Log a double-precision scalar that will be visualized as a time-series plot.

The current simulation time will be used for the time/X-axis, hence scalars cannot be timeless!


Required: Scalar

Recommended: Radius, Color

Optional: Text, ScalarScattering


Simple line plot

"""Log a scalar over time.""" import math import rerun as rr rr.init("rerun_example_scalar", spawn=True) for step in range(0, 64): rr.set_time_sequence("step", step) rr.log("scalar", rr.TimeSeriesScalar(math.sin(step / 10.0)))

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) for t in range(0, int(tau * 2 * 100.0)): rr.set_time_sequence("step", t) # Log two time series under a shared root so that they show in the same plot by default. rr.log("trig/sin", rr.TimeSeriesScalar(sin(float(t) / 100.0), label="sin(0.01t)", color=[255, 0, 0])) rr.log("trig/cos", rr.TimeSeriesScalar(cos(float(t) / 100.0), label="cos(0.01t)", color=[0, 255, 0])) # Log scattered points under a different root so that they shows in a different plot by default. lcg_state = (1140671485 * lcg_state + 128201163) % 16777216 # simple linear congruency generator rr.log("scatter/lcg", rr.TimeSeriesScalar(lcg_state, scattered=True))