llms.txt

# schedule-visualizer

> Dry-run timeline of a reinforcement schedule: from a contingency-dsl string (FR, VR, RR, FI, VI, RI, FT, VT, RT, CRF, EXT; Conc, Mult, Chain) and a duration, lists and draws when reinforcement would become available, one lane per component, without running a session.

schedule-visualizer is part of OperantKit, a set of Python/Rust packages for designing and running operant and respondent conditioning experiments (reinforcement schedules, session recording, analysis). Use it to preview a schedule while designing an experiment. It does not simulate responding: ratio schedules are shown as response-count annotations, and variable intervals are drawn from an exponential distribution for illustration rather than from the Fleshler-Hoffman progression used when the schedule actually runs.

## Install

```bash
pip install git+https://github.com/OperantKit/schedule-visualizer
```

## Minimal example

```python
from schedule_visualizer.render import render_timeline
from schedule_visualizer.stepper import step_schedule

timeline = step_schedule("FI 30s", duration_s=90.0)
print([e.timestamp for e in timeline.events()])  # [30.0, 60.0, 90.0]
render_timeline(timeline, "fi.png")
```

CLI: `schedule-visualizer visualize "Conc(VI 30s, VI 60s)" --duration 120 --output conc.png --seed 7`.

## Docs

- [README](https://github.com/OperantKit/schedule-visualizer/blob/main/README.md): scope, 30-second example, supported schedule subset
- [AGENTS.md](https://github.com/OperantKit/schedule-visualizer/blob/main/AGENTS.md): when to use this package and what not to do

## Related packages

- [contingency-dsl](https://github.com/OperantKit/contingency-dsl): the schedule language specification
- [schedule-writer](https://github.com/OperantKit/schedule-writer): builds schedule strings without writing DSL syntax
- [contingency-py](https://github.com/OperantKit/contingency-py): runs the schedule against responses (`pip install contingency-py`)
- [experiment-io](https://github.com/OperantKit/experiment-io): virtual subjects for simulated sessions (`pip install experiment-io`)