45: 6,50: 5,55: 5,60: 4,65: 3,70: 3,75: 3,80: 2,85: 2,90: 1}})




xv, yv = np.meshgrid(df.index, df.columns)
ma = np.nanmax(df.values)
norm = matplotlib.colors.Normalize(vmin = 0, vmax = ma, clip = True)

fig = plt.figure(1)
ax = Axes3D(fig)
surf = ax.plot_surface(yv,xv,df, cmap='viridis_r', linewidth=0.3,
                       alpha = 0.8, edgecolor = 'k', norm=norm)

p = Circle((25, 35), 3, ec='k', fc="none")
ax.add_patch(p)
art3d.pathpatch_2d_to_3d(p, z=4, zdir="z")

plt.show()

Answer 2

在2020年遇到此问题，并且不想切换到另一个软件包。此解决方案是对以上Will答案的修改。基本上在三个轴上绘制圆，使其更像点。也可以使用椭圆来调整轴比。如果您设置较小的半径并选择面部颜色，效果会更好：

   def add_point(ax, x, y, z, fc = None, ec = None, radius = 0.005):
       xy_len, z_len = ax.get_figure().get_size_inches()
       axis_length = [x[1] - x[0] for x in [ax.get_xbound(), ax.get_ybound(), ax.get_zbound()]]
       axis_rotation =  {'z': ((x, y, z), axis_length[1]/axis_length[0]),
                         'y': ((x, z, y), axis_length[2]/axis_length[0]*xy_len/z_len),
                         'x': ((y, z, x), axis_length[2]/axis_length[1]*xy_len/z_len)}
       for a, ((x0, y0, z0), ratio) in axis_rotation.items():
           p = Ellipse((x0, y0), width = radius, height = radius*ratio, fc=fc, ec=ec)
           ax.add_patch(p)
           art3d.pathpatch_2d_to_3d(p, z=z0, zdir=a)

其中radius是“圆”的半径，fc是面部颜色，ec是边缘颜色。

修改后的代码：

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D, art3d
from matplotlib.patches import Circle, Ellipse
import numpy as np

df = pd.DataFrame({10: {10: 1,15: 1,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   15: {10: 4,15: 1,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   20: {10: 6,15: 3,20: 1,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   25: {10: 7,15: 5,20: 3,25: 1,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   30: {10: 9,15: 6,20: 4,25: 3,30: 1,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   35: {10: 10,15: 7,20: 5,25: 4,30: 2,35: 1,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   40: {10: 11,15: 8,20: 6,25: 4,30: 3,35: 2,40: 1,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   45: {10: 12,15: 9,20: 7,25: 5,30: 4,35: 3,40: 2,45: 1,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   50: {10: 13,15: 9,20: 7,25: 6,30: 5,35: 4,40: 3,45: 2,50: 1,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   55: {10: 14,15: 10,20: 8,25: 7,30: 5,35: 4,40: 3,45: 3,50: 2,55: 1,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   60: {10: 15,15: 11,20: 9,25: 7,30: 6,35: 5,40: 4,45: 3,50: 3,55: 2,60: 1,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   65: {10: 16,15: 12,20: 9,25: 8,30: 6,35: 5,40: 5,45: 4,50: 3,55: 2,60: 2,65: 1,70: 1,75: 1,80: 1,85: 1,90: 1},
                   70: {10: 17,15: 12,20: 10,25: 8,30: 7,35: 6,40: 5,45: 4,50: 4,55: 3,60: 2,65: 2,70: 1,75: 1,80: 1,85: 1,90: 1},
                   75: {10: 18,15: 13,20: 10,25: 9,30: 7,35: 6,40: 5,45: 5,50: 4,55: 3,60: 3,65: 2,70: 2,75: 1,80: 1,85: 1,90: 1},
                   80: {10: 19,15: 14,20: 11,25: 9,30: 8,35: 7,40: 6,45: 5,50: 4,55: 4,60: 3,65: 3,70: 2,75: 2,80: 1,85: 1,90: 1},
                   85: {10: 21,15: 14,20: 11,25: 10,30: 8,35: 7,40: 6,45: 6,50: 5,55: 4,60: 4,65: 3,70: 3,75: 2,80: 2,85: 1,90: 1},
                   90: {10: 23,15: 15,20: 12,25: 10,30: 9,35: 8,40: 7,45: 6,50: 5,55: 5,60: 4,65: 3,70: 3,75: 3,80: 2,85: 2,90: 1}})




xv, yv = np.meshgrid(df.index, df.columns)
ma = np.nanmax(df.values)
norm = matplotlib.colors.Normalize(vmin = 0, vmax = ma, clip = True)

fig = plt.figure(1)
ax = Axes3D(fig)
surf = ax.plot_surface(yv,xv,df, cmap='viridis_r', linewidth=0.3,
                       alpha = 0.8, edgecolor = 'k', norm=norm)

add_point(ax, 25, 35, 0, radius=1)
add_point(ax, 25, 35, 2, radius=2)
add_point(ax, 25, 35, 4, radius=3)

plt.show()