Rerun

Python Quick Start

Installing Rerun

The Rerun SDK for Python requires a working installation of Python-3.8+.

You can install the Rerun SDK using the rerun-sdk pypi package via pip:

$ pip3 install rerun-sdk

You are now ready to start logging and visualizing data.

Trying out the viewer

The Rerun SDK comes packaged with a simple demo so you can quickly get a feel for the viewer. You can launch it with

$ python3 -m rerun_demo

If everything is installed and working correctly, you should end up with a window like below. Try looping the recording to see the fun animation.

colored cube

Note: If this is your first time launching Rerun you will see a notification in the terminal about the Rerun anonymous data usage policy. Rerun collects anonymous usage data to help improve the project, though you may choose to opt out if you would like.

If you're having problems

Using the viewer

Try out the following to interact with the viewer:

  • Click and drag in the main view to rotate the cube.
  • Zoom in and out with the scroll wheel.
  • Mouse over the "?" icons to find out about more controls.
  • Grab the time-slider and move it to see the cube at different time-points.
  • Click the "play" button to animate the cube.
  • Click on the cube to select all of the points.
  • Hover and select individual points to see more information.

This is just a taste of some of what you can do with the viewer. We will cover other functionality in much more detail later in the Viewer Walkthrough

Logging your own data

Now instead of using a prepackaged demo, let's create some data ourselves. We will start with an extremely simplified version of this dataset that just logs 1 dimension of points instead of 3.

Create a new python script with the following code:

import rerun as rr  # NOTE: `rerun`, not `rerun-sdk`!
import numpy as np

rr.init("rerun_example_my_data", spawn=True)

positions = np.zeros((10, 3))
positions[:,0] = np.linspace(-10,10,10)

colors = np.zeros((10,3), dtype=np.uint8)
colors[:,0] = np.linspace(0,255,10)

rr.log_points("my_points", positions=positions, colors=colors, radii=0.5)

When you run this script you will again be greeted with the Rerun Viewer, this time only showing a simple line of red points.

simple line

The rr.log_points function can take any Nx2 or Nx3 numpy array as a collection of positions.

Feel free to modify the code to log a different set of points. If you want to generate the colored cube from the built-in demo, you can use the following numpy incantation.

import rerun as rr
import numpy as np

rr.init("rerun_example_my_data", spawn=True)

SIZE = 10

pos_grid = np.meshgrid(*[np.linspace(-10, 10, SIZE)]*3)
positions = np.vstack([d.reshape(-1) for d in pos_grid]).T

col_grid = np.meshgrid(*[np.linspace(0, 255, SIZE)]*3)
colors = np.vstack([c.reshape(-1) for c in col_grid]).astype(np.uint8).T

rr.log_points("my_points", positions=positions, colors=colors, radii=0.5)

What's next

If you're ready to move on to more advanced topics, check out the Viewer Walkthrough or our more advanced guide for Logging Data in Python where we will explore the core concepts that make Rerun tick and log our first non-trivial dataset.

If you'd rather learn from examples, check out the example gallery for some more realistic examples, or browse the Loggable Data Types section for more simple examples of how to use the main s.

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