def test_invalid_sim(self):
# Run simulation and assure that the results is correct
self.lsqpe = pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
# No x0
self.assertRaises(ValueError, self.lsqpe.run_simulation, \
x0 = None, psim = self.phat)
# Wrong dimension for x0
self.assertRaises(ValueError, self.lsqpe.run_simulation, \
x0 = self.yN[0, :-1], psim = self.phat)
# No psim
self.assertRaises(AttributeError, self.lsqpe.run_simulation, \
x0 = self.yN[0, :])
# Wrong dimension for psim
self.assertRaises(ValueError, self.lsqpe.run_simulation, \
x0 = self.yN[0, :], psim = self.phat[:-1])
# Unsupported integrator
self.assertRaises(RuntimeError, self.lsqpe.run_simulation, \
x0 = self.yN[0, :], psim = self.phat, method = "dummy")
def test_covmat_invalid(self):
self.lsqpe = pecas.LSq(system = self.bsys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN, wv = self.wv)
self.assertRaises(AttributeError, self.lsqpe.compute_covariance_matrix)
def test_pe_invalid_method(self):
self.lsqpe = pecas.LSq(system = self.bsys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN, wv = self.wv)
self.assertRaises(NotImplementedError, \
self.lsqpe.run_parameter_estimation, hessian = "dummy")
def test_sim(self):
# There is no simulation for BasicSystem
self.lsqpe = pecas.LSq(system = self.bsys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN, wv = self.wv)
self.assertRaises(NotImplementedError, \
self.lsqpe.run_simulation, x0 = None)
def test_plot_ellipsoid_invalid_call(self):
self.lsqpe = pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
self.assertRaises(AttributeError, \
self.lsqpe.plot_confidence_ellipsoids)
def test_valid_sim(self):
# Run simulation and assure that the results is correct
self.lsqpe = pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
self.lsqpe.run_simulation(x0 = self.yN[0, :], psim = self.phat)
def test_valid_lsq_init(self):
# Test valid setup cases
pecas.LSq(system = self.bsys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN, wv = self.wv)
pecas.LSq(system = self.bsys, \
tu = np.atleast_2d(self.tu).T, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN, wv = self.wv)
pecas.LSq(system = self.bsys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN.T, wv = self.wv)
pecas.LSq(system = self.bsys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN, wv = self.wv.T)
def lsq_run_gauss_newton(self):
# Run parameter estimation and assure that the results is correct
self.lsqpe = pecas.LSq(system = self.bsys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
yN = self.yN, wv = self.wv)
self.assertRaises(AttributeError, getattr, self.lsqpe, "phat")
self.assertRaises(AttributeError, getattr, self.lsqpe, "Xhat")
self.assertRaises(AttributeError, self.lsqpe.show_results)
self.lsqpe.run_parameter_estimation(hessian="gauss-newton")
phat = self.lsqpe.phat
testing.assert_almost_equal(phat, self.phat, decimal=5)
self.lsqpe.show_results()
def lsq_run_exact_hessian(self):
# Run parameter estimation and assure that the results is correct
self.lsqpe = pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
self.assertRaises(AttributeError, getattr, self.lsqpe, "phat")
self.assertRaises(AttributeError, getattr, self.lsqpe, "Xhat")
self.assertRaises(AttributeError, self.lsqpe.show_results)
self.lsqpe.run_parameter_estimation(hessian="exact-hessian")
phat = self.lsqpe.phat
Xhat = self.lsqpe.Xhat
testing.assert_almost_equal(phat, self.phat, decimal=5)
self.lsqpe.show_results()
