我有以下格式的数据：
我使用plotly 生成散点图，然后使用以下代码通过散点拟合曲线。
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()
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.
上述输出是使用创建的XLSTAT它将类别作为第四列作为类别。
有人可以解释一下如何生成 Z 数据以帮助我对类别进行不同的着色吗？
我尝试过将 2D 矩阵分成一半 0 和一半 1 之类的东西，并得到类似这样的输出。
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
我需要生成代表表面颜色的 2D 数据，并使用自定义颜色为不同类别着色
Just as griddata可用于插值一维z数组到二维网格，你可以使用griddata插值一维color数组到相同的二维网格：
color = [colormap[cat] for cat in category]
C = np.nan_to_num(griddata((x, y), color, (X, Y), method='cubic'))
然后你可以使用颜色图cm.coolwarm将值映射到C转RGBAfacecolors:
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()
