def test_fit_simualtion():
parameters = {
'B_1A': 0.7,
'B_2A': 0.0,
'B_3A': 0.0,
'C_1A': 10.0,
'C_3A': 0.0,
'C_5A': 0.0,
}
bounds = {
'B_1A': (-np.inf, np.inf), # Assuming only positive coefficients
'B_2A': (-np.inf, np.inf), # Assuming only positive coefficients
'B_3A': (-np.inf, np.inf), # Assuming only positive coefficients
}
direct_estimator = EstimatorCubic(fit_method='integration', bounds=bounds)
phi0 = np.deg2rad(10)
phi1d0 = 0
t = np.arange(0, 10, 0.01)
X = simulate(t=t, phi0=phi0, phi1d0=phi1d0, **parameters)
X['phi2d'] = np.gradient(X['phi1d'].values, X.index.values)
check(X=X, estimator=direct_estimator, parameters=parameters)
def test_fit_simualtion_full_cubic():
parameters = {
'B_1A': 0.7,
'B_2A': 1.0,
'B_3A': 3.0,
'C_1A': 10.0,
'C_3A': 10.0,
'C_5A': 0.0,
}
direct_estimator = EstimatorCubic(fit_method='integration')
phi0 = np.deg2rad(20)
phi1d0 = 0
t = np.arange(0, 10, 0.01)
X = simulate(t=t, phi0=phi0, phi1d0=phi1d0, **parameters)
X['phi2d'] = np.gradient(X['phi1d'].values, X.index.values)
check(X=X, estimator=direct_estimator, parameters=parameters)
def test_simulation():
parameters = {
'B_1A': 0.3,
'B_2A': 0.0,
'B_3A': 0.0,
'C_1A': 0.3,
'C_3A': 0.0,
'C_5A': 0.0,
}
phi0 = np.deg2rad(10)
phi1d0 = 0
t = np.arange(0, 10, 0.1)
X = simulate(t=t, phi0=phi0, phi1d0=phi1d0, **parameters)
direct_estimator = EstimatorCubic.load(**parameters)
direct_estimator.is_fitted_ = True
X_pred = direct_estimator.predict(X=X)
assert_almost_equal(X['phi'].values, X_pred['phi'].values)
def test_fit_simualtion_cubic_damping_not_converged():
parameters = {
'B_1A': 0.7,
'B_2A': 1.5,
'B_3A': 5.0,
'C_1A': 10.0,
'C_3A': 0.0,
'C_5A': 0.0,
}
direct_estimator = EstimatorCubic(
fit_method='integration',
maxfev=1,
)
phi0 = np.deg2rad(10)
phi1d0 = 0
t = np.arange(0, 10, 0.01)
X = simulate(t=t, phi0=phi0, phi1d0=phi1d0, **parameters)
X['phi2d'] = np.gradient(X['phi1d'].values, X.index.values)
with pytest.raises(FitError):
check(X=X, estimator=direct_estimator, parameters=parameters)
def simulate(t, phi0, phi1d0, **kwargs):
estimator = EstimatorCubic.load(**kwargs)
return estimator.simulate(t=t, phi0=phi0, phi1d0=phi1d0)