def match_model_to_agent(agent_dirname, dt, dx):
agent_fnames = bu.get_filenames(agent_dirname)
m_agent_0 = bu.filename_to_model(agent_fnames[0])
# x_agent = np.linspace(-m_agent_0.L / 2.0, m_agent_0.L / 2.0,
# m_agent_0.c.a.shape[0])
ts = np.array([bu.filename_to_model(f).t for f in agent_fnames])
rho_0 = m_agent_0.rho_0
origin_flag = m_agent_0.origin_flag
dx_agent = m_agent_0.dx
mu = 0.5 * m_agent_0.chi * m_agent_0.v_0
if m_agent_0.onesided_flag:
mu /= 2.0
rho_D = m_agent_0.v_0 ** 2 / m_agent_0.p_0
L = m_agent_0.L
c_D = m_agent_0.c_D
c_sink = m_agent_0.c_sink
c_sink = m_agent_0.c_sink
c_source = m_agent_0.c_source
m = ModelCoarse1D(dt,
rho_0, rho_D, origin_flag, dx_agent,
mu,
L, dx,
c_D, c_sink, c_source)
fig = plt.figure()
ax = fig.gca()
ax.set_xlim(-500.0, 500.0)
ax.set_ylim(0.0, 1000.0)
plot_rho = ax.plot(m.get_x(), m.get_rho(), c='red')[0]
plot_arho = ax.plot(m.get_x(), get_rho_agent(m_agent_0, m), c='green')[0]
plt.ion()
plt.show()
every = 10
while m.t < 20000.0:
if not m.i % every:
rho_coarse = m.get_rho()
plot_rho.set_ydata(rho_coarse)
i_agent_fname = find_nearest_index(m.t, ts)
m_agent = bu.filename_to_model(agent_fnames[i_agent_fname])
rho_agent = get_rho_agent(m_agent, m)
plot_arho.set_ydata(rho_agent)
ax.set_ylim(0.0, 1.1 * max(rho_coarse.max(), rho_agent.max()))
fig.canvas.draw()
print(m.t, np.mean(m.get_rho()), agent_fnames[i_agent_fname])
m.iterate()
return m
dt = 10.0
dx = 5.0
m_coarse = ModelCoarse1D(dt,
rho_0, rho_D, origin_flag, dx_agent,
mu,
L, dx,
c_D, c_sink, c_source)
for _ in range(200):
m_coarse.iterate()
rho_coarse = m_coarse.get_rho()
agent_fnames = bu.get_filenames(agent_dirname)
ts = np.array([bu.filename_to_model(f).t for f in agent_fnames])
i_agent_fname = find_nearest_index(m_coarse.t, ts)
m_agent = bu.filename_to_model(agent_fnames[i_agent_fname])
rho_agent = get_rho_agent(m_agent, m_coarse)
ms = [bu.filename_to_model(fname) for fname in bu.get_filenames(agent_dirname)]
rho_agent = np.mean([get_rho_agent(m, m_coarse)
for m in ms if m.t > m_coarse.t], axis=0)
x_tild = m_coarse.get_x() / np.sqrt(rho_D / c_sink)
ax.plot(x_tild, 1.0 * rho_coarse / rho_0, c=set2[0], label='Coarse')
ax.plot(x_tild + 0.1, rho_agent / rho_0, c=set2[1], label='Agent')
ax.legend(loc='lower right', fontsize=26, frameon=True)
ax.set_xlabel(r'$\tilde{x}$', fontsize=35)
ax.set_ylabel(r'$\rho / \rho_0$', fontsize=35)
ax.tick_params(axis='both', labelsize=26, pad=10.0)
ax.set_xlim(-2.5, 2.5)