# The weightings for the measurements errors given to PECas are calculated
# from the standard deviations of the measurements, so that the least squares
# estimator ist the maximum likelihood estimator for the estimation problem.
wnu = 1.0 / (pl.ones(tu.size)*sigmanu**2)
ww = 1.0 / (pl.ones(tu.size)*sigmaw**2)
wv = pl.array([wnu, ww])
# Run parameter estimation and assure that the results is correct
lsqpe = pecas.LSq( \
system = odesys, tu = tu, \
uN = uN, \
pinit = 1, \
xinit = yN,
# linear_solver = "ma97", \
yN = yN, wv = wv)
# lsqpe.run_parameter_estimation(hessian = "gauss-newton")
lsqpe.run_parameter_estimation(hessian = "exact-hessian")
lsqpe.show_results()
lsqpe.compute_covariance_matrix()
lsqpe.show_results()
lsqpe.run_simulation([numeas[0], wmeas[0]])
nusim = lsqpe.Xsim[0,:].T
wsim = lsqpe.Xsim[1,:].T
odesys = pecas.systems.ExplODE( \
x = x, u = u, p = p, f = f, phi = phi)
dt = 1.0 / fs
tu = pl.linspace(0, N, N + 1) * dt
uN = ca.DMatrix(0.1 * pl.random(N))
yN = pl.zeros((x.shape[0], N + 1))
wv = pl.ones(yN.shape)
lsqpe_sim = pecas.LSq( \
system = odesys, tu = tu, \
uN = uN, \
pinit = p_guess, \
xinit = yN,
# linear_solver = "ma97", \
yN = yN, wv = wv)
lsqpe_sim.run_simulation(x0=[0.0, 0.0], psim=p_true / scale)
p_test = []
sigma = 0.01
wv = (1. / sigma**2) * pl.ones(yN.shape)
repetitions = 100
for k in range(repetitions):
delimiter = ", ", skiprows = 1))
ty = data[100:250, 1]
yN = data[100:250, [2, 4, 6, 8]]
wv = pl.ones(yN.shape)
uN = data[100:249, [9, 10]]
pinit = [0.5, 17.06, 12.0, 2.17, 0.1, 0.6]
lsqpe = pecas.LSq(system = odesys, \
tu = ty, uN = uN, \
pinit = pinit, \
ty = ty, yN =yN, \
wv = wv, \
xinit = yN, \
linear_solver = "ma97", \
scheme = "radau", \
order = 3)
lsqpe.run_parameter_estimation(hessian="exact-hessian")
lsqpe.run_simulation(x0=yN[0, :])
xhat = lsqpe.Xsim[0, :].T
yhat = lsqpe.Xsim[1, :].T
psihat = lsqpe.Xsim[2, :].T
vhat = lsqpe.Xsim[3, :].T
pl.close("all")
phi = x
odesys = pecas.systems.ExplODE( \
x = x, u = u, p = p, f = f, phi = phi)
dt = 1.0 / fs
ty = pl.linspace(0, N, N + 1) * dt
u_data = ca.DMatrix(0.1 * pl.random(N))
y_data_dummy = pl.zeros((x.shape[0], N + 1))
wv_dummy = y_data_dummy
lsqpe_dummy = pecas.LSq( \
system = odesys, \
tu = ty, \
uN = u_data, \
yN = y_data_dummy, \
wv = wv_dummy)
lsqpe_dummy.run_simulation(x0=[0.0, 0.0], psim=p_true / scale)
y_data = lsqpe_dummy.Xsim
y_data += 1e-3 * pl.random((x.shape[0], N + 1))
wv = pl.ones(y_data.shape)
lsqpe = pecas.LSq( \
system = odesys, \
tu = ty, \
uN = u_data, \
yN = y_data, \
def test_valid_lsq_init(self):
# Test valid setup cases
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = np.atleast_2d(self.tu).T, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
ty = self.tu, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
ty = np.atleast_2d(self.tu).T, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN.T, \
wv = self.wv, weps_e = self.weps_e, weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv.T, weps_e = self.weps_e, weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = [self.weps_e], weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = np.atleast_1d(self.weps_e), \
weps_u = self.weps_u)
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, weps_u = [self.weps_u])
pecas.LSq(system = self.odesys, \
tu = self.tu, uN = self.uN, \
pinit = self.pinit, \
xinit = self.xinit, \
yN = self.yN, \
wv = self.wv, weps_e = self.weps_e, \
weps_u = np.atleast_1d(self.weps_u))
odesys = pecas.systems.ExplODE(x=x, p=p, f=f, phi=phi)
odesys.show_system_information(showEquations=True)
# The weightings for the measurements errors given to PECas are calculated
# from the standard deviations of the measurements, so that the least squares
# estimator ist the maximum likelihood estimator for the estimation problem.
wv = pl.zeros((2, yN.shape[1]))
wv[0, :] = (1.0 / sigma_x1**2)
wv[1, :] = (1.0 / sigma_x2**2)
lsqpe = pecas.LSq(system = odesys, \
tu = T, \
pinit = [0.5, 1.0], \
xinit = yN, \
yN = yN, \
# linear_solver = "ma97", \
wv = wv)
lsqpe.run_parameter_estimation(hessian="gauss-newton")
# lsqpe.run_parameter_estimation(hessian = "exact-hessian")
lsqpe.show_results()
lsqpe.compute_covariance_matrix()
lsqpe.show_results()
t = pl.linspace(0, 10, 1000)
lsqpe.run_simulation(x0=yN[:, 0], tsim=t)
pl.figure()
