def subsample_trajectory(traj: Trajectory, step=1, step_in_seconds=False):
"""
Sample the trajectory ``traj`` by removing evenly spaced
points according to ``step``.
Parameters
----------
traj : Trajectory
Input trajectory.
step : int, optional
Number of sample points or period, depending on the value
of ``step_in_seconds``. By default 1.
step_in_seconds : bool, optional
If True, ``step`` is considered as the number of sample
points. Otherwise, ``step`` is interpreted as the sample
period, in seconds. By default False.
Returns
-------
Trajectory
Output trajectory.
"""
if step_in_seconds:
step = int(step / traj.dt)
points = traj.r[::step]
ang = traj.ang[::step] if traj.ang is not None else None
t = traj.t[::step] if traj.t is not None else None
return Trajectory(points=points, t=t, ang=ang, dt=step * traj.dt)
def test_load():
# Wrong trajectory file extension at loading
with pytest.raises(ValueError):
t1 = Trajectory.load('t1.abc')
# Loading json
t1 = Trajectory.load('t1.json')
for tp, point in zip(t1, [[1, 4], [2, 5], [3, 6]]):
assert (np.array(point) == tp.r).all()
# Loading csv
t1 = Trajectory.load('t1.csv')
for tp, point in zip(t1, [[1, 4], [2, 5], [3, 6]]):
assert (np.array(point) == tp.r).all()
os.remove('t1.json')
os.remove('t1.csv')
def test_save():
t1 = Trajectory(x=[1, 2, 3], y=[4, 5, 6])
# Wrong trajectory file extension at saving
with pytest.raises(ValueError):
t1.save('t1', file_type='abc')
# Saving json
t1.save('t1', file_type='json')
# Saving csv
t1.save('t1', file_type='csv')
def test_creation_with_time():
Trajectory(x=[1, 2], y=[2, 3], t=[0, 0.1])
Trajectory(x=[1, 2], y=[2, 3], dt=0.1)
Trajectory(x=[1, 2], y=[2, 3], t=[0, 0.1], dt=0.1)
Trajectory(x=[1, 2], y=[2, 3], t=[0.4, 0.5])
Trajectory(x=[1, 2], y=[2, 3], t=[0.4, 0.5], dt=0.1, t0=0.4)
with pytest.raises(ValueError):
Trajectory(x=[1, 2], y=[2, 3], t=[0])
with pytest.raises(ValueError):
Trajectory(x=[1, 2], y=[2, 3], t=[0, 0.1], dt=0.2)
with pytest.raises(ValueError):
Trajectory(x=[1, 2], y=[2, 3], t=[0.4, 0.5], dt=0.1)
def generate(self):
# Get position vectors
r = self._get_r()
# Generate RandomWalk object
trajs = []
for i in range(self.N):
points = r[:, :, i]
trajs.append(
Trajectory(points=points,
dt=self.dt,
t=self.t,
traj_id=f"Random Walker {i + 1}"))
return trajs
def test_creation_by_dimensions():
Trajectory(dimensions=[[1, 2]])
Trajectory(dimensions=[[1, 2], [2, 3]])
Trajectory(dimensions=[[1, 2], [2, 3], [1, 4]])
Trajectory(dimensions=[[1, 2], [2, 3], [1, 4], [7, 8]])
with pytest.raises(ValueError):
Trajectory(dimensions=[[1, 2], [2]])
with pytest.raises(ValueError):
Trajectory(dimensions=[[1, 2], [2], [2, 5]])
with pytest.raises(ValueError):
Trajectory(dimensions=[[1, 2], [2, 6], [5]])
with pytest.raises(ValueError):
Trajectory(dimensions=[[2], [2, 6], [5, 8]])
def test_creation_by_points():
Trajectory(points=[[1, 2]])
Trajectory(points=[[1, 2], [2, 3]])
Trajectory(points=[[1, 2, 4], [2, 3, 2], [1, 4, 8]])
Trajectory(points=[[1, 2, 7, 3], [2, 3, 5, 3]])
with pytest.raises(ValueError):
Trajectory(points=[[1, 2], [2]])
with pytest.raises(ValueError):
Trajectory(points=[[1, 2], [2], [2, 5]])
def generate(self):
self._simulate()
self.r *= self.r_scale
self.v *= self.v_scale
self.t *= self.t_scale
self.dt *= self.t_scale
trajs = []
for i in range(self.N):
points = self.r[:, :, i]
trajs.append(
Trajectory(points=points,
dt=self.dt,
t=self.t,
traj_id=f"LangevinSolution {i + 1}"))
return trajs
def test_creation_by_xyz():
Trajectory(x=[1, 2])
Trajectory(x=[1, 2], y=[2, 3])
Trajectory(x=[1, 2], y=[2, 3], z=[1, 4])
with pytest.raises(ValueError):
Trajectory(x=[1, 2], y=[2])
with pytest.raises(ValueError):
Trajectory(x=[1, 2], y=[2], z=[2, 5])
with pytest.raises(ValueError):
Trajectory(x=[1, 2], y=[2, 6], z=[5])
with pytest.raises(ValueError):
Trajectory(x=[2], y=[2, 6], z=[5, 8])
def traj(x):
return Trajectory(x=x)
def add_dynamic_reference(traj: Trajectory,
reference: Tuple[np.ndarray, np.ndarray, np.ndarray],
start_at_origin=True):
"""
This function fuses the information of a trajectory with an
external reference of the motion of the Frame of Reference
(FoR).
