How to show scalars as categories with a scalar bar (and how to move labels of an axis to the other side)

Two issues here related to the following plot:

Here is the code I used to plot the figure:

import numpy as np
from vedo import TetMesh, show, screenshot, settings, Picture, buildLUT, Box, \
    Plotter

import time as tm

settings.defaultFont = 'Kanopus'
# settings.useParallelProjection = True # avoid perspective parallax

tet = TetMesh('final_mesh.1.vtk')
# Clone the TetMesh object and threshold it to get the conducotrs
conductor = tet.clone().threshold(name='cell_scalars', above=0, below=4)
cond = conductor.tomesh().lineWidth(2).lineColor([7, 15, 190])

tet.threshold(name='cell_scalars', above=0, below=15)
msh = tet.tomesh().lineWidth(2).lineColor([7, 15, 190])

# Crop the entire mesh
bounds = [503500, 505000, 6414000, 6417000, -1830, 600]
msh.crop(bounds=bounds)

# Crop the conductor mesh
bounds = [503500, 505200, 6414000, 6417000, -1830, 600]
cond.crop(bounds=bounds)

# We need to build a look up table for our color bar
lut = buildLUT([
    (0.0, 'k'),
    (1.0, 'cyan'),
    (2.0, 'skyeblue'),
    (3.0, 'dodgerblue'),
    (4.0, 'blue'),
    (5.0, 'gray'),
    (6.0, 'yellow'),
    (7.0, 'gold'),
    (9.0, 'red'),
    (11.0, 'powderblue'),
    (13.0, 'lime'),
    (15.0, 'seagreen'),
]
)

msh.cmap(lut, 'cell_scalars', on='cells')
# msh.cmap("coolwarm", 'cell_scalars', on='cells')
msh.addScalarBar3D(
    pos=(505800, 6415500, -1830),
    title='Units',
    titleSize=1.5,
    sx=100,
    sy=2000,
    # titleXOffset=,
)
# Create cmap for cond
cond.cmap(lut, 'cell_scalars', on='cells')

axes_opt = dict(
    xtitle='Easting (m)',
    ytitle='Northing (m)',
    ztitle='Elevation (m)',
    xLabelSize=0.015,
    xTitlePosition=0.65,
    yTitlePosition=0.65,
    zTitlePosition=0.65,
    axesLineWidth=4,
    # xrange=msh.xbounds(),
    yrange=msh.ybounds(),
    # zInverted=True,
    xTitleOffset=0.1,
    # zTitleOffset=0.5,
    yzShift=1,
    tipSize=0.,
    # yLabelOffset=0.5,
)

# Set the camera position
plt = Plotter()
plt.camera.SetPosition( [510267.33, 6408661.841, 5262.764] )
plt.camera.SetFocalPoint( [504361.406, 6415247.796, -650.844] )
plt.camera.SetViewUp( [-0.397, 0.39, 0.831] )
plt.camera.SetDistance( 10640.747 )
plt.camera.SetClippingRange( [6131.035, 16343.212] )

size = [3940, 2160]
show(msh, cond, axes=axes_opt, size=size, interactive=True, zoom=1.2)
screenshot('model_mesh_vedo.png')
# npimg = screenshot(returnNumpy=True)
# pic = Picture(npimg)
# pic.crop(left=0.2, right=0.2, top=0.01, bottom=0.01).write('src_obs_refinement_vedo.png')

# from wand import image

# with image.Image(filename='src_obs_refinement_vedo.png') as imag:
#     imag.trim(color=None, fuzz=0)
#     imag.save(filename='src_obs_refinement_vedo_trim.png')

1. How to show scalar values as categories in the scalar bar. Right now only values are shown. In Paraview, I can show scalars as categories and it seems that I can probably also do that using PyVista.
2. The 3D axes. The yz axes used to be located at the left of the figure (default) behaviour, but that causes the z-title to be not readable (reversed, something like the below picture). Then, I had to move it to the right side. However, the titles are not moving together with the axes (both the y- and z-axis titles). Also, the labels of the y-axis is located at the left of the axis and seems to be buried under the mesh. I want to simply move them to the right of the axis. So, how to do all of these changes?

The vtk file used to plot the figure: final_mesh.1.vtk.zip

Thanks in advance for any comments and suggestions!

[XushanLu]

The manuscript I mentioned earlier that uses vedo has been accepted for publish. I have finished proofreading and the journal should be able to put the final version up on their website soonish. But here is a link to the paper on the journal’s website as of now: https://library.seg.org/doi/10.1190/geo2020-0657.1

I might remember to update the link after the final version appears on the website. The paper cited vedo and acknowledged your help in the end. Once again, thanks for making vedo available to us and for all the help you provided along the way. Hopefully, more people from the geophysics community could see the potential of what vedo can do in plotting all the Earth models when they read my paper.

[marcomusy]

yes: pip install -U git+https://github.com/marcomusy/vedo.git is sufficient to install the dev version. I just pushed it to gihub so you need to redo the pip command above.