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Gross and net flux on the Drunkard’s walk

This example shows how to compute and visualize gross and net reactive flux (see deeptime.markov.ReactiveFlux) using the deeptime.data.drunkards_walk().

Gross flux, Net flux
 9 import matplotlib as mpl
10 import matplotlib.pyplot as plt
11 from mpl_toolkits.axes_grid1 import make_axes_locatable
12
13 import numpy as np
14
15 from deeptime.data import drunkards_walk
16
17 sim = drunkards_walk(grid_size=(10, 10),
18                      bar_location=[(0, 0), (0, 1), (1, 0), (1, 1)],
19                      home_location=[(8, 8), (8, 9), (9, 8), (9, 9)])
20 sim.add_barrier((5, 1), (5, 5))
21 sim.add_barrier((0, 9), (5, 8))
22 sim.add_barrier((9, 2), (7, 6))
23 sim.add_barrier((2, 6), (5, 6))
24
25 sim.add_barrier((7, 9), (7, 7), weight=5.)
26 sim.add_barrier((8, 7), (9, 7), weight=5.)
27
28 sim.add_barrier((0, 2), (2, 2), weight=5.)
29 sim.add_barrier((2, 0), (2, 1), weight=5.)
30
31 flux = sim.msm.reactive_flux(sim.home_state, sim.bar_state)
32
33 fig, axes = plt.subplots(1, 2, figsize=(18, 10))
34 dividers = [make_axes_locatable(axes[i]) for i in range(len(axes))]
35 caxes = [divider.append_axes("right", size="5%", pad=0.05) for divider in dividers]
36
37 titles = ["Gross flux", "Net flux"]
38 fluxes = [flux.gross_flux, flux.net_flux]
39
40 cmap = plt.cm.copper_r
41 thresh = [0, 1e-12]
42
43 for i in range(len(axes)):
44     ax = axes[i]
45     F = fluxes[i]
46     ax.set_title(titles[i])
47
48     vmin = np.min(F[np.nonzero(F)])
49     vmax = np.max(F)
50
51     sim.plot_2d_map(ax)
52     sim.plot_network(ax, F, cmap=cmap, connection_threshold=thresh[i])
53     norm = mpl.colors.LogNorm(vmin=vmin, vmax=vmax)
54     fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=caxes[i])

Total running time of the script: (0 minutes 0.753 seconds)

Estimated memory usage: 590 MB