floxs.predator_prey.viewer.PredatorPreyViewer

class floxs.predator_prey.viewer.PredatorPreyViewer(name: str = 'predator_prey', env_size: Tuple[float, float] = (1.0, 1.0), prey_color: str = '#5ec962', predator_color: str = '#440154', render_mode: str = 'human')

Bases: jumanji.viewer.MatplotlibViewer[floxs.predator_prey.types.State]

Predator-prey environment visualiser

Parameters:

  • name – Plot name, default predator_prey

  • env_size – Tuple containing the dimensions of the environment

  • prey_color – Color applied to prey agents

  • predator_color – Color applied to predator agents

  • render_mode – Default human

animate(states: Sequence[floxs.predator_prey.types.State], interval: int, save_path: str | None = None) → matplotlib.animation.FuncAnimation

Create an animation from a sequence of states.

Parameters:

  • states – Sequence of State objects corresponding to subsequent timesteps.

  • interval – Delay between frames in milliseconds, default to 200.

  • save_path – The path where the animation file should be saved. If it is None, the plot will not be saved

Returns:

Animation object that can be saved as a GIF, MP4, or rendered with HTML

Return type:

FuncAnimation

render(state: floxs.predator_prey.types.State, save_path: str | None = None) → numpy.typing.NDArray | None

Render a frame of the environment for a given state using matplotlib.

Parameters:

  • state – State object containing the current dynamics of the environment.

  • save_path – Optional path to save the rendered environment image to.

Returns:

RGB array if the render_mode is ‘rgb_array’.

Return type:

Array