def update_state_vector(self, state, dt):
"""Find the next state vector.
This implements the f(x) prediction function for the Kalman filter.
The state vector is of the form (x, y, z, px, py, pz).
Parameters
----------
state : array-like
The current state
dt : float
The time step to the next state
Returns
-------
ndarray
The next state
"""
pos0 = state[0:3]
mom0 = state[3:6]
self.particle.position = pos0
self.particle.momentum = mom0
# tstep = pos_step / (self.particle.beta * c_lgt)
new_state = sim.find_next_state(self.particle, self.gas, self.efield, self.bfield, dt)
self.particle.state_vector = new_state
return np.hstack([self.particle.position, self.particle.momentum])
def test_zero_energy(self):
"""If the energy is 0, the particle should stay still"""
self.p.energy = 0
old_sv = self.p.state_vector
new_sv = sim.find_next_state(*self.args)
self.assertTrue(numpy.array_equal(old_sv, new_sv),
msg='state vector changed')
