基于通过散点的类别对3D表面进行着色 [英] Color 3D Surface Based on Categories that passes through scatter points

问题描述

我具有以下格式的数据:

I have the data in the following format:

我使用散点图生成散点图，然后使用以下代码通过散点拟合曲线.

I used plotly to generate a scatter plot and then a fit a Curve through the scatter points using the following code.

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

x=np.asarray([3,5,9,3,3,7,6,9,1,9]);
y=np.asarray([4,3,3,10,8,2,4,10,9,3]);
z=np.asarray([1,2,4,10,1,7,10,3,1,7]);
# x = np.random.random(100)

xi=np.linspace(min(x), max(x),50)
#print xi
yi=np.linspace(min(y),max(y),50)


X,Y= np.meshgrid(xi,yi)
Z = np.nan_to_num(griddata((x,y), z, (X, Y), method='cubic'))

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
                       linewidth=0, antialiased=False,alpha=0.4)
plt.show()

我想要做的就是根据类似这样的颜色对绘图进行着色: 其中红色代表类别1，蓝色代表类别2. 因此，为了获得类似的内容，我需要生成一个2D数组，然后使用色图/色标对类别进行相应的着色.

What i am looking to do is to color the plot according to categories something like this : Where red represents the category 1 and Blue represents category 2. So inorder to get something like this I need to generate a 2D Array and then use a colormap/colorscale to color the categories accordingly.

上面的输出是使用 XLSTAT 创建的，其中将category作为第4个列.

The above output have been created using XLSTAT where it took category as the 4th col as the category.

有人可以解释我如何生成Z数据，这将有助于我为类别添加不同的颜色吗?

Can someone explain me how do i generate the Z data which will help me color the categories differently?

我已经尝试过将2D矩阵分为0和1的一半，并得到类似的输出.

I have tried to something like dividing the 2D matrix into halves 0's and half 1's and got output something like this.

考虑以下示例数据:

x   y   z   Category
3   4   1   Cat 1
5   3   2   cat2
9   3   4   cat2
3   10  10  cat3
3   8   1   cat3
7   2   7   cat2
6   4   10  Cat 1
9   10  3   Cat 4
1   9   1   Cat 1
9   3   7   cat2

我需要生成2D数据，以表示表面颜色并使用自定义颜色为不同类别着色

推荐答案

就像 griddata 可用于将1D z数组插值到2D网格，可以使用griddata将1D color数组插值到同一2D网格:

Just as griddata can be used to interpolate the 1D z array to a 2D grid, you can use griddata to interpolate a 1D color array to the same 2D grid:

color = [colormap[cat] for cat in category]
C = np.nan_to_num(griddata((x, y), color, (X, Y), method='cubic'))

然后，您可以使用颜色映射cm.coolwarm将C中的值映射到RGBA facecolors:

Then you can use the colormap cm.coolwarm to map values in C to RGBA facecolors:

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cmap,
                linewidth=0, antialiased=False, alpha=0.4, facecolors=cmap(C))

---

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

x = np.asarray([3, 5, 9, 3, 3, 7, 6, 9, 1, 9])
y = np.asarray([4, 3, 3, 10, 8, 2, 4, 10, 9, 3])
z = np.asarray([1, 2, 4, 10, 1, 7, 10, 3, 1, 7])
category = np.array(['Cat 1', 'cat2', 'cat2', 'cat3', 'cat3',
                     'cat2', 'Cat 1', 'Cat 4', 'Cat 1', 'cat2'])
# coolwarm: 0 --> blue, 1 --> red
# want: 'Cat 1' --> blue, 'cat2' --> red, 'cat3' --> ?, 'Cat 4' --> ?
colormap = {'Cat 1': 0, 'cat2': 1, 'cat3': 0.333, 'Cat 4': 0.666}
color = np.array([colormap[cat] for cat in category])

xi = np.linspace(min(x), max(x), 50)
yi = np.linspace(min(y), max(y), 50)
X, Y = np.meshgrid(xi, yi)
Z = np.nan_to_num(interpolate.griddata((x, y), z, (X, Y), method='cubic'))
C = interpolate.griddata((x, y), color, (X, Y), method='cubic')

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
cmap = cm.coolwarm
ax.scatter(x, y, z, c=color, cmap=cmap)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cmap,
                linewidth=0, antialiased=False, alpha=0.4, facecolors=cmap(C))
plt.show()

这篇关于基于通过散点的类别对3D表面进行着色的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！