def __init__(self, *args, **kwargs):
super(cMyTest, self).__init__(*args, **kwargs)
self.N_ = N = 20 # np.random.randint(2, 10)
self.dim_ = dim = 8 # np.random.randint(1, 8)
self.wp_ = wp = np.random.rand(N + 1, dim)
self.basis_ = basis = cBasis0010()
self.cost_ = cJerkL2Norm(wp, basis)
def test_l2_norms(self):
''' Test L2 norms '''
tau = np.random.rand() * 5.0
Bimpl = cBasis0010()
Qd3 = np.zeros((6, 6))
Qd1 = np.zeros((6, 6))
Bimpl.l2_norm(tau, Qd3, 3)
Bimpl.l2_norm(tau, Qd1, 1)
y = np.random.rand(Bimpl.dim_)
def qd3norm2(s):
res = Bimpl.evalDerivOnWindow(s, tau, 3).dot(y)
return np.power(res, 2.0) * tau / 2.0
def qd1norm2(s):
res = Bimpl.evalDerivOnWindow(s, tau, 1).dot(y)
return np.power(res, 2.0) * tau / 2.0
dt = 5.0e-6
for f, Q in [(qd1norm2, Qd1), (qd3norm2, Qd3)]:
fv = np.array([f(t) for t in np.arange(-1, 1, dt)])
Itest = np.sum(fv[1:] + fv[:-1]) * dt / 2.0
Inom = Q.dot(y).dot(y)
err = np.abs(Itest - Inom)
assert err < 5.0e-2, '''
Error in integral = {:.4f}
Nominal integral = {:.4f}
Test integral = {:.4f}
'''.format(err, Inom, Itest)
def test_value(self):
''' Compare the value of the symbolic and the implemented Legendre
Polynomials'''
for item in range(0, 100):
Bimpl = cBasis0010()
tau_ = 10.0 * np.random.rand()
s_ = np.random.rand() * 2.0 - 1.0
B = [Bi.subs({self.tausym_: tau_}) for Bi in self.Bsym_]
B = [sp.lambdify(self.ssym_, Bi) for Bi in B]
B = np.array([Bi(s_) for Bi in B])
e = np.max(np.abs(B - Bimpl.evalOnWindow(s_, tau_)))
# print('error = {:.3e}\r'.format(e), end='')
assert (e < 1.0e-10)
def test_derivative_y_2(self):
''' Second test for the derivative of y wr.t. tau.
First test the identity A*dydtau + dAdtau y = 0A
Second test the identity of above using basis
'''
for i in range(40):
np.random.seed()
dim = np.random.randint(1, 3)
N = np.random.randint(2, 6)
a = np.random.rand()
wp = (np.random.rand(N + 1, dim) - 0.5) * 2 * np.pi
tauv = 0.5 + np.random.rand(N) * 2.0
splcalc = cSplineCalc(dim, N, cBasis0010())
basis = splcalc.basis_
dydtauNom, y = splcalc.eval_dydtau(tauv, wp)
A = splcalc.eval_A(tauv).todense()
y = y.copy()
# A*dydtau + dAdtau y = 0
for i, _ in enumerate(tauv):
v0 = A.dot(dydtauNom[:, i])
v1 = splcalc.eval_dAdtiy(tauv, i, y).todense()
res = v0 + v1.T
e = np.abs(res)
assert np.max(e) < 1.0e-10, '''
e = {:14.7e}
'''.format(e)
for i, taui in enumerate(tauv):
B = basis.evalDerivOnWindow(-1, taui, 0)
dB_dtau = basis.evalDerivWrtTauOnWindow(-1, taui, 0)
for idim in range(dim):
i0 = i * 6 * dim + 6 * idim
i1 = i0 + 6
e = dydtauNom[i0:i1, i].dot(B) + y[i0:i1].dot(dB_dtau)
assert np.abs(e) < 1.0e-10, '''
error computing dydtau^\\top B + y^\\top dB_dtau
e = {:14.7e}
index of tau = {:d}
index of q = {:d}
i0 = {:d}
i1 = {:d}
'''.format(e, i, idim, i0, i1)
def test_derivatives_wrt_t(self):
''' Compare the derivative w.r.t. t of the symbolic and the implemented
Legendre Polynomials'''
for item in range(0, 100):
Bimpl = cBasis0010()
tau_ = 10.0 * np.random.rand()
s_ = np.random.rand() * 2.0 - 1.0
ddeg = np.random.randint(1, 6)
B = [
Bi.diff(self.ssym_, ddeg) * sp.Pow(2 / self.tausym_, ddeg)
for Bi in self.Bsym_
]
B = [Bi.subs({self.tausym_: tau_}) for Bi in B]
B = [sp.lambdify(self.ssym_, Bi) for Bi in B]
B = np.array([Bi(s_) for Bi in B])
e = np.max(np.abs(B - Bimpl.evalDerivOnWindow(s_, tau_, ddeg)))
assert (
e < 5.0e-3
), 'Large error on derivatives wrt t, error = {:+.3e}'.format(e)
def test_values(self):
server = xmlrpc.client.ServerProxy(self.url_)
dim = 6 # np.random.randint(2, 6)
N = 5 # np.random.randint(3, 120)
T = 10.0
box = np.pi
unique_id = 'hola'
q0 = rand_interpolate(N, dim, cBasis0010(), T, box)
tspan = np.arange(0, T, 0.01)
q0t = q0(tspan)
q0dt = q0.deriv()(tspan)
server.gspline_load(unique_id, piecewise2json(q0))
q1t = np.array([
server.gspline_desired_position(unique_id, float(t)) for t in tspan
])
q1dt = np.array([
server.gspline_desired_velocity(unique_id, float(t)) for t in tspan
])
assert np.linalg.norm(q0t - q1t, ord=np.inf) < 1.0e-10
assert np.linalg.norm(q0dt - q1dt, ord=np.inf) < 1.0e-10
def test_container(self):
dim = 6 # np.random.randint(2, 6)
N = 5 # np.random.randint(3, 120)
T = 10.0
box = np.pi
q0 = rand_interpolate(N, dim, cBasis0010(), T, box)
server = xmlrpc.client.ServerProxy(self.url_)
server.gspline_load('unique_id', piecewise2json(q0))
res = server.gspline_get('u*')
q1 = [json2piecewise(el) for el in res][0]
timespan = np.arange(0, T, 0.01)
assert np.linalg.norm(q0(timespan) - q1(timespan),
ord=np.inf) < 1.0e-10
server.gspline_load('unique_id_2', piecewise2json(q0))
res = server.gspline_get('u*')
assert len(res) == 2