mayavi映射表面上的離散色條

Question

我想根據另一個採用離散值（因此需要離散色條）的值（ID）更改表面的顏色。 在簡化的例子中下面我得出的球體用3點不同的ID：

0 /紅色左側

2 /藍色中間

1 /綠上留下

但是通過下面的代碼，我在紅色和藍色之間的界限上獲得了一些奇怪的行爲（綠點）。 這可能是因爲插值！

驗證碼：

from mayavi import mlab 
import numpy as np 

# my dataset -simplified- 
x,y,z = np.mgrid[-3:3:100j, -3:3:100j, -3:3:100j] 
values = np.sqrt(x**2 + y**2 + z **2) 

# my color values : the volume is divided in 3 sub-volumes along x taking 
colorvalues=np.empty(values.shape) 
colorvalues[0:33,:,:]=0. 
colorvalues[33:66,:,:]=2. 
colorvalues[66:,:,:] =1. 

src = mlab.pipeline.scalar_field(values) 
src.image_data.point_data.add_array(colorvalues.T.ravel()) 
src.image_data.point_data.get_array(1).name = 'myID' 
src.image_data.point_data.update() 

# the surface i am interested on 
contour = mlab.pipeline.contour(src) 
contour.filter.contours= [2.8,] 

# to map the ID 
contour2 = mlab.pipeline.set_active_attribute(contour, point_scalars='myID') 

# And we display the surface The colormap is the current attribute: the ID. 
mySurf=mlab.pipeline.surface(contour2) 

# I change my colormap to a discrete one : R-G-B 
mySurf.module_manager.scalar_lut_manager.lut.table = np.array([[255,0,0,255],[0,255,0,255],[0,0,255,255]]) 

mlab.colorbar(title='ID', orientation='vertical', nb_labels=3)  
mlab.show() 

我也跟這一行試圖mlab.show（）之前：

mySurf.actor.mapper.interpolate_scalars_before_mapping = True 

渲染是更好，但綠點成爲綠色跳閘。

Answer 1

我已經使用SciPy的近鄰插值和利用我的目標細胞的一種顏色找到了我的答案。

from mayavi import mlab 
import numpy as np 
import scipy.interpolate 

# my dataset -simplified- 
x,y,z = np.mgrid[-3:3:100j, -3:3:100j, -3:3:100j] 
values = np.sqrt(x**2 + y**2 + z **2) 

# my color values : the volume is divided in 3 sub-volumes along x taking 
colorvalues=np.empty(values.shape) 
colorvalues[0:33,:,:]=0. 
colorvalues[33:66,:,:]=2. 
colorvalues[66:,:,:] =1. 

src = mlab.pipeline.scalar_field(x,y,z ,values) 

# the surface i am interested on 
contour = mlab.pipeline.contour(src) 
contour.filter.contours= [2.8,] 

# I extract points that form my surface 
PtsCoord = contour.outputs[0].points.to_array() 

# then the variable that contains the indices of the points forming triangles. 
PolyAndTriIDs = contour.outputs[0].polys.to_array() 
PolyAndTriIDs = PolyAndTriIDs.reshape(PolyAndTriIDs.size/4,4) 

# Coordinates of each triangle 
x1,y1,z1 = PtsCoord[PolyAndTriIDs[:,1]].T 
x2,y2,z2 = PtsCoord[PolyAndTriIDs[:,2]].T 
x3,y3,z3 = PtsCoord[PolyAndTriIDs[:,3]].T 

# I interpolate the color value at the center of triangles with the Nearest-neighbour interpolation method 
interp0 = scipy.interpolate.NearestNDInterpolator((x.ravel(),y.ravel(),z.ravel()), colorvalues.ravel()) 
result0 = interp0((np.mean((x1,x2,x3),0),np.mean((y1,y2,y3),0),np.mean((z1,z2,z3),0))) 

# Displaying with triangular_mesh and color given by cell scalar value 
mesh = mlab.triangular_mesh(PtsCoord[:,0], PtsCoord[:,1], PtsCoord[:,2], PolyAndTriIDs[:,1:]) 
cell_data = mesh.mlab_source.dataset.cell_data 
cell_data.scalars = result0 
cell_data.scalars.name = 'Cell data' 
cell_data.update() 
mesh.actor.mapper.scalar_mode = 'use_cell_data' 
mesh.module_manager.scalar_lut_manager.lut.table = np.array([[255,0,0,255],[0,255,0,255],[0,0,255,255]]) 
mesh.module_manager.scalar_lut_manager.use_default_range = False 
mesh.module_manager.scalar_lut_manager.data_range = [ 0., 2.] 

mlab.colorbar(title='ID', orientation='vertical', nb_labels=3) 

結果不那麼流暢但更相關。