def test_ODE_stdev():
"""
Make sure that parameters from ODEModels get standard deviations.
"""
x, v, t = sf.variables('x, v, t')
k = sf.Parameter(name='k')
k.min = 0
k.value = 10
a = -k * x
model = sf.ODEModel({
sf.D(v, t): a,
sf.D(x, t): v,
},
initial={
v: 0,
x: 1,
t: 0
})
t_data = np.linspace(0, 10, 150)
noise = np.random.normal(1, 0.05, t_data.shape)
x_data = model(t=t_data, k=11).x * noise
v_data = model(t=t_data, k=11).v * noise
fit = sf.Fit(model, t=t_data, x=x_data, v=v_data)
result = fit.execute()
assert result.stdev(k) is not None
assert np.isfinite(result.stdev(k))
def test_harmonic_oscillator_errors():
"""
Make sure the errors produced by fitting ODE's are the same as when
fitting an exact solution.
"""
x, v, t = sf.variables('x, v, t')
k = sf.Parameter(name='k', value=100)
m = 1
a = -k/m * x
ode_model = sf.ODEModel({sf.D(v, t): a,
sf.D(x, t): v},
initial={t: 0, v: 0, x: 1})
t_data = np.linspace(0, 10, 250)
np.random.seed(2)
noise = np.random.normal(1, 0.05, size=t_data.shape)
x_data = ode_model(t=t_data, k=100).x * noise
ode_fit = sf.Fit(ode_model, t=t_data, x=x_data, v=None)
ode_result = ode_fit.execute()
phi = 0
A = 1
model = sf.Model({x: A * sf.cos(sf.sqrt(k/m) * t + phi)})
fit = sf.Fit(model, t=t_data, x=x_data)
result = fit.execute()
assert result.value(k) == pytest.approx(ode_result.value(k), 1e-4)
assert result.stdev(k) == pytest.approx(ode_result.stdev(k), 1e-2)
assert result.stdev(k) >= ode_result.stdev(k)
print(country, N_0)
model_dict = {
sf.D(S, t): -β * I * S / N,
sf.D(I, t): β * I * S / N - γ * I - μ * I,
sf.D(R, t): γ * I,
sf.D(D, t): μ * I,
}
p_infected = 0.01
I_0, R_0, D_0 = N_mu * p_infected, N_mu * p_infected - 100.0, 100.0
S_0 = N_mu - I_0 - R_0 - D_0
ode_model[country] = sf.ODEModel(model_dict,
initial={
t: 0.0,
S: S_0,
I: I_0,
R: R_0,
D: D_0
})
idx = sorted_data["country"] == country
x = sorted_data[idx]["days_since_100_cases"].values
y = sorted_data[idx]["cumulative_deaths"].values
fit = sf.Fit(
ode_model[country],
t=x,
S=None,
I=None,
R=None,
D=y,
