jupedsim-scenarios

Python toolkit for running, sweeping, and analyzing JuPedSim scenarios authored in the Web-Based JuPedSim editor.

Intro video (3 min):

Install

pip install jupedsim-scenarios

For development from a clone:

pip install -e ".[dev]"

Single-run usage

from jupedsim_scenarios import load_scenario, run_scenario

scenario = load_scenario("my_scenario.zip")
result = run_scenario(scenario, seed=42)
print(result.evacuation_time)
df = result.trajectory_dataframe()
result.cleanup()

load_scenario accepts a ZIP archive, a directory containing <name>.json + <name>.wkt, or a single self-contained JSON file (geometry embedded as walkable_area_wkt).

To build a Scenario in pure Python — without going through a web-app export — see examples/howtos/09_build_from_scratch.ipynb.

Already writing vanilla jupedsim code by hand? See examples/howtos/12_from_vanilla_jupedsim.ipynb: the same bottleneck study in ~45 lines of vanilla JuPedSim vs. ~10 lines here, plus a parameters × seeds sweep in one call.

Quick CLI: run a ZIP and bundle the trajectory

examples/run_zip.py loads a scenario ZIP, runs it, prints summary metrics (evacuation time, wall-clock time, agent counts), and writes <name>_run.zip containing the original config.json + geometry.wkt plus trajectory.sqlite — ready to drop back into the web app for visualization.

python examples/run_zip.py my_scenario.zip [seed]

Mutating a scenario — copy first, then assign

Scenario is a mutable object. Direct assignments and add_* / remove_* / set_* calls all change the instance in place:

base = load_scenario(...)
base.seed = 99            # this mutates `base` — every later use sees seed=99

That's fine when you only want one variant. For sweeps or any time you want to keep the original intact, call .copy() first and edit the clone:

trial = base.copy()
trial.seed = 99
trial.max_simulation_time = 60
# base is untouched

run_sweep does this for you per trial. The pattern only matters when you build variants manually (see examples/howtos/11_sweep_via_copy.ipynb).

Monte Carlo sweep

from jupedsim_scenarios import load_scenario, run_sweep

base = load_scenario("faster_is_slower.zip")

sweep = run_sweep(
    base,
    axes={"v0": [0.8, 1.2, 1.6, 1.8]},
    apply={"v0": lambda s, v: s.set_agent_params(0, desired_speed=v)},
    seeds=range(40, 50),
    workers=4,
)

df = sweep.to_dataframe()
print(df.groupby("v0")["evacuation_time"].agg(["mean", "std"]))
sweep.cleanup()

run_sweep walks the cartesian product of axes, applies each axis's mutator to an isolated .copy() of the base, and runs the trials. workers=0 uses one worker per CPU. For sweeps that need a different scenario shape per trial — geometry that depends on the parameters, journeys that vary — use run_sweep_from_factory instead.

For deeper coverage see the how-to notebooks:

• 05_sweep_basics — axes / apply / paired conditions
• 10_sweep_save_load — SweepResult.save / load
• 11_sweep_via_copy — factory sweeps and Scenario.copy()

Visualisation

Visualisation needs the optional viz extra (matplotlib + plotly):

pip install "jupedsim-scenarios[viz]"

Two entry points cover the common cases:

scenario = load_scenario("my_scenario.zip")
scenario.plot()                      # static geometry: distributions, exits, zones, checkpoints

result = run_scenario(scenario, seed=42)
result.visualise()                   # interactive plotly playback of the trajectory
result.visualise(save_path="run.html")  # write a self-contained interactive page

Scenario.plot() returns a matplotlib Axes; pass ax= to draw into an existing figure, or show_journeys=False / show_trajectories= to control overlays. ScenarioResult.visualise() returns a plotly.graph_objects.Figure that renders inline in Jupyter — just make it the last line of a cell. Frames are subsampled automatically for long runs; override with every_nth_frame.

See 02_visualisation for a full walkthrough.

Command line

jps-scenarios run scenario.json --seed 42 --out trajectory.sqlite

Runs a single scenario and prints a one-line JSON summary (evacuation_time, agent counts, sqlite_file) to stdout. Useful in CI or scripted pipelines; notebook workflows should stay on the Python API.

Documentation

API reference, bottleneck tutorial, and how-tos are built with Sphinx and deployed on every push to main via GitHub Pages.

To build locally:

pip install -e .
pip install -r docs/requirements.txt
sphinx-build -b html docs/source docs/build/html

Roadmap

Shipped: see CHANGELOG.md. Current release: 0.6.4.5.

On the table for future releases:

• Greenfield Scenario() constructor — a builder shape that doesn't require pre-loading a JSON template (R3.7 in docs/dev/api-design-cleanup.md).
• Typed Zone / Stage view classes with property setters, replacing the set_zone_speed_factor / set_checkpoint_waiting_time wrappers (R3.10).
• Removal of the v0 / v0_std / v0_distribution deprecated kwargs (currently still accepted with DeprecationWarning).

Concrete proposals are tracked under issues.

Citation

If jupedsim-scenarios supports work you publish, please cite the upstream JuPedSim project and link to this repository. A dedicated DOI for this toolkit will be added once a Zenodo deposit is in place.

Contributing

See CONTRIBUTING.md for the dev install, local checks, and PR conventions.

License

MIT. See LICENSE.