Rerun

Scalar

Log a double-precision scalar.

The current timeline value will be used for the time/X-axis, hence scalars cannot be static.

When used to produce a plot, this archetype is used to provide the data that is referenced by the SeriesLine or SeriesPoint archetypes. You can do this by logging both archetypes to the same path, or alternatively configuring the plot-specific archetypes through the blueprint.

Components components

Required: Scalar

Shown in shown-in

Examples examples

Simple line plot simple-line-plot

"""Log a scalar over time."""

import math

import rerun as rr

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

# Log the data on a timeline called "step".
for step in range(0, 64):
    rr.set_time_sequence("step", step)
    rr.log("scalar", rr.Scalar(math.sin(step / 10.0)))

Multiple time series plots multiple-time-series-plots

"""Log a scalar over time."""

from math import cos, sin, tau

import numpy as np
import rerun as rr

rr.init("rerun_example_scalar_multiple_plots", spawn=True)
lcg_state = np.int64(0)

# Set up plot styling:
# They are logged as static as they don't change over time and apply to all timelines.
# Log two lines series under a shared root so that they show in the same plot by default.
rr.log("trig/sin", rr.SeriesLine(color=[255, 0, 0], name="sin(0.01t)"), static=True)
rr.log("trig/cos", rr.SeriesLine(color=[0, 255, 0], name="cos(0.01t)"), static=True)
# Log scattered points under a different root so that they show in a different plot by default.
rr.log("scatter/lcg", rr.SeriesPoint(), static=True)

# Log the data on a timeline called "step".
for t in range(0, int(tau * 2 * 100.0)):
    rr.set_time_sequence("step", t)

    rr.log("trig/sin", rr.Scalar(sin(float(t) / 100.0)))
    rr.log("trig/cos", rr.Scalar(cos(float(t) / 100.0)))

    lcg_state = (1140671485 * lcg_state + 128201163) % 16777216  # simple linear congruency generator
    rr.log("scatter/lcg", rr.Scalar(lcg_state))
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