def test_t33(const):
def odefun(y, _, k):
return y[1], (y[0] * y[3] - y[2] * y[1]) / k[0], y[3], y[4], y[5], (
-y[2] * y[5] - y[0] * y[1]) / k[0]
def odejac(y, _, k):
df_dy = np.array(
[[0, 1, 0, 0, 0, 0],
[y[3] / k[0], -y[2] / k[0], -y[1] / k[0], y[0] / k[0], 0, 0],
[0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1],
[-y[1] / k[0], -y[0] / k[0], -y[5] / k[0], 0, 0, -y[2] / k[0]]])
df_dp = np.empty((6, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] + 1, y0[2], y0[3], yf[0] - 1, yf[2], yf[3]
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
sol = Trajectory()
sol.t = np.linspace(0, 1, 2)
sol.y = np.array([[-1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0]])
sol.k = np.array([const])
sol = algo.solve(sol)['traj']
assert sol.converged
def test_t31(const):
def odefun(y, _, k):
return np.sin(y[1]), y[2], -y[3] / k[0], \
((y[0]-1) * np.cos(y[1]) - y[2] / np.cos(y[1]) - k[0] * y[3] * np.tan(y[1])) / k[0]
def odejac(y, _, k):
df_dy = np.array([[0, np.cos(y[1]), 0, 0], [0, 0, 1, 0],
[0, 0, 0, -1 / k[0]],
[
np.cos(y[1]) / k[0],
-(np.sin(y[1]) * (y[0] - 1) + k[0] * y[3] *
(np.tan(y[1])**2 + 1) +
(y[2] * np.sin(y[1])) / np.cos(y[1])**2) /
k[0], -1 / (k[0] * np.cos(y[1])), -np.tan(y[1])
]])
df_dp = np.empty((4, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], y0[2], yf[0], yf[2]
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
sol = Trajectory()
sol.t = np.linspace(0, 1, 2)
sol.y = np.array([[0, 0, 0, 0], [0, 0, 0, 0]])
sol.k = np.array([const])
sol = algo.solve(sol)['traj']
assert sol.converged
def test_t21(const):
def odefun(y, _, k):
return y[1], (y[0] *
(1 + y[0]) - np.exp(-2 * y[2] / np.sqrt(k[0]))) / k[0], 1
def odejac(y, _, k):
df_dy = np.array([[0, 1, 0],
[(2 * y[0] + 1) / k[0], 0,
(2 * np.exp(-(2 * y[2]) / np.sqrt(k[0]))) /
k[0]**(3 / 2)], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, k):
return y0[0] - 1, yf[0] - np.exp(-1 / np.sqrt(k[0])), y0[2]
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 0, 0], [0, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = np.exp(-sol.y[:, 2] / np.sqrt(const))
e2 = -np.exp(-sol.y[:, 2] / np.sqrt(const)) / np.sqrt(const)
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t13(const):
def odefun(y, _, k):
return 2 * y[1], 2 * ((y[0] - k[0] * np.pi**2 * np.cos(np.pi * y[2]) -
np.cos(np.pi * y[2])) / k[0]), 2
def odejac(y, _, k):
df_dy = np.array([[0, 2, 0],
[
2 / k[0], 0,
(2 * (np.pi * np.sin(np.pi * y[2]) +
k[0] * np.pi**3 * np.sin(np.pi * y[2]))) /
k[0]
], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], yf[0] + 1, y0[2] + 1
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[-1, 0, -1], [0, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = np.cos(np.pi * sol.y[:, 2]) + np.exp(
-(1 + sol.y[:, 2]) / np.sqrt(sol.k[0]))
e2 = -np.exp(-(sol.y[:, 2] + 1) / np.sqrt(sol.k[0])) / np.sqrt(
sol.k[0]) - np.pi * np.sin(np.pi * sol.y[:, 2])
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t2(const):
def odefun(y, _, k):
return y[1], y[1] / k[0]
def odejac(_, __, k):
df_dy = np.array([[0, 1], [0, 1 / k[0]]])
df_dp = np.empty((2, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] - 1, yf[0]
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 1], [0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = (1.e0 - np.exp(
(sol.t - 1.e0) / sol.k)) / (1.e0 - np.exp(-1.e0 / sol.k))
e2 = np.exp((sol.t - 1) / sol.k) / (sol.k * (1 / np.exp(1 / sol.k) - 1))
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t4(const):
