def test_ackermann_args():
# Not SIxO system
G = State(eye(2), eye(2), eye(2))
assert_raises(ValueError, ackermann, G, [1, 2])
# Wrong # of poles
G = State(eye(2), [[1], [0]], [1, 0])
assert_raises(ValueError, ackermann, G, [1, 2, 3])
def test_State_algebra_mimo_siso():
static_siso_state = State(5)
static_mimo_state = State(2.0*np.eye(3))
dynamic_siso_state = State(haroldcompanion([1, 3, 3, 1]),
e_i(3, -1),
e_i(3, 1).T,
0)
dynamic_mimo_state = State(haroldcompanion([1, 3, 3, 1]),
e_i(3, [1, 2]),
np.eye(3),
np.zeros((3, 2)))
dynamic_square_state = State(haroldcompanion([1, 3, 3, 1]),
np.eye(3),
np.eye(3),
np.zeros((3, 3))
)
assert_raises(IndexError, dynamic_siso_state.__mul__, static_mimo_state)
assert_raises(IndexError, dynamic_siso_state.__add__, static_mimo_state)
assert_raises(IndexError, static_mimo_state.__add__, dynamic_mimo_state)
assert_raises(IndexError, static_mimo_state.__add__, static_siso_state)
assert_raises(IndexError, static_mimo_state.__mul__, static_siso_state)
F = static_mimo_state @ dynamic_mimo_state
assert_almost_equal(F.c, np.eye(3)*2.0)
assert_almost_equal((dynamic_square_state + static_mimo_state).d,
2*np.eye(3))
def test_minimal_realization_State():
M = array([[-6.5, 0.5, 6.5, -6.5, 0., 1., 0.],
[-0.5, -5.5, -5.5, 5.5, 2., 1., 2.],
[-0.5, 0.5, 0.5, -6.5, 3., 4., 3.],
[-0.5, 0.5, -5.5, -0.5, 3., 2., 3.],
[1., 1., 0., 0., 0., 0., 0.]])
G = State(*matrix_slice(M, (1, 4), corner='sw'))
H = minimal_realization(G)
assert H.a.shape == (2, 2)
#
G = State(
array([[0., 1., 0., 0., 0.], [-0.1, -0.5, 1., -1., 0.],
[0., 0., 0., 1., 0.], [0., 0., 0., 0., 1.],
[0., 3.5, 1., -2., 2.]]), array([[0.], [1.], [0.], [0.], [1.]]),
array([[0., 3.5, 1., -1., 0.]]), array([[1.]]))
H = minimal_realization(G)
assert H.a.shape == (4, 4)
#
G = State(
array([[-2., 0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.],
[0., -12., 4., 3.]]),
array([[1., 0.], [0., 0.], [0., 0.], [0., 1.]]),
array([[1., -9., 0., 0.], [0., -20., 0., 5.]]),
array([[0., 0.], [0., 1.]]))
H = minimal_realization(G)
assert H.a.shape == (3, 3)
def test_State_Instantiations():
assert_raises(TypeError, State)
G = State(5)
assert G.a.size == 0
assert G._isSISO
assert G._isgain
assert_equal(G.d, np.array([[5.]]))
