mayavi mapping a discrete colorbar on a surface

I want to change the color of a surface depending on another value (an ID) that takes discrete values (so I need a discrete colorbar). In the simplified example below I have drawn a sphere with 3 different IDs:

0/red on the left

2/blue on the middle

1/Green on the left

But with the code below, I obtain some strange behaviours (green points) at the limit between red and blue. It is probably because of an interpolation!

The Code:

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()

I have also tried with this line before mlab.show():

mySurf.actor.mapper.interpolate_scalars_before_mapping = True

The rendering is better but green points become a green strip.

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)

Recommended topics

Hot tags