I'm really excited to finally, officially, share a new(ish) project called napari with the world. We have been developing napari in the open from the very first commit, but we didn't want to make any premature fanfare about it… Until now. It's still alpha software, but for months now, both the core napari team and a few collaborators/early adopters have been using napari in our daily work. I've found it life-changing.
I've been looking for a great nD volume viewer in Python for the better part of
a decade. In 2009, I joined Mitya Chklovskii's lab and the FlyEM team at the
Janelia [Farm] Research Campus to work on the segmentation of 3D electron
microscopy (EM) volumes. I started out in Matlab, but moved to Python pretty
quickly and it was a very smooth transition (highly recommended! ;). Looking at
my data was always annoying though. I was either looking at single 2D slices
matplotlib.pyplot.imshow, or saving the volumes in VTK format and
loading them into ITK-SNAP — which worked ok but required me to constantly be
changing terminals, and anyway was pretty inefficient because my disk ended up
cluttered with “temporary” VTK volumes.
I tried a bunch of things after that time, including Mayavi and even building my own orthogonal views viewer with Matplotlib (which was super slow), but nothing really clicked. What's worse, I didn't see any movement on this — I even found a very discouraging thread on MNE-Python in which Gaël Varoquaux concluded, given the lack of support, that 3D visualisation was not mission critical to anyone and not worth pursuing further. (!) Throughout that time, I kept living with either manually picking out 2D slices, or going back to tools outside of Python, with all the context switching that entailed. The end result is that I was looking at my data far less than I should have been, which slowed down my work. I can't count the number of silly mistakes I made and dead ends I chased, just because I wasn't looking at the data enough.