G = State(np.eye(2))
assert_equal(G.shape, (2, 2))
assert G._isgain
assert not G._isSISO
assert_raises(ValueError, State,
np.eye(2),
np.array([[1], [2], [3]]),
np.array([1, 2]),
0)
assert_raises(ValueError, State,
np.eye(2),
np.array([[1], [2]]),
np.array([1, 2, 3]),
0)
assert_raises(ValueError, State,
np.eye(2),
np.array([[1], [2]]),
np.array([1, 2]),
np.array([0, 0]))
def test_simulate_step_response_response():
G = State(4)
yout, tout = simulate_step_response(G)
assert_allclose(yout, 4 * ones(len(yout))[:, None])
G = State(4, dt=0.01)
y2, t2 = simulate_step_response(G)
assert_allclose(y2, 4 * ones(len(y2))[:, None])
def test_State_slicing():
F = State(np.random.rand(4, 4))
H = State(F.d, np.random.rand(4, 3), np.random.rand(5, 4))
Hind = [(1, 3), (5, 1),
(5, 3), (1, 1),
(2, 3), (5, 2),
(2, 3), (5, 2),
(1, 2), (2, 1),
(3, 3), (5, 2)]
Find = [(1, 4), (4, 1),
(4, 4), (1, 1),
(2, 4), (4, 2),
(2, 4), (4, 2),
(1, 2), (2, 1),
(2, 4), (4, 2)]
for s, sind in ((H, Hind), (F, Find)):
for ind, x in enumerate([s[1, :], s[:, 1],
s[:, :], s[0, 0],
s[1:3, :], s[:, 1:3],
s[[1, 2], :], s[:, [1, 2]],
s[2, [1, 2]], s[[1, 2], 2],
s[::2, :], s[:, ::2]]):
assert_equal(x.shape, sind[ind])
assert_raises(ValueError, H.__setitem__)
def test_State_algebra_add_radd():
sta_siso = State(5)
sta_mimo = State(2.0*np.eye(3))
dyn_siso = State(haroldcompanion([1, 3, 3, 1]), e_i(3, -1), e_i(3, 1).T)
dyn_mimo = State(haroldcompanion([1, 3, 3, 1]), e_i(3, [1, 2]), np.eye(3))
dyn_mimo_sq = State(haroldcompanion([1, 3, 3, 1]), np.eye(3), np.eye(3))
G = dyn_mimo + sta_siso
assert_array_almost_equal(G.d, sta_siso.to_array()*np.ones(dyn_mimo.shape))
assert_raises(ValueError, dyn_mimo.__add__, sta_mimo)
G = dyn_mimo_sq + sta_mimo
assert_array_almost_equal(G.d, 2.*np.eye(3))
G = dyn_mimo + dyn_siso
J = np.array([[0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 1., 0.],
[-1., -3., -3., 0., 0., 0., 0., 1.],
[0., 0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., -1., -3., -3., 1., 1.],
[1., 0., 0., 0., 1., 0., 0., 0.],
[0., 1., 0., 0., 1., 0., 0., 0.],
[0., 0., 1., 0., 1., 0., 0., 0.]])
assert_array_almost_equal(concatenate_state_matrices(G), J)
assert_raises(ValueError, dyn_mimo.__add__, dyn_mimo_sq)
assert_raises(ValueError, dyn_mimo.__sub__, dyn_mimo_sq)
assert_raises(ValueError, dyn_mimo.__radd__, dyn_mimo_sq)
assert_raises(ValueError, dyn_mimo.__rsub__, dyn_mimo_sq)
def test_State_algebra_mul_rmul_mimo_siso():
sta_siso = State(5)
sta_mimo = State(2.0 * np.eye(3))
dyn_siso = State(haroldcompanion([1, 3, 3, 1]), e_i(3, -1), e_i(3, 1).T)
dyn_mimo = State(haroldcompanion([1, 3, 3, 1]), e_i(3, [1, 2]), np.eye(3))
dyn_mimo_sq = State(haroldcompanion([1, 3, 3, 1]), np.eye(3), np.eye(3))
G = dyn_siso * dyn_mimo
J = np.array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.],
[-1., -3., -3., 0., 0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[0., 0., 0., -1., -3., -3., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., -1., -3., -3., 0., 1.],
[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])
assert_array_almost_equal(concatenate_state_matrices(G), J)
G = dyn_mimo * dyn_siso
assert_array_almost_equal(concatenate_state_matrices(G), J)
G = dyn_mimo * sta_siso
assert_array_almost_equal(G.b, 5 * dyn_mimo.b)
assert_array_almost_equal(G.d, 5 * dyn_mimo.d)
assert_raises(ValueError, sta_mimo.__add__, dyn_mimo)
F = sta_mimo @ dyn_mimo
J = np.array([[0., 1., 0., 0., 0.], [0., 0., 1., 1., 0.],
[-1., -3., -3., 0., 1.], [2., 0., 0., 0., 0.],
[0., 2., 0., 0., 0.], [0., 0., 2., 0., 0.]])
