Diffusion Animator

The Diffusion Animator visualises a diffusion process as a frame-by-frame animation of the network graph. Node colours reflect the simulated status at each iteration: a categorical palette is used for discrete-state models (e.g. SIR) and a sequential colourmap (RdYlBu_r) for continuous-opinion models.

class ndlib.viz.mpl.DiffusionAnimator.DiffusionAnimator(model, iterations, pos=None)
DiffusionAnimator.__init__(model, iterations, pos)
Parameters:

  • model – A configured and executed NDLib diffusion model.

  • iterations – Output of model.iteration_bunch().

  • pos – Optional dict mapping node id → (x, y). If None, a spring layout is computed automatically.

DiffusionAnimator.plot()

Build and return a matplotlib.animation.FuncAnimation of the diffusion process over the graph.

The returned object can be displayed inline in a Jupyter notebook or passed to save_gif().

Returns:

matplotlib.animation.FuncAnimation

DiffusionAnimator.save_gif(filename, fps)

Save the animation as a GIF file.

Parameters:

  • filename – Output path, e.g. "diffusion.gif".

  • fps – Frames per second (default 10).

Below is a minimal example for the SIR model.

import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics as epd
from ndlib.viz.mpl.DiffusionAnimator import DiffusionAnimator
import networkx as nx

# Network topology
g = nx.erdos_renyi_graph(20, 0.2)

# Model selection
model = epd.SIRModel(g)

# Model configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.5)
cfg.add_model_parameter('gamma', 0.1)
cfg.add_model_parameter("fraction_infected", 0.2)
model.set_initial_status(cfg)

# Simulation execution
iterations = model.iteration_bunch(20)

# Animation – returns matplotlib.animation.FuncAnimation
viz = DiffusionAnimator(model, iterations)
anim = viz.plot()                  # display inline in Jupyter, or …
viz.save_gif("sir.gif", fps=3)     # … export as a GIF