Tensnap Python Bindings

Python bindings for the TenSnap protocol v0.3 runtime.

Installation

pip install tensnap

Quick Start

import asyncio

from tensnap import (
    SimulationScenario,
    agent,
    agent_layer,
    chart,
    env,
    grid_layer,
    params,
)


@agent(x="position[0]", y="position[1]")
class Bird:
    def __init__(self, bird_id: int, position: tuple[int, int]):
        self.id = bird_id
        self.position = position


@grid_layer()
@agent_layer("birds")
@env(id="main")
class Aviary:
    def __init__(self):
        self.width = 20
        self.height = 10
        self.birds = [Bird(1, (2, 3)), Bird(2, (4, 5))]

    def step(self) -> None:
        for bird in self.birds:
            x, y = bird.position
            bird.position = (x + 1, y)

    @chart("population", "Population")
    def population(self) -> int:
        return len(self.birds)


@params(include=["speed"])
class Config:
    speed = 1.0


scenario = SimulationScenario(port=8765)
model = Aviary()
config = Config()

scenario.add_all(model)
scenario.add_all(config)


async def main() -> None:
    await scenario.register_model_handler(model_step=model.step)
    await scenario.run()


if __name__ == "__main__":
    asyncio.run(main())

tensnap.bindings is the unified attach/readback surface for Python bindings. Use it for decorators such as env, grid_layer, and agent_layer, and for readback helpers such as environment_binding, layer_bindings, and bindings.

Layer and item binding fields now support these common forms:

• omitted fields, None, or auto() for one-time same-name discovery from the class or initialized instance
• selector strings such as x="pos[0]" or item_iterable_projector="agents" when the source path differs from the output field or layer id
• literal values such as coord_offset="float" or icon="circle"; arbitrary strings still default to selectors unless they match a known literal value, CSS-like string such as "#3498db", or JSON-like string
• explicit helpers such as attr("pos[0]"), value("red"), auto(), and skip()
• direct callables such as size=lambda agent: agent.radius

None/auto() field discovery is resolved once when instances initialize or when layer_bindings(instance) materializes an instance-specific binding, so fields assigned in __init__ are picked up without scanning during projection:

from tensnap import agent, agent_layer, grid_layer, value


@agent(x="position[0]", y="position[1]", color=value("red"))
class Bird:
    def __init__(self, bird_id: int, position: tuple[int, int]):
        self.id = bird_id
        self.position = position


@grid_layer()
@agent_layer("birds")
class Aviary:
    def __init__(self):
        self.width = 20
        self.height = 10
        self.birds = [Bird(1, (2, 3))]

SimulationScenario registers the renderer-driven built-in actions start, step, and reset during construction. The initial synchronized state is always time 0, and the first simulated tick emitted by start or step is 1. For most targets, use scenario.add_all(target) to register available environment/layer, chart, and action bindings. Parameters are opt-in with @params(...) or an explicit config.

register_model_handler(model_init=None, model_step=None, model_reset=None) lets you keep reset distinct from init. If model_reset is omitted, the default handler falls back to model_init.

Reset reconciles declarations with strict CRUD, deletes the previous agent set before creating reset state, clears chart history, and preserves stable trajectory configuration so the renderer can apply on_reset.

add_all(..., dry_run=True) and the targeted add_* dry-run modes report registry ids without mutating the scenario. Mesa lifecycle helpers use this to unregister and rebuild model-owned registrations cleanly.

Trajectory layers expose length, width, color, z_index, on_agent_delete, on_state_sync, and on_reset as first-class decorator arguments. See the Python API reference for their lifecycle semantics.

Monitors use explicit create/update/delete protocol operations. Scene checkpoint callbacks work with model data only: provide both checkpoint_capture and checkpoint_restore, and the binding infers the wire encoding. Pass restore=None to @scene_restore(...) for checkpoint-only models. Every connection starts with a simulator_info handshake containing stable model identity, per-instance identity, schema compatibility, and declared capabilities.

For Mesa models, prefer BoundModelReinitializer from tensnap or tensnap.bindings.lifecycle. Use bind_kwargs(...) to expose constructor keyword arguments as resettable parameters when needed. When a constructor kwarg and a model-owned parameter publish the same source field during register_model(), the model parameter keeps UI ownership and the reinitializer aliases its reset value from the model field instead of registering a duplicate kwarg parameter. configure_reinit(...) now applies the default Mesa cleanup automatically when no explicit cleanup callback is provided.

Examples

Example simulations are located in the repository root:

• examples/python/ - Standard Python examples (flock, hk, sirs)
• examples/python_mesa/ - Mesa-based examples (cgol, sugarscape, mushroom)

Documentation

Full documentation: https://github.com/billstark001/tensnap

License

See LICENSE file in the repository root.