assert_array_almost_equal(concatenate_state_matrices(F), J)
assert_almost_equal((dyn_mimo_sq + sta_mimo).d, 2 * np.eye(3))
def test_State_algebra_mul_rmul_dt():
G = State(1, 2, 3, 4, dt=0.1)
F = State(4, 3, 2, 1)
with assert_raises(ValueError):
F * G
with assert_raises(ValueError):
G * F
def test_State_to_array():
G = State(1, 1, 1)
H = State(5)
with assert_raises(TypeError):
G.to_array()
assert_equal(H.to_array(), np.array([[5]]))
H = State(np.ones((4, 4)))
assert_equal(H.to_array(), np.ones((4, 4)))
def test_simulate_step_response():
G = State(4)
yout, tout = simulate_step_response(G)
assert_allclose(yout, 4 * ones(len(yout))[:, None])
G = State(4, dt=0.01)
y2, t2 = simulate_step_response(G)
assert_allclose(y2, 4 * ones(len(y2))[:, None])
# Custom time input
G = Transfer(1, [1, 1, 1])
t = [0, 1, 2, 3]
yout, tout = simulate_step_response(G, t)
assert_allclose(tout, t)
def test_simple_dlqr():
# Example taken from M. de Oliveira's MAE280B lecture notes
H = State([[0, 0, 1, 0],
[0, 0, 0, 1],
[4.03428022844288e-06, 0, 0, 0.0515652322798669],
[0, 0, -0.000104315254033883, 0]],
[[0, 0], [1e-5/3, 0], [0, 0], [0, 0.01]],
eye(4), dt=0.1)
k, _, _ = lqr(H[:, 1], eye(4))
H.a = H.a.T
f, _, _ = lqr(H[:, 0], block_diag(0, 0, 1e-5, 1e-5), 0.1)
assert_almost_equal(k, array([[0, 0, -2.08727337333631e-06, 0]]))
assert_almost_equal(f, array([[1.71884123e-11, 0, 0, -1.79301359e-15]]))
def test_simple_dlqr():
# Example taken from M. de Oliveira's MAE280B lecture notes
H = State([[0, 0, 1, 0], [0, 0, 0, 1],
[4.03428022844288e-06, 0, 0, 0.0515652322798669],
[0, 0, -0.000104315254033883, 0]],
[[0, 0], [1e-5 / 3, 0], [0, 0], [0, 0.01]],
eye(4),
dt=0.1)
k, _, _ = lqr(H[:, 1], eye(4))
H.a = H.a.T
f, _, _ = lqr(H[:, 0], block_diag(0, 0, 1e-5, 1e-5), 0.1)
assert_almost_equal(k, array([[0, 0, -2.08727337333631e-06, 0]]))
assert_almost_equal(f, array([[1.71884123e-11, 0, 0, -1.79301359e-15]]))
def test_simple_lqr():
# Example taken from M. de Oliveira's MAE280B lecture notes
H = State([[0, 0, 1, 0],
[0, 0, 0, 1],
[4.03428022844288e-06, 0, 0, 0.0515652322798669],
[0, 0, -0.000104315254033883, 0]],
[[0, 0], [1e-5/3, 0], [0, 0], [0, 0.01]],
eye(4))
k, _, _ = lqr(H[:, 1], eye(4))
H.a = H.a.T
f, _, _ = lqr(H[:, 0], block_diag(0, 0, 1e-5, 1e-5), 0.1)
assert_almost_equal(k, array([[1.00554916, -1, 52.52180106, 18.51107167]]))
assert_almost_equal(f, array([[-577.370350, 173.600463,
0.383744946, 0.050228534]]), decimal=5)
def test_simulate_linear_system_check_x0():
t = [0, 0.01, 0.02]
u = [1, 2, 3]
G = State(5)
# No IC for static models
assert_raises(ValueError, simulate_linear_system, G, u, t, x0=1)
# No IC for Transfer
G = Transfer(1, [1, 2])
assert_raises(ValueError, simulate_linear_system, G, u, t, x0=1)
# x0 is not 1D
G = State(eye(3), ones([3, 1]), ones([1, 3]), dt=0.01)
