Matplotlib：在3D散点图onpick中更改点的颜色

Question

我正在绘制3D散点图，并希望在单击点时将其颜色更改为红色。 单击新点时，我希望先前单击的点的颜色恢复为默认值。 到目前为止，我什至无法获得matplotlib来更改单击时该点的颜色。 有谁知道这是什么问题，或者3D散点图不支持此问题？

points = ax1.scatter(X_t[:,0], X_t[:,1], X_t[:,2], picker=True)

def plot_curves(indexes):
    for i in indexes: # might be more than one point if ambiguous click
        points._facecolors[i,:] = (1, 0, 0, 1)
        points._edgecolors[i,:] = (1, 0, 0, 1)
    plt.draw()

def onpick(event):
    ind = event.ind
    plot_curves(list(ind))

fig.canvas.mpl_connect('pick_event', onpick)
plt.show()

Answer 1

散点图的所有点都只有一种颜色。 因此，您无法更改仅包含一行的数组的第五行。 因此，首先您需要确保通过facecolors参数分别设置了facecolors。 然后，您可以以阵列格式获得它们，一旦单击发生，请提供原始阵列的副本，并将相应元素更改为散点图。

2D

import numpy as np
import matplotlib.pyplot as plt

X_t = np.random.rand(10,4)*20

fig, ax1 = plt.subplots()


points = ax1.scatter(X_t[:,0], X_t[:,1], 
                     facecolors=["C0"]*len(X_t), edgecolors=["C0"]*len(X_t), picker=True)
fc = points.get_facecolors()

def plot_curves(indexes):
    for i in indexes: # might be more than one point if ambiguous click
        new_fc = fc.copy()
        new_fc[i,:] = (1, 0, 0, 1)
        points.set_facecolors(new_fc)
        points.set_edgecolors(new_fc)
    fig.canvas.draw_idle()

def onpick(event):
    ind = event.ind
    plot_curves(list(ind))

fig.canvas.mpl_connect('pick_event', onpick)
plt.show()

3D

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

X_t = np.random.rand(10,4)*20

fig, ax1 = plt.subplots(subplot_kw=dict(projection="3d"))


points = ax1.scatter(X_t[:,0], X_t[:,1], X_t[:,2],
                     facecolors=["C5"]*len(X_t), edgecolors=["C5"]*len(X_t), picker=True)
fc = points.get_facecolors()

def plot_curves(indexes):
    for i in indexes: # might be more than one point if ambiguous click
        new_fc = fc.copy()
        new_fc[i,:] = (1, 0, 0, 1)
        points._facecolor3d = new_fc
        points._edgecolor3d = new_fc
    fig.canvas.draw_idle()

def onpick(event):
    ind = event.ind
    plot_curves(list(ind))

fig.canvas.mpl_connect('pick_event', onpick)
plt.show()