A, C, G, R = worker.A, worker.C, worker.G, worker.R

xhat_0, Σ_0 = worker.xhat_0, worker.Σ_0

if isinstance(mu_0, type(None)):

if mu_0 is None:

mu_0 = xhat_0

if isinstance(Sigma_0, type(None)):

if Sigma_0 is None:

Sigma_0 = worker.Σ_0

ss = LinearStateSpace(A, C, G, np.sqrt(R),

kalman.update(y[i])

x_hat, Σ = kalman.x_hat, kalman.Sigma

Σ_t.append(Σ)

[y_hat_t[i]] = worker.G @ x_hat

[u_hat_t[i]] = x_hat[1]

y_hat_t[i] = (worker.G @ x_hat).item()

u_hat_t[i] = x_hat[1].item()

if diff == True:

if diff :

title = ('Difference between inferred and true work ethic over time'

if title == None else title)

if title is None else title)

ax.plot(u_hat_t - u_0, alpha=.5)

ax.axhline(y=0, color='grey', linestyle='dashed')

ax.set_title(title)

else:

label_line = (r'$E[u_t|y^{t-1}]$' if name == None

label_line = (r'$E[u_t|y^{t-1}]$' if name is None

else name)

title = ('Inferred work ethic over time'

if title == None else title)

if title is None else title)

u_hat_plot = ax.plot(u_hat_t, label=label_line)

ax.axhline(y=u_0, color=u_hat_plot[0].get_color(),