assert_raises(ValueError, simulate_linear_system, G, u, t, x0=ones([5, 5]))
# x0 isnot compatible with number of states
assert_raises(ValueError, simulate_linear_system, G, u, t, x0=arange(4))
def test_simple_lqr():
# Example taken from M. de Oliveira's MAE280B lecture notes
H = State([[0, 0, 1, 0], [0, 0, 0, 1],
[4.03428022844288e-06, 0, 0, 0.0515652322798669],
[0, 0, -0.000104315254033883, 0]],
[[0, 0], [1e-5 / 3, 0], [0, 0], [0, 0.01]], eye(4))
k, _, _ = lqr(H[:, 1], eye(4))
H.a = H.a.T
f, _, _ = lqr(H[:, 0], block_diag(0, 0, 1e-5, 1e-5), 0.1)
assert_almost_equal(k, array([[1.00554916, -1, 52.52180106, 18.51107167]]))
assert_almost_equal(
f,
array([[-577.370350, 173.600463, 0.383744946, 0.050228534]]),
decimal=5)
def test_simulate_impulse_response():
G = State(4)
yout, tout = simulate_impulse_response(G)
# Check if the DC_gain * dt * u[0] == 1
dt = tout[1] - tout[0]
assert yout[0] * dt == approx(4)
assert_allclose(yout[1:], 0)
G = State(4, dt=0.01)
y2, t2 = simulate_impulse_response(G)
assert y2[0] == approx(400)
# Custom time input
G = Transfer(1, [1, 1, 1])
t = [0, 1, 2, 3]
yout, tout = simulate_step_response(G, t)
assert_allclose(tout, t)
def test_Transfer_algebra_mul_rmul_siso_mimo():
F = Transfer(2, [1, 1])
H = Transfer(np.arange(1, 5).reshape(2, 2), [1, 2])
K = Transfer([1, 3], [1, 0, 1])
FK_num, FK_den = (F * K).polynomials
assert_equal(FK_num, np.array([[2, 6]]))
assert_equal(FK_den, np.array([[1, 1, 1, 1]]))
for J in (F * H, H * F):
for x in range(2):
for y in range(2):
assert_equal(J.num[x][y], np.array([[(1 + y + 2 * x) * 2]]))
assert_equal(J.den[x][y], np.array([[1, 3, 2]]))
H = Transfer([1, 2], [1, 2, 3]) * np.arange(1, 5).reshape(2, 2)
HH = H * H
for x in range(4):
assert_equal(sum(HH.den, [])[x], np.array([[1., 4., 10., 12., 9.]]))
assert_equal(sum(HH.num, [])[x], (x + 1)**2 * np.array([[1., 4., 4.]]))
F = Transfer(1, [1, 1])
H = State(1, 2, 3, 4, 0.1)
with assert_raises(ValueError):
F * H
with assert_raises(ValueError):
F * 'string'
def test_simple_lqry():
# Scalar matrices
H = State(1, 1, 1, 1)
k, x, e = lqr(H, Q=3, weight_on='output')
assert_almost_equal(array([k[0, 0], x[0, 0], e[0]]), [1.5, 3, -0.5 + 0j])
# Wrong S shape
assert_raises(ValueError, lqr, H, Q=3, S=eye(2), weight_on='output')
def test_State_matmul_rmatmul_ndarray():
H = State([[-5, -2], [1, 0]], [[2], [0]], [3, 1], 1)
J1 = np.array([[-5., -2., 0., 0., 0., 0., 2., 4., 6., 8.],
[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., -5., -2., 0., 0., 10., 12., 14., 16.],
[0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., -5., -2., 18., 20., 22., 24.],
[0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[3., 1., 0., 0., 0., 0., 1., 2., 3., 4.],
[0., 0., 3., 1., 0., 0., 5., 6., 7., 8.],
[0., 0., 0., 0., 3., 1., 9., 10., 11., 12.]])