It allows to remap the information gathered in local SoRs
to a more general FoR.
Parameters
----------
traj : Trajectory
Input trajectory.
reference : Tuple[np.ndarray,np.ndarray,np.ndarray]
Angular and translational parameters of the form
``(ang:np.ndarray, tx:np.ndarray, ty:np.ndarray)`` that
accounts for the orientation and displacement of the reference.
start_at_origin : bool, optional
If True, set initial position at the origin. By default True.
Returns
-------
Trajectory
Output trajectory in the lab frame of reference.
"""
def affine2camera(theta, tx, ty):
x_cl, y_cl, theta_cl = np.zeros((3, theta.size + 1))
theta_cl[1:] = np.cumsum(theta)
for i in range(theta.size):
A = affine_matrix(theta_cl[i + 1], x_cl[i], y_cl[i], R_inv=True)
x_cl[i + 1], y_cl[i + 1] = A @ [-tx[i], -ty[i], 1]
x_cl, y_cl, theta_cl = x_cl[1:], y_cl[1:], theta_cl[1:]
return x_cl, y_cl, theta_cl
def camera2obj(x_ac, y_ac, x_cl, y_cl, theta_cl):
x_al, y_al = np.empty((2, x_ac.size))
for i in range(x_ac.size):
A = affine_matrix(theta_cl[i], x_cl[i], y_cl[i], R_inv=True)
x_al[i], y_al[i] = A @ [x_ac[i], y_ac[i], 1]
return x_al, y_al
def affine2obj(theta, tx, ty, x_ac, y_ac):
x_cl, y_cl, theta_cl = affine2camera(theta, tx, ty)
x_al, y_al = camera2obj(x_ac, y_ac, x_cl, y_cl, theta_cl)
return x_al, y_al
theta, tx, ty = reference
x_al, y_al = affine2obj(theta, tx, ty, traj.r.x, traj.r.y)
if start_at_origin:
x_al = x_al - x_al[0]
y_al = y_al - y_al[0]
traj.x = x_al
traj.y = y_al
return Trajectory(x=x_al, y=y_al, ang=traj.ang, t=traj.t, traj_id=traj.id)
def test_creation_general():
Trajectory(x=[1, 2], y=[2, 3], t=[0, 1], ang=[0, 0], traj_id='test')
Trajectory(x=[1, 2], y=[2, 3], dt=0.5, ang=[0, 1.2], traj_id='test')
Trajectory(points=[[1, 2], [2, 3]], dt=0.5, ang=[0, 1.2], traj_id='test')
Trajectory(dimensions=[[1, 2], [2, 3]], dt=0.5, t=[1, 1.5], t0=1,
traj_id='test')
def test_creation_with_ang():
Trajectory(x=[1, 2], y=[2, 3], ang=[0, 0.1])
with pytest.raises(ValueError):
Trajectory(x=[1, 2], y=[2, 3], ang=[0.1])
from yupi import Trajectory
from yupi.analyzing import plot_trajectory
# Creation by assigning the axis directly
# Data you got elsewhere
x = [0, 1.0, 0.63, -0.37, -1.24, -1.5, -1.08, -0.19, 0.82, 1.63, 1.99, 1.85]
y = [0, 0, 0.98, 1.24, 0.69, -0.3, -1.23, -1.72, -1.63, -1.01, -0.06, 0.94]
# Generate the trajectory object
track = Trajectory(x=x, y=y, traj_id="Spiral")
# Plot the results
plot_trajectory(track, title='Assigning the axis directly')
# Creation by assigning the dimensions data
# Data you got elsewhere
dims = [[
0, 1.0, 0.63, -0.37, -1.24, -1.5, -1.08, -0.19, 0.82, 1.63, 1.99, 1.85
], [0, 0, 0.98, 1.24, 0.69, -0.3, -1.23, -1.72, -1.63, -1.01, -0.06, 0.94]]
# Generate the trajectory object
track = Trajectory(dimensions=dims, traj_id="Spiral")
# Plot the results
plot_trajectory(track, title='Assigning the dimensions data')
# Creation by assigning the points data
# Data you got elsewhere