VAMP on Position Based Fluids

Projection of position based fluids simulation timeseries on dominant component. For more details, see the VAMP tutorial.

import matplotlib.pyplot as plt
import numpy as np

from deeptime.data import position_based_fluids
from deeptime.decomposition import VAMP

pbf_simulator = position_based_fluids(n_burn_in=500, n_jobs=8)
trajectory = pbf_simulator.simulate_oscillatory_force(n_oscillations=3, n_steps=400)
n_grid_x = 20
n_grid_y = 10
kde_trajectory = pbf_simulator.transform_to_density(
    trajectory, n_grid_x=n_grid_x, n_grid_y=n_grid_y, n_jobs=8
)
tau = 100
model = VAMP(lagtime=100).fit(kde_trajectory).fetch_model()
projection_left = model.forward(kde_trajectory, propagate=False)
projection_right = model.backward(kde_trajectory, propagate=False)

f, ax = plt.subplots(1, 1, figsize=(5, 5))
start = 400
stop = len(kde_trajectory) - tau  # 5000
left = projection_left[:-tau][start:stop, 0]
right = projection_right[tau:][start:stop, 0]
lw = 4
ax.plot(np.arange(start, stop), left, label="left", linewidth=lw)
ax.plot(np.arange(start, stop)[::50], right[::50], '--', label="right", linewidth=3, markersize=12)
ax.vlines([start + i * 400 for i in range(1, (stop - start) // 400)], np.min(left), np.max(left),
          linestyles='dotted')
ax.legend()

Estimated memory usage: 45 MB