def odefun(y, _, k):
return 2 * y[1], 2 * (((1 + k[0]) * y[0] - y[1]) / k[0]), 2
def odejac(_, __, k):
df_dy = np.array([[0, 2,
0], [2 * (1 + k[0]) / k[0], 2 * (-1) / k[0], 0],
[0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, k):
return y0[0] - 1 - np.exp(-2), yf[0] - 1 - np.exp(
-2 * (1 + k[0]) / k[0]), y0[2] + 1
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[-1, 0, -1], [-1, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = np.exp(sol.y[:, 2] - 1) + np.exp(-((1 + sol.k[0]) *
(1 + sol.y[:, 2]) / sol.k[0]))
e2 = np.exp(sol.y[:, 2] - 1) - (sol.k[0] + 1) / (sol.k[0] * np.exp(
(sol.y[:, 2] + 1) * (sol.k[0] + 1) / sol.k[0]))
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t10(const):
def odefun(y, _, k):
return 2 * y[1], 2 * (-y[2] * y[1] / k[0]), 2
def odejac(y, _, k):
df_dy = np.array([[0, 2,
0], [0, 2 * (-y[2]) / k[0], 2 * (-y[1] / k[0])],
[0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], yf[0] - 2, y0[2] + 1
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 0, -1], [2, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = 1 + erf(sol.y[:, 2] / np.sqrt(2 * sol.k[0])) / erf(
1 / np.sqrt(2 * sol.k[0]))
e2 = np.sqrt(2) / (np.sqrt(np.pi) * np.sqrt(sol.k[0]) * np.exp(
sol.y[:, 2]**2 /
(2 * sol.k[0])) * erf(np.sqrt(2) / (2 * np.sqrt(sol.k[0]))))
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t8(const):
def odefun(y, _, k):
return y[1], (-y[1] / k[0]), 1
def odejac(_, __, k):
df_dy = np.array([[0, 1, 0], [0, -1 / k[0], 0], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] - 1, yf[0] - 2, y0[2]
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[1, 0, -1], [2, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = (2 - np.exp(-1 / sol.k[0]) -
np.exp(-sol.y[:, 2] / sol.k[0])) / (1 - np.exp(-1 / sol.k[0]))
e2 = -1 / (sol.k[0] * np.exp(sol.y[:, 2] / sol.k[0]) *
(1 / np.exp(1 / sol.k[0]) - 1))
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t9(const):
def odefun(y, _, k):
return 2 * y[1], 2 * (-(4 * y[2] * y[1] + 2 * y[0]) /
(k[0] + y[2]**2)), 2
def odejac(y, _, k):
df_dy = np.array(
[[0, 2, 0],
[
-4 / (y[2]**2 + k[0]), -(8 * y[2]) / (y[2]**2 + k[0]),
(4 * y[2] * (2 * y[0] + 4 * y[1] * y[2])) /
(y[2]**2 + k[0])**2 - (8 * y[1]) / (y[2]**2 + k[0])
], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, k):
return y0[0] - 1 / (1 + k[0]), yf[0] - 1 / (1 + k[0]), y0[2] + 1
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[1 / (1 + const), 0, -1], [1 / (1 + const), 1, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = 1 / (sol.k[0] + sol.y[:, 2]**2)
e2 = -(2 * sol.y[:, 2]) / (sol.y[:, 2]**2 + sol.k[0])**2
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t17(const):
def odefun(y, _, k):
return 0.2 * y[1], 0.2 * (-3 * k[0] * y[0] / (k[0] + y[2]**2)**2), 0.2
def odejac(y, _, k):
df_dy = np.array([[0, 0.2, 0],
[
-(3 * k[0]) / (5 * (y[2]**2 + k[0])**2), 0,
(12 * k[0] * y[0] * y[2]) / (5 *
(y[2]**2 + k[0])**3)
], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, k):
return y0[0] + 0.1 / np.sqrt(k[0] + 0.01), yf[0] - 0.1 / np.sqrt(
k[0] + 0.01), y0[2] + 0.1
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 0, 0], [0, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = sol.y[:, 2] / np.sqrt(sol.k[0] + sol.y[:, 2]**2)
e2 = 1 / np.sqrt(sol.y[:, 2]**2 + sol.k[0]) - sol.y[:, 2]**2 / (
sol.y[:, 2]**2 + sol.k[0])**(3 / 2)
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t3(algorithm, const):
def odefun(y, _, k):