J2 = np.array([[-5., -2., 0., 0., 0., 0., 2., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., -5., -2., 0., 0., 0., 2., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., -5., -2., 0., 0., 2.],
[0., 0., 0., 0., 1., 0., 0., 0., 0.],
[3., 1., 6., 2., 9., 3., 1., 2., 3.],
[12., 4., 15., 5., 18., 6., 4., 5., 6.],
[21., 7., 24., 8., 27., 9., 7., 8., 9.],
[30., 10., 33., 11., 36., 12., 10., 11., 12.]])
mat = np.arange(1, 13).reshape(3, 4)
Fm = concatenate_state_matrices(mat @ H)
assert_array_almost_equal(J1, Fm)
Fm = concatenate_state_matrices(H @ mat)
assert_array_almost_equal(J1, Fm)
mat = np.arange(1, 13).reshape(4, 3)
Fm = concatenate_state_matrices(mat @ H)
assert_array_almost_equal(J2, Fm)
Fm = concatenate_state_matrices(H @ mat)
assert_array_almost_equal(J2, Fm)
def test_verbosity_State(capsys):
_ = State.validate_arguments(-2 * np.eye(2),
np.eye(2), [[1, -3], [0, 0]],
[[0, 1], [-1, 0]],
verbose=True)
out, err = capsys.readouterr()
assert not err.strip()
assert out == ("========================================\n"
"Handling A\n"
"========================================\n"
"Trying to np.array A\n"
"========================================\n"
"Handling B\n"
"========================================\n"
"Trying to np.array B\n"
"========================================\n"
"Handling C\n"
"========================================\n"
"Trying to np.array C\n"
"========================================\n"
"Handling D\n"
"========================================\n"
"Trying to np.array D\n"
"All seems OK. Moving to shape mismatch check\n")
def test_feedback_static_static():
G = State(5)
H = Transfer(4)
assert_almost_equal(feedback(G, G).d, array([[10.208333333333334]]))
assert_almost_equal(feedback(G, H).d, array([[10.263157894736842]]))
assert_almost_equal(feedback(H, G).d, array([[8.210526315789473]]))
assert_almost_equal(feedback(H, H).num, array([[8.266666666666666]]))
def test_impulse_response_plot_shape():
seed(1234)
assert_equal(len(impulse_response_plot(Transfer(5, dt=0.5)).axes), 1)
a, b, c = -3*eye(5) + rand(5, 5), rand(5, 3), rand(4, 5)
G = State(a, b, c)
f = impulse_response_plot(G)
prop = f.axes[0].get_subplotspec().get_topmost_subplotspec().get_gridspec()
assert_equal(prop.get_geometry(), G.shape)
def test_step_response_plot_shape():
seed(1234)
f = step_response_plot(Transfer(5, dt=0.5))
assert f.shape == (1, 1)
a, b, c = -3*eye(5) + rand(5, 5), rand(5, 3), rand(4, 5)
G = State(a, b, c)
f = step_response_plot(G)
assert f.shape == G.shape
def test_static_model_conversion_sampling_period():
G = State(np.eye(5), dt=0.001)
H = state_to_transfer(G)
assert_(H._isgain) # 0
assert_(not H._isSISO) # 1
assert_equal(H.SamplingPeriod, 0.001) # 2
K = transfer_to_state(H)
assert_equal(K.SamplingPeriod, 0.001) # 3
def test_State_algebra_add_radd():
sta_siso = State(5)
sta_mimo = State(2.0 * np.eye(3))
dyn_siso = State(haroldcompanion([1, 3, 3, 1]), e_i(3, -1), e_i(3, 1).T)
dyn_mimo = State(haroldcompanion([1, 3, 3, 1]), e_i(3, [1, 2]), np.eye(3))
dyn_mimo_sq = State(haroldcompanion([1, 3, 3, 1]), np.eye(3), np.eye(3))
G = dyn_mimo + sta_siso
assert_array_almost_equal(G.d,
sta_siso.to_array() * np.ones(dyn_mimo.shape))
assert_raises(ValueError, dyn_mimo.__add__, sta_mimo)
G = dyn_mimo_sq + sta_mimo
assert_array_almost_equal(G.d, 2. * np.eye(3))
G = dyn_mimo + dyn_siso
J = np.array([[0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 1., 0.],
[-1., -3., -3., 0., 0., 0., 0., 1.],
[0., 0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., -1., -3., -3., 1., 1.],
[1., 0., 0., 0., 1., 0., 0., 0.],
[0., 1., 0., 0., 1., 0., 0., 0.],
[0., 0., 1., 0., 1., 0., 0., 0.]])
