Sinks

Histogram Block

Visualizes data distribution

# Histogram Block

Description

The Histogram block accumulates incoming scalar values over the simulation run and renders their frequency distribution as a bar histogram. Values are sorted into a fixed number of equal-width bins spanning a user-defined range, giving an at-a-glance view of the statistical character of a signal.

This block is well suited to noise analysis, Monte Carlo result inspection, and quality-control monitoring where the shape of the distribution — not the time-series waveform — is the quantity of interest.

Parameters

bins

Number of equal-width bins that span the [minValue, maxValue] range. Higher values give finer resolution at the cost of requiring more samples before the distribution stabilises. Default: 20.

minValue

Lower bound of the histogram range. Samples below this value are clamped to the first bin. Default: -5.

maxValue

Upper bound of the histogram range. Samples above this value are clamped to the last bin. Default: 5.

Examples

Analysing sensor noise

NoiseSensor → Histogram

Connect a noisy sensor output directly to the Histogram to observe whether the noise is approximately Gaussian and to measure its spread.

Monte Carlo output distribution

RandomSource → Plant → Histogram

Drive the plant with a random input source; the Histogram displays the resulting output distribution across the simulation run.

Remarks

Sink block: Histogram has no output ports. It is a terminal display node and does not feed any downstream computation.
Scalar input: The input port accepts a scalar numeric signal. Each simulation step contributes one sample to the accumulated distribution.
Accumulation: Unlike instantaneous display blocks, the Histogram accumulates all samples from simulation start. The distribution becomes more stable as the run length increases.
Range clamping: Samples outside [minValue, maxValue] are counted in the boundary bins, not discarded. Adjust the range if the distribution appears heavily clipped.