return (2 * y[1], 2 * (-(2 + np.cos(np.pi * y[2])) * y[1] + y[0] - (1 + k[0] * np.pi * np.pi) * np.cos(
np.pi * y[2]) - (2 + np.cos(np.pi * y[2])) * np.pi * np.sin(np.pi * y[2])) / k[0], 2)
def odejac(y, _, k):
df_dy = np.array([[0, 2, 0],
[2 / k[0], -(2 * np.cos(np.pi * y[2]) + 4)/k[0],
(2*np.pi**2 * np.sin(np.pi * y[2])**2 + 2 * np.pi*np.sin(np.pi*y[2])*(k[0]*np.pi**2 + 1)
- 2*np.pi**2*np.cos(np.pi*y[2])*(np.cos(np.pi*y[2]) + 2)
+ 2*y[1]*np.pi*np.sin(np.pi*y[2]))/k[0]],
[0, 0, 0]], dtype=np.float)
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] + 1, yf[0] + 1, y0[2] + 1
algo = Shooting(odefun, None, bcfun, algorithm=algorithm)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[-1, 0, -1], [-1, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = np.cos(np.pi * sol.y[:, 2])
e2 = -np.pi * np.sin(np.pi * sol.y[:, 2])
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t16(const):
def odefun(y, _, k):
return 1 * y[1], 1 * (-y[0] * np.pi**2 / (4 * k[0])), 1
def odejac(_, __, k):
df_dy = np.array([[0, 1, 0], [-np.pi**2 / (4 * k[0]), 0, 0], [0, 0,
0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, k):
return y0[0], yf[0] - np.sin(np.pi / (2 * np.sqrt(k[0]))), y0[2]
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 0, 0], [0, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = np.sin(np.pi * sol.y[:, 2] / (2 * np.sqrt(sol.k[0])))
e2 = (np.pi * np.cos((np.pi * sol.y[:, 2]) /
(2 * np.sqrt(sol.k[0])))) / (2 * np.sqrt(sol.k[0]))
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def map(self,
sol: Solution,
control_costate: Union[float, np.ndarray] = 0.) -> Solution:
idx_u_list = []
for idx_u, (idx_y,
u) in enumerate(sorted(zip(self.control_idxs, sol.u.T))):
sol.y = np.insert(sol.y, idx_y, u, axis=1)
if isinstance(control_costate, Iterable):
if not isinstance(control_costate, np.ndarray):
control_costate = np.array(control_costate)
costate_insert = control_costate[idx_u] * np.ones_like(sol.t)
else:
costate_insert = control_costate * np.ones_like(sol.t)
sol.lam = np.insert(sol.lam, -1, costate_insert, axis=1)
if len(sol.lam_u) == 0:
sol.lam_u = np.array([costate_insert])
else:
sol.lam_u = np.insert(sol.lam_u, -1, costate_insert, axis=1)
idx_u_list.append(idx_u)
sol.u = np.delete(sol.u, idx_u_list, axis=1)
return sol
def test_t1(algorithm, const):
def odefun(y, _, k):
return y[1], y[0] / k[0]
def odejac(_, __, k):
df_dy = np.array([[0, 1], [1 / k[0], 0]])
df_dp = np.empty((2, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] - 1, yf[0]
algo = Shooting(odefun, None, bcfun, algorithm=algorithm)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 1], [0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = (np.exp(-sol.t / np.sqrt(sol.k)) - np.exp((sol.t - 2) / np.sqrt(sol.k))) / (
1 - np.exp(-2.e0 / np.sqrt(sol.k)))
e2 = (1. / (sol.k ** (1 / 2) * np.exp(sol.t / sol.k ** (1 / 2))) + np.exp(
(sol.t - 2) / sol.k ** (1 / 2)) / sol.k ** (1 / 2)) / (1 / np.exp(2 / sol.k ** (1 / 2)) - 1)
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t9(algorithm, const):
def odefun(y, _, k):
return 2 * y[1], 2 * (-(4 * y[2] * y[1] + 2 * y[0]) / (k[0] + y[2] ** 2)), 2
def odejac(y, _, k):
df_dy = np.array([[0, 2, 0],
[-4 / (y[2] ** 2 + k[0]), -(8 * y[2]) / (y[2] ** 2 + k[0]),
(4 * y[2] * (2 * y[0] + 4 * y[1] * y[2])) / (y[2] ** 2 + k[0]) ** 2
- (8 * y[1]) / (y[2] ** 2 + k[0])], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, k):
return y0[0] - 1 / (1 + k[0]), yf[0] - 1 / (1 + k[0]), y0[2] + 1