assert_array_almost_equal(concatenate_state_matrices(G), J)
assert_raises(ValueError, dyn_mimo.__add__, dyn_mimo_sq)
assert_raises(ValueError, dyn_mimo.__sub__, dyn_mimo_sq)
assert_raises(ValueError, dyn_mimo.__radd__, dyn_mimo_sq)
assert_raises(ValueError, dyn_mimo.__rsub__, dyn_mimo_sq)
def test_simple_kalman_decomp():
assert_raises(ValueError, kalman_decomposition, 1)
G = State([[2, 1, 1], [5, 3, 6], [-5, -1, -4]], [[1], [0], [0]], [1, 0, 0])
F = kalman_decomposition(G)
J = np.array([[2, 0., -1.41421356, -1.], [7.07106781, -3, -7., 0.],
[0, 0, 2., 0.], [-1, 0, 0., 0.]])
assert_array_almost_equal(F.a, J[:3, :3])
assert_array_almost_equal(F.b, J[:3, [-1]])
assert_array_almost_equal(F.c, J[[-1], :3])
def test_State_Instantiations():
assert_raises(TypeError, State)
G = State(5)
assert_(G.a.size == 0)
assert_(G._isSISO)
assert_(G._isgain)
assert_equal(G.d, np.array([[5.]]))
G = State(np.eye(2))
assert_equal(G.shape, (2, 2))
assert_(G._isgain)
assert_(not G._isSISO)
# Wrong sized A, B, C, D
assert_raises(ValueError, State, np.ones((3, 2)), [[1], [2]], [1, 2])
assert_raises(ValueError, State, np.eye(2), [[1], [2], [3]], [1, 2])
assert_raises(ValueError, State, np.eye(2), [[1], [2]], [1, 2, 3])
assert_raises(ValueError, State, np.eye(2), [[1], [2]], [1, 2], [0, 0])
def test_frequency_response():
seed(1234)
# SISO, only integrators
G = Transfer([1, 0], [1, 0, 0, 0])
_, _ = frequency_response(G)
G = Transfer([1, -1], [1, -2, 1], dt=0.1)
_, _ = frequency_response(G)
# Static Gains
G = Transfer(5, dt=0.5)
_, _ = frequency_response(G)
G = Transfer(eye(5))
_, _ = frequency_response(G)
# MIMO
a, b, c = -3 * eye(5) + rand(5, 5), rand(5, 3), rand(4, 5)
G = State(a, b, c)
_, _ = frequency_response(G)
G = State(a, b, c, dt=1)
_, _ = frequency_response(G)
def test_minimal_realization_State():
M = array([[-6.5, 0.5, 6.5, -6.5, 0., 1., 0.],
[-0.5, -5.5, -5.5, 5.5, 2., 1., 2.],
[-0.5, 0.5, 0.5, -6.5, 3., 4., 3.],
[-0.5, 0.5, -5.5, -0.5, 3., 2., 3.],
[1., 1., 0., 0., 0., 0., 0.]])
