Note

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PCCA+ on the Drunkard’s walk

This example shows a decomposition into metastable sets (see deeptime.markov.pcca()) of states in the deeptime.data.drunkards_walk() example. The state assignments are shown via their probability distributions over the micro states.

Memberships for metastable set 1, Memberships for metastable set 2, Memberships for metastable set 3, Memberships for metastable set 4, Memberships for metastable set 5, Memberships for metastable set 6
 9 import matplotlib as mpl
10 import matplotlib.pyplot as plt
11
12 from deeptime.data import drunkards_walk
13
14 sim = drunkards_walk(grid_size=(10, 10),
15                      bar_location=[(0, 0), (0, 1), (1, 0), (1, 1)],
16                      home_location=[(8, 8), (8, 9), (9, 8), (9, 9)])
17 sim.add_barrier((5, 1), (5, 5))
18 sim.add_barrier((0, 9), (5, 8))
19 sim.add_barrier((9, 2), (7, 6))
20 sim.add_barrier((2, 6), (5, 6))
21
22 sim.add_barrier((7, 9), (7, 7), weight=5.)
23 sim.add_barrier((8, 7), (9, 7), weight=5.)
24
25 sim.add_barrier((0, 2), (2, 2), weight=5.)
26 sim.add_barrier((2, 0), (2, 1), weight=5.)
27
28 pcca = sim.msm.pcca(6)
29
30 fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)
31
32 for i, ax in enumerate(axes.flatten()):
33     ax.set_title(f"Memberships for metastable set {i + 1}")
34     handles, labels = sim.plot_2d_map(ax, barrier_mode='hollow')
35
36     Q = pcca.memberships[:, i].reshape(sim.grid_size)
37     cb = ax.imshow(Q, interpolation='nearest', origin='lower', cmap=plt.cm.Blues)
38 norm = mpl.colors.Normalize(vmin=0, vmax=1)
39 fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=plt.cm.Blues), ax=axes, shrink=.8)

Total running time of the script: ( 0 minutes 2.057 seconds)

Estimated memory usage: 8 MB