algo = Shooting(odefun, None, bcfun, algorithm=algorithm, num_arcs=2)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0., 1., 2)
# noinspection PyTypeChecker
solinit.y = np.array([[1. / (1. + const), 0., -1.], [1. / (1. + const), 1., 1.]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = 1 / (sol.k[0] + sol.y[:, 2] ** 2)
e2 = -(2 * sol.y[:, 2]) / (sol.y[:, 2] ** 2 + sol.k[0]) ** 2
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def test_t7(algorithm, const):
def odefun(y, _, k):
return 2 * y[1], 2 * ((-y[2] * y[1] + y[0] - (1.0e0 + k[0] * np.pi ** 2) * np.cos(np.pi * y[2]) - np.pi *
y[2] * np.sin(np.pi * y[2])) / k[0]), 2
def odejac(y, _, k):
df_dy = np.array([[0, 2, 0],
[2 / k[0], -2 * y[2] / k[0],
-(2 * (y[1] + np.pi * np.sin(np.pi * y[2]) + np.pi ** 2 * y[2] * np.cos(np.pi * y[2])
- np.pi * np.sin(np.pi * y[2]) * (k[0] * np.pi ** 2 + 1))) / k[0]],
[0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] + 1, yf[0] - 1, y0[2] + 1
algo = Shooting(odefun, None, bcfun, algorithm=algorithm)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[-1, 0, -1], [1, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
e1 = np.cos(np.pi * sol.y[:, 2]) + sol.y[:, 2] + (
sol.y[:, 2] * erf(sol.y[:, 2] / np.sqrt(2.0e0 * sol.k[0]))
+ np.sqrt(2 * sol.k[0] / np.pi) * np.exp(-sol.y[:, 2] ** 2 / (2 * sol.k[0]))) / (
erf(1.0e0 / np.sqrt(2 * sol.k[0])) + np.sqrt(2.0e0 * sol.k[0] / np.pi)
* np.exp(-1 / (2 * sol.k[0])))
e2 = erf((np.sqrt(2) * sol.y[:, 2]) / (2 * np.sqrt(sol.k[0]))) / (
erf(np.sqrt(2) / (2 * np.sqrt(sol.k[0]))) + (np.sqrt(2) * np.sqrt(sol.k[0])) / (
np.sqrt(np.pi) * np.exp(1 / (2 * sol.k[0])))) - np.pi * np.sin(np.pi * sol.y[:, 2]) + 1
assert all(e1 - sol.y[:, 0] < tol)
assert all(e2 - sol.y[:, 1] < tol)
def map(self, sol: Solution) -> Solution:
idx_u_list = []
for idx_u, (idx_y,
u) in enumerate(sorted(zip(self.control_idxs, sol.u.T))):
sol.y = np.insert(sol.y, idx_y, u, axis=1)
idx_u_list.append(idx_u)
sol.u = np.delete(sol.u, idx_u_list, axis=1)
return sol
def inv_map(self, sol: Solution) -> Solution:
sol = copy.deepcopy(sol)
if self.delta_ind_idx is None:
self.delta_ind_idx = np.shape(sol.p)[0] - 1
sol.t = sol.t * sol.p[self.delta_ind_idx]
sol.p = np.delete(sol.p, self.delta_ind_idx)
return sol
def inv_map(self, sol: Solution, retain_dual=True) -> Solution:
if not retain_dual:
sol.lam = empty_array
sol.nu = empty_array
sol.cost = self.compute_cost(sol.t, sol.y, sol.q, sol.u, sol.p, sol.k)
return sol
def inv_map(self, sol: Solution) -> Solution:
if self.ind_state_idx is None:
self.ind_state_idx = np.shape(sol.y)[1] - 1
sol.t = sol.y[:, self.ind_state_idx]
sol.y = np.delete(sol.y, self.ind_state_idx, axis=1)
sol.lam_t = sol.lam[:, self.ind_state_idx]
sol.lam = np.delete(sol.lam, self.ind_state_idx, axis=1)
return sol
def map(self, sol: Solution) -> Solution:
if self.ind_state_idx is None:
self.ind_state_idx = np.shape(sol.y)[1]
if len(sol.lam_t) == 0:
sol.lam_t = np.zeros_like(sol.t)
sol.y = np.insert(sol.y, self.ind_state_idx, sol.t, axis=1)
sol.lam = np.insert(sol.lam, self.ind_state_idx, sol.lam_t, axis=1)
return sol
def map(self, sol: Solution) -> Solution:
sol = copy.deepcopy(sol)
if self.delta_ind_idx is None:
self.delta_ind_idx = np.shape(sol.p)[0]
delta_t = sol.t[-1] - sol.t[0]
sol.p = np.insert(sol.p, self.delta_ind_idx, delta_t)
sol.t = (sol.t - sol.t[0]) / delta_t
return sol
def map(self, sol: Solution, lam=empty_array, nu=empty_array) -> Solution:
# sol.lam = lam
if len(sol.nu) == 0:
