在Matplotlib 3D散点图中丢失的颜色

For a project I am making some 3D scatter plots with the three corresponding projections under it. I use different colors to indicate a fourth parameter. First I plot data with a certain color and then I overplot that with other data with a different color, so that in the end the order is such that I can see everything as I want:

对于一个项目,我正在制作一些3D散点图,其下面有三个相应的投影。我用不同的颜色来表示第四个参数。首先,我用一定的颜色绘制数据,然后用其他颜色用不同的颜色绘制出来,这样最终的顺序就是我可以看到我想要的一切:

In the beginning this worked fine, but when I try to do the same thing with slightly different data, the colors get messed up. The colors shown in the projections are the right ones, but some of them are missing in the 3D plot so they don't match anymore:

一开始这个工作正常,但是当我尝试用稍微不同的数据做同样的事情时,颜色搞砸了。投影中显示的颜色是正确的,但3D图中缺少一些颜色,因此它们不再匹配:

When I rotate the 3D plot in a funny way, the colors are recovered and I can see them as they were supposed to be:

当我以有趣的方式旋转3D绘图时,颜色会被恢复,我可以看到它们应该是:

However, I don't want a 3D plot that is rotated in a funny way, because the axes get messed up and it's impossible to read it properly like that.

但是,我不希望以有趣的方式旋转3D绘图,因为轴会搞砸,并且不可能像这样正确地读取它。

I found one solution to the problem here: plotting 3d scatter in matplotlib. It basically says that I should replace my ax.scatter(X,Y) with ax.plot(X,Y,'o'). When I do this the colors are shown the way they were supposed to be, but the plot is much messier and uglier this way. Basically I just want to be able to do this with a scatter plot.

我在这里找到了一个问题的解决方案:在matplotlib中绘制三维散点图。它基本上说我应该用ax.plot(X,Y,'o')替换我的ax.scatter(X,Y)。当我这样做时,颜色会以它们应该的方式显示出来,但是这样的情节更加混乱和丑陋。基本上我只是希望能够用散点图来做到这一点。

Does anyone know how to solve this?

有谁知道如何解决这个问题?

Here's a minimum example of my code, for only two colors:

这是我的代码的最小示例,仅适用于两种颜色:

from mpl_toolkits.mplot3d import art3d
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import gridspec

art3d.zalpha = lambda *args:args[0]

numcols = 20
percentage = 50

def load(Td, pc):
    T = np.load(str(pc) + 'pctTemperaturesTd=' + str(Td) + '.npy')
    D = np.load(str(pc) + 'pctDensitiesTd=' + str(Td) + '.npy')
    CD = np.load(str(pc) + 'pctColDensitiesTd=' + str(Td) + '.npy')
    return T, D, CD

def colors(ax):
    colors = np.zeros((numcols, 4))
    cm = plt.get_cmap('gist_rainbow')
    ax.set_color_cycle([cm(1.*i/numcols) for i in range(numcols)])
    for i in range(numcols):
        color = cm(1.*i/numcols)
        colors[i,:] = color
    return colors

# LOAD DATA
T10, D10, CD10 = load(10, percentage)
T200, D200, CD200 = load(200, percentage)

# 3D PLOT
fig = plt.figure(1)
gs = gridspec.GridSpec(4, 4) 

ax = fig.add_subplot(gs[:-1,:-1], projection='3d')
colours = colors(ax)

ax.plot(T200/100., np.log10(D200), np.log10(CD200), '*', markersize=10,color=colours[10], mec = colours[10], label='Td = 200', alpha=1)
ax.plot(T10/100., np.log10(D10), np.log10(CD10), '*', markersize=10,color=colours[0], mec = colours[0], label='Td = 10', alpha=1)

ax.set_xlabel('\nTg/100', fontsize='x-large')
ax.set_ylabel('\nlog(nH)', fontsize='x-large')
ax.set_zlabel('\nlog(colDen)', fontsize='x-large')
ax.set_xlim(0,5)
#ax.set_zlim(0,)
ax.set_ylim(2,6)

# PROJECTIONS

# Tg, nH
ax2 = fig.add_subplot(gs[3,0])

ax2.scatter(T200/100., np.log10(D200), marker='*', s=10, color=colours[10], label='Td = 200', alpha=1, edgecolor=colours[10])
ax2.scatter(T10/100., np.log10(D10), marker='*', s=10, color=colours[0], label='Td = 10', alpha=1, edgecolor=colours[0])

ax2.set_xlabel('Tg/100')
ax2.set_ylabel('log(nH)')
ax2.set_xlim(0,6)

# Tg, colDen 
ax3 = fig.add_subplot(gs[3,1])

ax3.scatter(T200/100., np.log10(CD200), marker='*', s=10, color=colours[10], label='Td = 200', alpha=1, edgecolor=colours[10])
ax3.scatter(T10/100., np.log10(CD10), marker='*', s=10, color=colours[0], label='Td = 10', alpha=1, edgecolor=colours[0])

ax3.set_xlabel('Tg/100')
ax3.set_ylabel('log(colDen)')
ax3.set_xlim(0,6)

# nH, colDen
ax4 = fig.add_subplot(gs[3,2])

ax4.scatter(np.log10(D200), np.log10(CD200), marker='*', s=10, color=colours[10], label='Td = 200', alpha=1, edgecolor=colours[10])
ax4.scatter(np.log10(D10), np.log10(CD10), marker='*', s=10, color=colours[0], label='Td = 10', alpha=1, edgecolor=colours[0])

ax4.set_xlabel('log(nH)')
ax4.set_ylabel('log(colDen)')

# LEGEND
legend = fig.add_subplot(gs[:,3])

text = ['Td = 10', 'Td = 20', 'Td = 30', 'Td = 40', 'Td = 50', 'Td = 60', 'Td = 70', 'Td = 80', 'Td = 90', 'Td = 100', 'Td = 110', 'Td = 120', 'Td = 130', 'Td = 140', 'Td = 150', 'Td = 160', 'Td = 170', 'Td = 180', 'Td = 190', 'Td = 200']

array = np.arange(0,2,0.1)
for i in range(len(array)):
    legend.scatter(0, i, marker='*', s=100, c=colours[numcols-i-1], edgecolor=colours[numcols-i-1])
    legend.text(0.3, i-0.25, text[numcols-i-1])
    legend.set_xlim(-0.5, 2.5)
    legend.set_ylim(0-1, i+1)

legend.axes.get_xaxis().set_visible(False)
legend.axes.get_yaxis().set_visible(False)

gs.tight_layout(fig)

plt.show()

1 个解决方案

#1

Rather than using ax.plot(x,y, 'o') try ax.plot(x,y,'.') or ax.plot(x,y,'*'. The 'o' is specifying the marker to use, and the 'o' marker is a large filled circle, which is why your plot looks ugly.

而不是使用ax.plot(x,y,'o')尝试ax.plot(x,y,'。')或ax.plot(x,y,'*'。'o'指定标记到使用,'o'标记是一个大圆圈,这就是为什么你的情节看起来很难看。