Interactive 3D Visualization in Jupyter | SciPy 2018 | Maarten Breddels 

you have no idea how helpful this video is for me! Thank you!

Super, super cool. And that's my reaction just from watching it. I didn't even get to try it myself yet. 😊 Thank you for another great video.

Incredible power, what a fantastic demo. :)

The cat simulation was amazing

Excellent presentation! Good work!

Great presentation, also i have been looking for something like this for my data Visualization without the pain of d3.js stuff, Thank you sir 👍👍

This guy is a legend.

Very impressive!

It's amazing!!!

It's nice. Could you please source code and document related that you gone so far.

Thank Maarten. I am painfully struggling with how to set axis label and tick font size adding grid to plot etc. After hours of search,still, I am not able to set those plot features. Any example or helpful link are really appreciated

Is ipyvolume useful for medical image visualization??

Im asking as a 3d Artist, is this the base, for Particle Simulation ? Like are those, implemented and created through things like Jupyter ? Very interesting, I know Python is used in many procedual 3D Softwares, like Houdini and stuf like that

Where can I find the jupyter files of your demo ? Please

He really should do a demo :P

The update of the map of the halo does not work when I select the portion of the map in the E,Lz plane. I don't understand why

Didn't work out of the box. No time for futher debugging. To bad, looked nice. Tested on 2020-02-24: from api: import numpy as np import ipyvolume as ipv V = np.zeros((128,128,128)) # our 3d array # outer box V[30:-30,30:-30,30:-30] = 0.75 V[35:-35,35:-35,35:-35] = 0.0 # inner box V[50:-50,50:-50,50:-50] = 0.25 V[55:-55,55:-55,55:-55] = 0.0 ipv.quickvolshow(V, level=[0.25, 0.75], opacity=0.03, level_width=0.1, data_min=0, data_max=1) => c:\users\location_to_package\ipyvolume\serialize.py:81: RuntimeWarning: invalid value encountered in true_divide gradient = gradient / np.sqrt(gradient[0]**2 + gradient[1]**2 + gradient[2]**2)