def __init__(self, k=50.0, m=1.0, g=9.8, **kw):
self.k = k
self.m = m
self.g = g
self.dim = 2
self.z = map(S, ['z0', 'z1'])
self.p = map(S, ['p0', 'p1'])
self.q = map(S, ['q0', 'q1'])
self.x = map(S, ['x0', 'x1', 'x2', 'x3'])
self.u = map(S, ['u0', 'u1'])
self.Mq = np.eye(self.dim) * self.m
self.Mqi = np.eye(self.dim) / self.m
self.Vq = -self.m * self.g * self.q[1]
# create Ohm and Psi
self._Ohm = np.array([self.z[0], self.z[1] + sym.sin(self.z[0])])
self._Psi = np.array([self.q[0], self.q[1] - sym.sin(self.q[0])])
#self.controller = lambda t, x: [0, 0]
super(SinFloor2D, self).__init__(si=1)
def __init__(self, k=50.0, m=1.0, g=9.8, **kw):
self.k = k
self.m = m
self.g = g
self.dim = 2
self.z = map(S, ['z0', 'z1'])
self.p = map(S, ['p0', 'p1'])
self.q = map(S, ['q0', 'q1'])
self.x = map(S, ['x0', 'x1', 'x2', 'x3'])
self.u = map(S, ['u0', 'u1'])
self.Mq = np.eye(self.dim) * self.m
self.Mqi = np.eye(self.dim) / self.m
self.Vq = -self.m * self.g * self.q[1]
# create Ohm and Psi
self._Ohm = np.array([self.z[0], self.z[1] + sym.sin(self.z[0])])
self._Psi = np.array([self.q[0], self.q[1] - sym.sin(self.q[0])])
#self.controller = lambda t, x: [0, 0]
super(SinFloor2D, self).__init__(si=1)
def __init__(self, k=50.0, m=1.0, g=9.8, **kw):
self.k = k
self.dim = 2
self.z = map(S, ['z0', 'z1'])
self.p = map(S, ['p0', 'p1'])
self.q = map(S, ['q0', 'q1'])
self.x = map(S, ['x0', 'x1', 'x2', 'x3'])
self.u = map(S, ['u0', 'u1'])
self.Mq = np.eye(self.dim) * m
self.Mqi = np.eye(self.dim) / m
self.Vq = -m * g * self.q[1]
# create Ohm and Psi
self._Ohm = self.z
self._Psi = self.q
super(FlatFloor2D, self).__init__(si=1)
def __init__(self, k=50.0, m=1.0, g=9.8, **kw):
self.k = k
self.dim = 2
self.z = map(S, ['z0', 'z1'])
self.p = map(S, ['p0', 'p1'])
self.q = map(S, ['q0', 'q1'])
self.x = map(S, ['x0', 'x1', 'x2', 'x3'])
self.u = map(S, ['u0', 'u1'])
self.Mq = np.eye(self.dim) * m
self.Mqi = np.eye(self.dim) / m
self.Vq = -m * g * self.q[1]
# create Ohm and Psi
self._Ohm = self.z
self._Psi = self.q
super(FlatFloor2D, self).__init__(si=1)
def _delf(self, t, xval, uval):
# calculates the jump term assuming the field switches
# between fplus and fminus at (t, x)
params = np.concatenate(([t], xval, uval))
fp = self._fplus.func(*params)
fm = self._fmins.func(*params)
dphi = self.dphi(xval)
# this assumes x = [z, zdot]
M = tn.eval(self.Mz, self.z, xval[:self.dim])
M = scipy.linalg.block_diag(M, np.eye(self.dim))
#dphi = matmult(M, dphi)
out = -np.outer(fp-fm, dphi)/np.abs(np.inner(fp, dphi))
#Tracer()()
#out = np.zeros((2*self.dim, 2*self.dim))
#for i in range(self.dim):
# out[self.si, i] = -M[self.si, i]
return out
