def calculate_trajectory_iterator(
self,
start_lambda=0.0,
stop_on_singularity=True,
OdeMethodKwargs={"stepsize": 1e-3},
return_cartesian=False,
):
"""
Calculate trajectory in manifold according to geodesic equation.
Yields an iterator.
Parameters
----------
start_lambda : float
Starting lambda, defaults to 0.0, (lambda ~= t)
stop_on_singularity : bool
Whether to stop further computation on reaching singularity, defaults to True
OdeMethodKwargs : dict
Kwargs to be supplied to the ODESolver, defaults to {'stepsize': 1e-3}
Dictionary with key 'stepsize' along with an float value is expected.
return_cartesian : bool
True if coordinates and velocities are required in cartesian coordinates(SI units), defaults to Falsed
Yields
------
float
proper time
~numpy.ndarray
array of [t, x1, x2, x3, velocity_of_time, v1, v2, v3] for each proper time(lambda).
"""
ODE = RK45(
fun=self.f_vec,
t0=start_lambda,
y0=self.initial_vec,
t_bound=1e300,
**OdeMethodKwargs
)
crossed_event_horizon = False
_event_hor = (
kerrnewman_utils.event_horizon(self.M.value, self.a.value, self.Q.value)[0]
* 1.001
)
while True:
if not return_cartesian:
yield ODE.t, ODE.y
else:
v = ODE.y
si_vals = BoyerLindquistConversion(
v[1], v[2], v[3], v[5], v[6], v[7], self.a.value
).convert_cartesian()
yield ODE.t, np.hstack((v[0], si_vals[:3], v[4], si_vals[3:]))
ODE.step()
if (not crossed_event_horizon) and (ODE.y[1] <= _event_hor):
warnings.warn("particle reached event horizon. ", RuntimeWarning)
if stop_on_singularity:
break
else:
crossed_event_horizon = True
def strip_velocities(coords):
if isinstance(coords, BoyerLindquistConversion):
vals = coords.values()
return BoyerLindquistConversion(*vals[:3], a=vals[-1])
elif isinstance(coords, (CartesianConversion, SphericalConversion)):
vals = coords.values()
return type(coords)(*vals[:3])
return None
def bl_coords():
return BoyerLindquistConversion(t=0.,
r=10.,
theta=np.pi / 2,
phi=np.pi / 4,
v_r=10.,
v_th=-20.,
v_p=20.)
def calculate_trajectory(
self,
start_lambda=0.0,
end_lambda=10.0,
stop_on_singularity=True,
OdeMethodKwargs={"stepsize": 1e-3},
return_cartesian=False,
):
"""
Calculate trajectory in manifold according to geodesic equation
Parameters
----------
start_lambda : float
Starting lambda(proper time), defaults to 0, (lambda ~= t)
end_lamdba : float
Lambda(proper time) where iteartions will stop, defaults to 100000
stop_on_singularity : bool
Whether to stop further computation on reaching singularity, defaults to True
OdeMethodKwargs : dict
Kwargs to be supplied to the ODESolver, defaults to {'stepsize': 1e-3}
Dictionary with key 'stepsize' along with an float value is expected.
return_cartesian : bool
True if coordinates and velocities are required in cartesian coordinates(SI units), defaults to False
Returns
-------
~numpy.ndarray
N-element array containing proper time.
~numpy.ndarray
(n,8) shape array of [t, x1, x2, x3, velocity_of_time, v1, v2, v3] for each proper time(lambda).
"""
vecs = list()
lambdas = list()
crossed_event_horizon = False
ODE = RK45(fun=self.f_vec,
t0=start_lambda,
y0=self.initial_vec,
t_bound=end_lambda,
**OdeMethodKwargs)
_event_hor = kerr_utils.event_horizon(self.M.value,
self.a.value)[0] * 1.001
while ODE.t < end_lambda:
vecs.append(ODE.y)
lambdas.append(ODE.t)
ODE.step()
if (not crossed_event_horizon) and (ODE.y[1] <= _event_hor):
warnings.warn("particle reached event horizon. ",
RuntimeWarning)
if stop_on_singularity:
break
else:
crossed_event_horizon = True
vecs, lambdas = np.array(vecs), np.array(lambdas)
if not return_cartesian:
return lambdas, vecs
else:
cart_vecs = list()
for v in vecs:
si_vals = BoyerLindquistConversion(
v[1], v[2], v[3], v[5], v[6], v[7],
self.a.value).convert_cartesian()
cart_vecs.append(
np.hstack((v[0], si_vals[:3], v[4], si_vals[3:])))
return lambdas, np.array(cart_vecs)
def bl_coords():
return BoyerLindquistConversion(
10.0, 1.5707963267948966, 0.7853981633974483, 10.0, -20.0, 20.0, 0.0
)
warnings.warn("particle reached Schwarzchild Radius. ", RuntimeWarning)
>>>>>>> 0e311bec1be2508a28ebd8a3f8b7b944db997269
if stop_on_singularity:
break
else:
crossed_event_horizon = True
vecs, lambdas = np.array(vecs), np.array(lambdas)
if not return_cartesian:
return lambdas, vecs
else:
cart_vecs = list()
for v in vecs:
si_vals = BoyerLindquistConversion(
v[1], v[2], v[3], v[5], v[6], v[7], self.a.value
).convert_cartesian()
cart_vecs.append(np.hstack((v[0], si_vals[:3], v[4], si_vals[3:])))
return lambdas, np.array(cart_vecs)
def calculate_trajectory_iterator(
self,
start_lambda=0.0,
stop_on_singularity=True,
OdeMethodKwargs={"stepsize": 1e-3},
return_cartesian=False,
):
"""
Calculate trajectory in manifold according to geodesic equation.
Yields an iterator.
def strip_velocities(coords):
if isinstance(coords, BoyerLindquistConversion):
vals = coords.values()
return BoyerLindquistConversion(*vals[:3], a=vals[-1])
vals = coords.values()
return type(coords)(*vals[:3])