Vedo: Visualization in jupyter Lab slicer.py file

[wangyibin0011]

Hello, thank you for your excellent work. I have a question to ask you:
Now I want to implement advance in jupyter lab/ slicer.py When embedwindow (false), a python visualizer will pop up, but when I want to get a visualization result on jupyter lab, nothing will appear when I set embedwindow ('K3D').
The code is as follows：
~~~
from vedo import datadir, load, addons, show, Text2D
from vedo.applications import Slicer
from vedo import embedWindow

embedWindow('k3d')

filename = datadir+'embryo.slc'

vol = load(filename).alpha([0,0,1]).c('gray') #.printInfo()

plt = Slicer(vol,
bg='white', bg2='lightblue',
# cmaps=("gist_ncar_r","jet","Spectral_r","hot_r","bone_r"),
cmaps=("gray","jet","Spectral_r","hot_r","bone_r"),
useSlider3D=True,
)

show(plt,vol)
~
The output of jupyter lab is as follows
~
Slicer tool
Use showInset() after first rendering the scene.
~~~
How can I get the visualization results in jupyter lab?

[wangyibin0011]

Jupyter lab and jupyter notebook get the same result

[marcomusy]

thanks @wangyibin0011
because vedo delegates in notebooks the rendering to k3d i'm afraid that Slicer can only work when a pop up window is allowed.. :(

[wangyibin0011]

All right, so that's that.
I have an idea. I want to generate multiple subgraphs in one window, and generate sliders for Vols in each subgraph to show slices. Do you have any good ideas? Any suggestion can be very useful. thank you！

[marcomusy]

That should be doable.. check out this example, type:
vedo -r sliders2

[wangyibin0011]

That's all I need. Thank you very much!

[wangyibin0011]

Now I seem to have a problem: the code is as follows:
~~~
from vedo import *
import math
import numpy as np

def sliderfunc_z(widget, event):
"""
map2cells
dims
"""
i = int(widget.GetRepresentation().GetValue())
rmin, rmax = vol.imagedata().GetScalarRange()
msh = vol.zSlice(i).lighting('', la, ld, 0)
msh.pointColors(cmap='gist_ncar_r', vmin=rmin, vmax=rmax)
vp.renderer.RemoveActor(visibles[2])
if i and i visibles[2] = msh

im_data = np.load('/Users/biomind/Biomind-Server-Tutorials/pipelines/im_data.npy',allow_pickle=True)
columns = 4
numplots = len(im_data)
rows = math.ceil(numplots / columns)
vp = Plotter(axes=False,sharecam=False,title='PreviewPridection',shape=(rows,columns))
la, ld = 0.7, 0.3

for i in range(len(im_data)):
vol = Volume(im_data[i]).c('gray')
dims = vol.dimensions()
visibles = [None,None,None]
vp.show(vol, at=i)
vp.addSlider2D(sliderfunc_z,
0, len(im_data[i]),
value=0,
pos=([0.03,0.03], # first point of slider in the renderer
[0.03,0.3]))

vp.show(interactive=1)
~~~
If you run this code, you will find that no matter which slider is operated, the last Vol will be sliced. In fact, I wrote the same thing. Obviously, I want to slice the corresponding Vol when sliding each slider. I noticed our sliderfuncs_ Z is passed to addslider2d as a parameter. Is there any way to pass Vol to sliderfunc_ Z? Otherwise, what are the feasible ways to realize my plan? Looking forward to your reply, NPY file has been uploaded.Of course, there are some problems with the data, but I can solve this

im_data.npy.zip

[wangyibin0011]

I solved the problem：
~~~
from vedo import *
import math
import numpy as np

im_data = np.load('/Users/biomind/Biomind-Server-Tutorials/pipelines/im_data.npy',allow_pickle=True)

columns = 4
numplots = len(im_data)
rows = math.ceil(numplots / columns)

vp = Plotter(axes=False,sharecam=False,title='PreviewPridection',shape=(rows,columns))

la, ld = 0.7, 0.3

def slice_factor(index, data):
vol = Volume(data).c('gray')
dims = vol.dimensions()
vp.show(vol, at=index)
visibles = [None,None,None]
def f(widget, event):
vol = Volume(data).c('gray')
i = int(widget.GetRepresentation().GetValue())
rmin, rmax = vol.imagedata().GetScalarRange()
msh = vol.zSlice(i).lighting('', la, ld, 0)
msh.pointColors(cmap='gist_ncar_r', vmin=rmin, vmax=rmax)
vp.renderer.RemoveActor(visibles[2])
if i and i visibles[2] = msh
return f

def add_slice(index, data):
vp.addSlider2D(slice_factor(index, data), 0, len(data),value=0, pos=([0.03,0.03],[0.03,0.3]))

for index, data in enumerate(im_data):
add_slice(index, data)

vp.show(interactive=1)

~~~

[marcomusy]

It's a very cool example, i've played a bit with it, consider this variation:

from vedo import Plotter, Volume, Text2D
import numpy as np

im_data = np.load('/home/musy/downloads/im_data.npy', allow_pickle=True)

cmap = 'gist_ncar_r'
alphas = [0,0,0.1,0,0]
zscale = 10
columns = 4
numplots = len(im_data)
rows = int(numplots/columns+0.5)

vp = Plotter(title='PreviewPrediction',
             sharecam=False,
             shape=(rows,columns),
             size='fullscreen')

def slice_factor(index, data):
    vol = Volume(data).mode(1).spacing([1,1,zscale]).c('k').alpha(alphas)
    dims = vol.dimensions()
    box = vol.box().alpha(0.5)
    comment = Text2D('dataset'+str(index), font='MonospaceTypewriter', s=0.8)
    vp.show(vol, box, comment, at=index, interactorStyle=6)
    visibles = [None,None,None]
    rmin, rmax = vol.scalarRange()
    sb = vol.zSlice(0).pointColors(cmap=cmap, vmin=rmin, vmax=rmax).addScalarBar3D()
    sb.UseBoundsOff() # avoid resetting the cam
    zb = vol.zbounds()
    def f(widget, event):
        i = int(widget.GetRepresentation().GetValue())
        vp.renderer = widget.GetCurrentRenderer()
        msh = vol.zSlice(i).lighting('', 0.7, 0.3, 0)
        msh.pointColors(cmap=cmap, vmin=rmin, vmax=rmax)
        vp.remove(visibles[2], render=False)
        if 0 < i < dims[2]:
            zlev = zb[1]/(zb[1]-zb[0])*i*zscale + zb[0]
            vp.add([msh, sb.z(zlev)])
        visibles[2] = msh
    return f

def add_slice(index, data):
    vp.addSlider2D(slice_factor(index, data),
                   0, len(data), value=0,
                   pos=([0.03,0.03],[0.03,0.3]))

for index, data in enumerate(im_data):
    add_slice(index, data)

vp.show(interactive=True)

• you dont need to recreate the volume in f
• use right click to rotate scene to avoid accdentally dragging the slider interactorStyle=6
• use vp.renderer = widget.GetCurrentRenderer() to grab the current window automatically
• expand the z axis with spacing() and adjust transparency with alpha()
• add a comment for each dataset if needed

PS: @wangyibin0011 As I see you have a different scaling in each dataset you can add a 3d scalarbar too (i updated the code above):

[wangyibin0011]

Thank you very much for your suggestions