Plotting Overview#

This tutorial gives an overview of the plotting capabilities of NAVis. We will cover 2D and 3D plotting with various backends and their pro's and con's.

NAVis contains functions for (static) 2D and (interactive) 3D plotting. These functions can use various different backends for plotting. For 2D plots we use matplotlib and for 3D plots we use either octarine, vispy, plotly or k3d.

Which plotting method (2D/3D) and which backend (octarine, plotly, etc.) to use depends on what you are after (e.g. static, publication quality figures vs interactive data exploration) and your environment (e.g. Jupyter/VS code or terminal). Here's a quick summary:

In theory there is feature parity across backends but due to built-in limitations there are minor differences.

If you installed NAVis via pip install navis[all] all of the above backends should be available to you. If you ran a minimal install (pip install navis) you may need to install the backends separately - NAVis will complain if you try to use a backend that is not installed!

2D plots#

This uses matplotlib to generate static 2D plots. The big advantage is that you can save these plots as vector graphics. Unfortunately, matplotlib's capabilities regarding 3D data are limited. The main problem is that depth (z) is only simulated by trying to layer objects (lines, vertices, etc.) according to their z-order rather than doing proper rendering which is why you might see some neurons being plotted in front of others even though they are actually behind them. It's still great for plotting individual neurons or small groups thereof!

Let's demonstrate with a simple example using the default settings:

import navis
import matplotlib.pyplot as plt

nl = navis.example_neurons(kind="skeleton")

# Plot using default settings
fig, ax = navis.plot2d(nl, view=("x", "-z"), method="2d")
plt.tight_layout()

Above plot used the default matplotlib 2D plot. You might notice that the plot looks rather "flat" - i.e. neurons seem to be layered on top of each other without intertwining. That is one of the limitations of matplotlib's 3d backend. We can try to ameliorate this by adjust the method parameter:

# Plot settings for more complex scenes - comes at a small performance cut though
fig, ax = navis.plot2d(nl, method="3d_complex", view=("x", "-z"))
plt.tight_layout()

Looks better now, doesn't it? Now what if we wanted to adjust the perspective? For 3d axes, matplotlib lets us adjust the viewing angle by setting the elev, azim and roll attributes. See also this official explanation.

Let's give that a shot:

Plot again

fig, ax = nl.plot2d(method="3d_complex", view=("x", "-z"), non_view_axes3d="show")

# Change view to see the neurons from a different angle
ax.elev = -20
ax.azim = 45
ax.roll = 180

plt.tight_layout()

We can use this to generate small animations:

# Render 3D rotation
for i in range(0, 360, 10):
   # Change rotation
   ax.azim = i
   # Save each incremental rotation as frame
   plt.savefig('frame_{0}.png'.format(i), dpi=200)

3D plots#

By "3D plots" we typically mean interactive 3D plots as opposed to the (mostly) static 2D or semi-3D plots above. 3D plots are great for great for exploring your data interactively but you can also use them to generate high-quality static images.

As laid out at the top of this page: for 3D plots, we are using either octarine, vispy, plotly or k3d. In brief:

By default, the choice is automatic and depends on (1) what backends are installed and (2) the context:

• from IPython/Terminal/scripts: octarine vispy plotly
• from Jupyter Lab/Notebook: plotly octarine k3d

You can always force a specific backend using the backend parameter in navis.plot3d:

n = navis.example_neurons()
navis.plot3d(n)

n = navis.example_neurons()
navis.plot3d(n, backend='octarine')

n = navis.example_neurons()
navis.plot3d(n, backend='plotly')

n = navis.example_neurons()
navis.plot3d(n, backend='vispy')

n = navis.example_neurons()
navis.plot3d(n, backend='k3d')

Alternatively, you can also set the default backend using an environment variable. For example:

export NAVIS_PLOT3D_BACKEND="octarine"

With that out of the way, let's have a look at some 3D plots! You will notice that for the octarine, vispy and k3d backends we're just showing screenshots - that's because their interactive plots can't be embedded into this documentation.

Octarine/Vispy#

Octarine and Vispy are pretty similar in that they both work via a Viewer object which allows you to interactively add/remove objects, change colors, etc. The main difference is that Octarine uses modern WGPU instead of OpenGL which makes it much faster than Vispy:

OctarineVispy

nl = navis.example_neurons()
viewer = navis.plot3d(nl, backend='octarine')

nl = navis.example_neurons()
viewer = navis.plot3d(nl, backend='vispy')

A few important notes regarding the Octarine/Vispy backends:

• The viewer is dynamic: you can keep adding/removing items in other cells. However, it will die with the kernel (unlike plotly)!

• By default, NAVis will track the "primary" viewer and subsequent calls of navis.plot3d will add object to that primary viewer

  • if you want to force a new viewer: navis.plot3d(nl, viewer='new')
  • if you want to add to a specific viewer: navis.plot3d(nl, viewer=viewer)

• You can dynamically resize the canvas (in Jupyter by dragging the lower right corner)

• For Jupyter: the rendering runs in your Jupyter Kernel and the frames are sent to Jupyter via a remote frame buffer (jupyter_rbf). If your Jupyter kernel runs on a remote machine you might experiences some lag depending on the connection speed and quality.

Some important methods for the viewer object:

# Close the viewer
viewer.close()

# Close the current primary viewer
navis.close3d()

# Add neurons to the primary viewer
navis.plot3d(nl)

# Add neurons to a specific
navis.plot3d(nl, viewer=viewer)

# Clear viewer
viewer.clear3d()

# Clear the primary viewer
navis.clear3d()

The Octarine viewer itsel has a bunch of neat features - check out the documentation to learn more.

K3d#

k3d plots work in Jupyter (and only there) but unlike plotly don't persist across sessions. Hence we will only briefly demo them using static screenshots and then move on to plotly. Almost everything you can do with the plotly backend can also be done with k3d (or octarine/vispy for that matter)!

p = navis.plot3d(nl, backend="k3d")

Out:

/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/traitlets/__init__.py:28: DeprecationWarning:


            Sentinel is not a public part of the traitlets API.
            It was published by mistake, and may be removed in the future.

Plotly#

Last but not least, we have the plotly backend. This is the only backend which allows us to embed interactive 3D plots into this documentation. The main advantage of plotly is that it works "inline" in Jupyter notebooks and that you can export the plots as standalone HTML files. The main disadvantage is that it can be quite slow for large scenes and that the resulting .ipynb notebook files can get quite large.

Using plotly as backend generates "inline" plots by default (i.e. they are rendered right away):

navis.plot3d(
    nl,
    backend="plotly",
    connectors=False,
    legend_orientation="h",  # horizontal legend (more space for plot)
    )

Instead of inline plotting, you can also export your plotly figure as 3D html file that can be opened in any browser:

import plotly

# Prevent inline plotting
fig = nl.plot3d(backend='plotly', connectors=False, width=1400, height=1000, inline=False)

# Save figure to html file
plotly.offline.plot(fig, filename='~/Documents/3d_plot.html')

Navigating the 3D viewers#

%%

High-quality renderings#

Above we demo'ed making a little GIF using matplotlib. While that's certainly fun, it's not very high production value. For high quality videos and renderings I recommend you check out the tutorial on navis' Blender interface. Here's a little taster:

Total running time of the script: ( 0 minutes 49.361 seconds)

Download Python source code: plot_00_plotting_intro.py

Download Jupyter notebook: plot_00_plotting_intro.ipynb

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