def test_compute_bond(trajectory):
# 1. Compute the bond distance between atoms 0 and 1 for the trajectory.
# Test if the first frame's bond length is correct.
ai.compute_bond(trajectory, "R01", 0, 1)
assert trajectory.frames[0].properties["R01"] == pt.approx(
1.282298373, rel=1e-10) # 0767554
def test_major_trajectory_operations(tmp_path, trajectory):
# 1. Plot Bond Distances (Spaghetti + Blur)
# Compute the a bond distance property for all Frames in traj
ai.compute_bond(trajectory, "R01", 0, 1)
ai.plot_scalar(
tmp_path / "R.pdf",
trajectory,
"R01",
ylabel=r"$R_{CC} [\AA{}]$",
time_units="fs",
state_colors=["r", "b"],
plot_average=True,
)
# Blur the bond distance (convolve)
R = np.linspace(0.5, 3.0, 50)
ai.blur_property(trajectory, "R01", "Rblur", R, alpha=8.0)
# Plot the heat map of blurred bond distance
ai.plot_vector(
tmp_path / "Rblur.pdf",
trajectory,
"Rblur",
y=R,
ylabel=r"$R [\AA{}]$",
time_units="fs",
nlevel=64,
)
# 2. Plot of Torsion Angle (Spaghetti + Blur)
# Compute the a torsion angle property for all Frames in traj
ai.compute_torsion(trajectory, "T0123", 0, 1, 2, 3)
ai.unwrap_property(trajectory, "T0123", 360.0)
ai.plot_scalar(
tmp_path / "T.pdf",
trajectory,
"T0123",
ylabel=r"$\Theta [^{\circ{}}]$",
time_units="fs",
state_colors=["r", "b"],
plot_average=True,
)
# Blur the torsion
T = np.linspace(-180.0, +180.0, 100)
ai.blur_property(trajectory, "T0123", "Tblur", T, alpha=0.02)
# Plot the heat map of blurred torison
ai.plot_vector(
tmp_path / "Tblur.pdf",
trajectory,
"Tblur",
y=T,
ylabel=r"$Theta [^{\circ{}}]$",
time_units="fs",
nlevel=64,
)
# 3. UED Cross Section
# Compute the "simple" form of the UED cross section in R
R = np.linspace(1.0, 6.0, 50)
ai.compute_ued_simple(trajectory, "UED", R=R, alpha=8.0)
# Plot the heat map of the UED cross section detailed above
ai.plot_vector(
tmp_path / "UED.pdf",
trajectory,
"UED",
y=R,
ylabel=r"$R [\AA{}]$",
time_units="fs",
diff=True,
)
x) for x in [1, 2, 3]
]
# Merge the trajectories into one super-big Bundle with uniform weights
traj = ai.Bundle.merge(trajs,
ws=[1.0 / len(trajs)] * len(trajs),
labels=[1, 2, 3])
print((traj.labels))
# Compute properties at ~1 fs intervals, removing nonsense due to adaptive timesteps
ts = np.arange(0.0, max(traj.ts), 40.0) # TODO: Cleaner edges
traj = traj.interpolate_nearest(ts)
# => Showoff Plot of Bond Distances (Spaghetti + Blur) <= #
# Compute the a bond distance property for all Frames in traj
# This particular bond is the one that leads to a three-ring complex
ai.compute_bond(traj, "R01", 7, 8)
# Blur the bond distance
R = np.linspace(1.0, 6.0, 50)
ai.blur_property(traj, "R01", "Rblur", R, alpha=8.0)
# Plot the heat map of blurred bond distance
# ai.plot_vector('R2.pdf', traj, 'Rblur', y=R, ylabel=r'$R [\AA{}]$', time_units='fs', twosided=False, cmap=plt.get_cmap('viridis'))
ai.plot_vector("R.pdf",
traj,
"Rblur",
y=R,
ylabel=r"$R [\AA{}]$",
time_units="fs",
nlevel=64)
ai.plot_scalar(
