Color 3d Surface Based On Categories That Passes Through Scatter Points

I have the data in the following format: X,Y,Z,Category I used plotly to generate a scatter plot and then a fit a Curve through the scatter points using the following code. from s

Solution 1:

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'))

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))

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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()