Matplotlib 3D柱状图：如何不为0值数据点添加边缘色

问题描述 #

我使用Matplotlib绘制数组的3D柱状图，需要为柱状图添加边缘色，但发现0.0值的数据点也被添上了黑色边缘色。尝试通过循环为0值点设置edgecolors='none'但无效，寻求解决方法。

原绘图脚本 #

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
lx= len(r[0])            
ly= len(r[:,0])
xpos = np.arange(0,lx,1)    
ypos = np.arange(0,ly,1)
xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)
xpos = xpos.flatten()   
ypos = ypos.flatten()
zpos = np.zeros(lx*ly)

cs = ['r', 'g', 'b', 'y', 'c'] * ly
edgecolor_store = []

dx = 0.5 * np.ones_like(zpos)
dy = 0 * np.ones_like(zpos)
#dz = zpos
dz = r.flatten()
for value in dz: 
    if value == 0.0:
        edgecolor_store.append('none')
    if value > 0.0:
        edgecolor_store.append('black')

 
mask_dz = dz == 0 # SO:60111736, 3d case
#print(dz)
ax.bar3d(xpos,ypos,zpos, dx, dy,dz,color=cs,zsort='average',alpha=0.5,edgecolors=edgecolor_store)

示例数据 #

[  0. ,   0. ,   0. ,   0. ,   0. ],
[  0. ,   0. ,   0. ,   0. ,   0. ],
[  0. ,   0. ,   0. ,   0. ,   0. ],
[  0. ,   0. ,   0. ,   0. ,   0. ],
[  0. ,   0. ,   0. ,   0. ,   0. ],
[  0. ,   0. ,   0. ,   0. ,   0. ],
[  0. ,   0. ,  20.7,   0. ,   0. ],
[  0.6,   0. ,   0.1,   0. ,   0.2],
[  0. ,  24.8,   0. ,  46.7,   0. ],
[  0. ,   0. ,  99.7,  17.1,  99.3],
[  0. ,  12.8,  98.6,   0. ,   6.7],
[  0.2,   0. ,   0. ,  12.6,   0. ]

当前问题表现 #

输出图中大量0值位置仍显示黑色边缘线，干扰正常可视化效果。

解决方法 #

问题根源在于：当dz=0时，bar3d仍会绘制高度为0的柱体，即使设置edgecolor='none'，Matplotlib的3D渲染逻辑依然会显示边缘。正确方案是直接过滤掉所有0值数据点，仅绘制有数值的柱体。

修改后的完整代码 #

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

# 定义示例数据
r = np.array([
    [0., 0., 0., 0., 0.],
    [0., 0., 0., 0., 0.],
    [0., 0., 0., 0., 0.],
    [0., 0., 0., 0., 0.],
    [0., 0., 0., 0., 0.],
    [0., 0., 0., 0., 0.],
    [0., 0., 20.7, 0., 0.],
    [0.6, 0., 0.1, 0., 0.2],
    [0., 24.8, 0., 46.7, 0.],
    [0., 0., 99.7, 17.1, 99.3],
    [0., 12.8, 98.6, 0., 6.7],
    [0.2, 0., 0., 12.6, 0.]
])

fig = plt.figure()
ax = Axes3D(fig)
lx = len(r[0])            
ly = len(r[:,0])
xpos = np.arange(0, lx, 1)    
ypos = np.arange(0, ly, 1)
xpos, ypos = np.meshgrid(xpos + 0.25, ypos + 0.25)
xpos = xpos.flatten()   
ypos = ypos.flatten()
zpos = np.zeros(lx * ly)

cs = ['r', 'g', 'b', 'y', 'c'] * ly
dx = 0.5 * np.ones_like(zpos)
dy = 0 * np.ones_like(zpos)
dz = r.flatten()

# 过滤掉所有0值数据点
mask = dz != 0
xpos_filtered = xpos[mask]
ypos_filtered = ypos[mask]
zpos_filtered = zpos[mask]
dx_filtered = dx[mask]
dy_filtered = dy[mask]
dz_filtered = dz[mask]
# 匹配过滤后的颜色列表
cs_filtered = [cs[i] for i in range(len(cs)) if mask[i]]
# 仅为非0柱体设置黑色边缘
edgecolor_store = ['black'] * len(dz_filtered)

ax.bar3d(xpos_filtered, ypos_filtered, zpos_filtered, dx_filtered, dy_filtered, dz_filtered,
         color=cs_filtered, zsort='average', alpha=0.5, edgecolors=edgecolor_store)

plt.show()

修改说明 #

• 添加掩码mask = dz != 0，筛选出所有非0值的坐标、尺寸、颜色参数
• 仅对非0柱体设置黑色边缘，0值数据直接不绘制，从根源避免边缘线问题
• 补充了示例数据的numpy数组定义，修复原代码中可能的未定义变量问题

内容的提问来源于stack exchange，提问作者Dimuthu Kodituwakku