Functions Continuous Model

MidpointNormalize

MidpointNormalize(colors.Normalize)

Normalise the colorbar.

The function normalises a colorbar so that diverging bars work there way either side from a prescribed midpoint value), e.g. im=ax1.imshow(array, norm=MidpointNormalize(midpoint=0.,vmin=-100, vmax=100))

Source :Link

time_evolution

time_evolution(tau, Lambda_0, T, epsilon=epsilon, s=s, beta=beta, age=age)

Simulate the continuous model on a discrete time grid.

The function computes the time evolution of the number of infected people according to the dynamics defined by the continuous model, which is discretized on an equally-spaced time grid.

INPUT

• tau - np.array time grid
• Lambda_0 - np.array initial number of infected people, with varying infection age
• T - float simulation time
• epsilon - function that returns a (possibly time-dependend) pair (eps_I, eps_T)
• beta - function time-dependent infectiousness
• age - float maximal time since infection of the initially infected people

OUTPUT

• np.sum(Lambda, axis=1)[age:] - cumulative distribution of the infected people
• Lambda - time distribution of the infected people
• A - system evolution matrix