sol.nu = np.ones(self.nu_len)
return sol
def test_t22(const):
def odefun(y, _, k):
return y[1], -(y[1] + y[0] * y[0]) / k[0]
def odejac(y, _, k):
df_dy = np.array([[0, 1], [-(2 * y[0]) / k[0], -1 / k[0]]])
df_dp = np.empty((2, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], yf[0] - 1 / 2
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 0], [0, 0]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
assert sol.converged
def test_t19(algorithm, const):
def odefun(y, _, k):
return y[1], (-y[1] / k[0]), 1
def odejac(_, __, k):
df_dy = np.array([[0, 1, 0], [0, -1 / k[0], 0], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], yf[0], y0[2]
algo = Shooting(odefun, None, bcfun, algorithm=algorithm)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 0, 0], [0, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
assert sol.converged
def test_t23(algorithm, const):
def odefun(y, _, k):
return y[1], 1 / k[0] * np.sinh(y[0] / k[0])
def odejac(y, _, k):
df_dy = np.array([[0, 1], [np.cosh(y[0] / k[0]) / k[0] ** 2, 0]])
df_dp = np.empty((2, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], yf[0] - 1
algo = Shooting(odefun, None, bcfun, algorithm=algorithm)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0, 0], [1, 0]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
assert sol.converged
def test_t29(algorithm, const):
def odefun(y, _, k):
return y[1], (y[0] - y[0]*y[1]) / k[0]
def odejac(y, _, k):
df_dy = np.array([[0, 1], [(1-y[1]) / k[0], -y[0] / k[0]]])
df_dp = np.empty((2, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], yf[0] - 3/2
algo = Shooting(odefun, None, bcfun, algorithm=algorithm, num_arcs=1)
algo.set_derivative_jacobian(odejac)
sol = Trajectory()
sol.t = np.linspace(0, 1, 2)
sol.y = np.array([[0, 0], [3/2, 0]])
sol.k = np.array([const])
sol = algo.solve(sol)['traj']
assert sol.converged
def test_t15(const):
def odefun(y, _, k):
return 2 * y[1], 2 * (y[2] * y[0] / k[0]), 2
def odejac(y, _, k):
df_dy = np.array([[0, 2, 0], [2 * (y[2] / k[0]), 0, 2 * (y[0] / k[0])],
[0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] - 1, yf[0] - 1, y0[2] + 1
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[1, 0, -1], [0, 0, 1]])
solinit.k = np.array([const])
sol = algo.solve(solinit)['traj']
assert sol.converged
def test_t30(const):
def odefun(y, _, k):
return y[1], (y[0] - y[0] * y[1]) / k[0]
def odejac(y, _, k):
df_dy = np.array([[0, 1], [(1 - y[1]) / k[0], -y[0] / k[0]]])
df_dp = np.empty((2, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0] + 7 / 6, yf[0] - 3 / 2
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
sol = Trajectory()
sol.t = np.linspace(0, 1, 2)
sol.y = np.array([[-7 / 6, 0], [3 / 2, 0]])
sol.k = np.array([const])
cc = np.linspace(const * 10, const, 10)
for c in cc:
sol = copy.deepcopy(sol)
sol.k = np.array([c])
sol = algo.solve(sol)['traj']
assert sol.converged
def test_t19(const):
def odefun(y, _, k):
return y[1], (-y[1] / k[0]), 1
def odejac(_, __, k):
df_dy = np.array([[0, 1, 0], [0, -1 / k[0], 0], [0, 0, 0]])
df_dp = np.empty((3, 0))
return df_dy, df_dp
def bcfun(y0, yf, _, __, ___):
return y0[0], yf[0], y0[2]
algo = SPBVP(odefun, None, bcfun)
algo.set_derivative_jacobian(odejac)
sol = Trajectory()
sol.t = np.linspace(0, 1, 2)
sol.y = np.array([[0, 0, 0], [0, 0, 1]])
sol.k = np.array([const])
cc = np.linspace(const * 100, const, 10)
for c in cc:
sol = copy.deepcopy(sol)
sol.k = np.array([c])
sol = algo.solve(sol)['traj']
assert sol.converged
