def run_param_sweep_2d():
dx = 0.2
L = 5.0
dim = 2
t_max = 40.0
dt = 0.1
D_rhos = np.logspace(-1, 2, 10)
mus = np.logspace(-1, 2, 10)
for D_rho, mu in product(D_rhos, mus):
if D_rho != mu:
m = ModelAbstract(dim, dt, dx, L, D_rho, mu, walls=None)
while m.t < t_max:
m.iterate()
rho_final = m.get_rho()
print(D_rho, mu, rho_final.max(), np.var(rho_final))
def run_model_abstract_2d():
dx = 0.2
L = 5.0
dim = 2
t_max = 40.0
dt = 0.1
D_rho = 22.
mu = 44.
# walls = bannock.walls.Maze(L, dim=2, dx=dx, d=2 * dx, seed=None)
# walls = bannock.walls.Traps(L, dx=dx, n=1, d=dx, w=10*dx, s=2*dx)
walls = None
m = ModelAbstract(dim, dt, dx, L, D_rho, mu, walls)
fig = plt.figure()
ax_rho = fig.add_subplot(2, 1, 1)
ax_c = fig.add_subplot(2, 1, 2)
plot_rho = ax_rho.imshow([[0]], cmap='Reds', interpolation='nearest',
origin='lower', extent=2 * [-L / 2.0, L / 2.0])
plot_c = ax_c.imshow([[0]], cmap='Reds', interpolation='nearest',
origin='lower', extent=2 * [-L / 2.0, L / 2.0])
ax_cb = plt.axes([0.875, 0.2, 0.05, 0.7])
fig.colorbar(plot_rho, cax=ax_cb)
plt.ion()
plt.show()
every = 1
while m.t < t_max:
if not m.i % every:
plot_rho.set_data(m.get_rho())
plot_c.set_data(m.get_c())
plot_rho.autoscale()
plot_c.autoscale()
fig.canvas.draw_idle()
print(m.t, np.var(m.get_rho()))
m.iterate()
def run_model_abstract_1d():
dx = 0.4
L = 5.0
dim = 1
t_max = 160.0
dt = 2.0
D_rho = 0.01
mu = 0.215
m = ModelAbstract(dim, dt, dx, L, D_rho, mu, walls=None)
print(m.get_x().shape, m.get_c().shape)
raw_input()
fig = plt.figure()
ax_rho = fig.add_subplot(2, 1, 1)
ax_c = fig.add_subplot(2, 1, 2)
plot_rho = ax_rho.plot(m.get_x(), m.get_rho(), c='red')[0]
plot_c = ax_c.plot(m.get_x(), m.get_c(), c='green')[0]
plt.ion()
plt.show()
every = 1
while m.t < t_max:
if not m.i % every:
plot_rho.set_ydata(m.get_rho())
plot_c.set_ydata(m.get_c())
rho = m.get_rho()
rho_ran = rho.max() - rho.min()
rho_max = rho.max() + 0.1 * rho_ran
rho_min = rho.min() - 0.1 * rho_ran
ax_rho.set_ylim(rho_min, rho_max)
c = m.get_c()
c_ran = c.max() - c.min()
c_max = c.max() + 0.1 * c_ran
c_min = c.min() - 0.1 * c_ran
ax_c.set_ylim(c_min, c_max)
fig.canvas.draw()
print(m.t, np.var(rho))
m.iterate()