G = State(*matrix_slice(M, (1, 4), corner='sw'))
H = minimal_realization(G)
assert_(H.a.shape, (2, 2))
def test_undiscretize_backeuler_nearzero_eig():
G = Transfer(1, [1, 0, 0], 0.1)
assert_raises(ValueError, undiscretize, G, method='<<')
arr = np.arange(9).reshape(3, 3).astype(float)
arr[0, 0] = 1e-15
G = State(arr, np.random.rand(3), np.random.rand(3), dt=0.1)
with catch_warnings():
simplefilter("error")
assert_raises(_rcond_warn, undiscretize, G, '<<')
def test_undiscretize():
ac = eye(2)
bc = 0.5 * ones((2, 1))
cc = np.array([[0.75, 1.0], [1.0, 1.0], [1.0, 0.25]])
dc = np.array([[0.0], [0.0], [-0.33]])
G = State(ac, bc, cc, dc)
for method in ('zoh', 'foh', 'tustin', '>>', '<<'):
print(method)
Gd = discretize(G, dt=0.5, method=method)
Gu = undiscretize(Gd)
assert_array_almost_equal(Gu.a, ac)
assert_array_almost_equal(Gu.b, bc)
assert_array_almost_equal(Gu.c, cc)
assert_array_almost_equal(Gu.d, dc)
Gd = State(zeros([2, 2]), ones([2, 1]), ones([1, 2]), dt=1)
Gd.DiscretizedWith = '<<'
assert_raises(ValueError, undiscretize, Gd)
def test_State_algebra_mul_rmul_scalar_array():
G = State(np.diag([-1, -2]), [[1, 2], [3, 4]], np.eye(2))
F = G * np.eye(2)
Fm = G @ np.eye(2)
assert_equal(concatenate_state_matrices(F), concatenate_state_matrices(Fm))
F = np.eye(2) * G
Fm = np.eye(2) @ G
assert_equal(concatenate_state_matrices(F), concatenate_state_matrices(Fm))
H = 1 / 2 * G
assert_equal(H.c, 0.5 * G.c)
def test_State_to_array():
G = State(1, 1, 1)
H = State(5)
with assert_raises(ValueError):
G.to_array()
assert_equal(H.to_array(), np.array([[5]]))
H = State(np.ones((4, 4)))
assert_equal(H.to_array(), np.ones((4, 4)))
def test_State_algebra_truediv_rtruediv():
G = State(1, 2, 3, 4)
F = G/0.5
assert_equal(F.b, np.array([[4.]]))
assert_equal(F.d, np.array([[8.]]))
G.d = 0.
with assert_raises(LinAlgError):
G/G
with assert_raises(ValueError):
G/3j
G.d = 4
# nonminimal but acceptable
H = G / G
ha, hb, hc, hd = H.matrices
assert_array_almost_equal(ha, [[1, -1.5], [0, -0.5]])
assert_array_almost_equal(hb, [[0.5], [0.5]])
assert_array_almost_equal(hc, [[3, -3]])
assert_array_almost_equal(hd, [[1]])
G = State(np.eye(3)*0.5)
assert_array_almost_equal((1 / G).to_array(), np.eye(3)*2)
def test_verbosity_State(capsys):
_ = State.validate_arguments(-2*np.eye(2), np.eye(2),
[[1, -3], [0, 0]], [[0, 1], [-1, 0]],
verbose=True)
out, err = capsys.readouterr()
assert not err.strip()
assert out == ("========================================\n"
"Handling A\n"
"========================================\n"
"Trying to np.array A\n"
"========================================\n"
"Handling B\n"
"========================================\n"
"Trying to np.array B\n"
"========================================\n"
"Handling C\n"
"========================================\n"
"Trying to np.array C\n"
"========================================\n"
"Handling D\n"
"========================================\n"
"Trying to np.array D\n"
"All seems OK. Moving to shape mismatch check\n")
