def test_mpl_slice_plot(self):
if show_plots:
vis.MplSlicePlot(self.test_data + self.test_data + self.test_data,
spatial_point=0.5,
ylabel='$x(0,t)$',
legend_label=['1', '2', '3', '4', '5', '6'])
plt.show()
evald_shifted_x.output_data +
evald_modal_xi.output_data -
evald_modal_T0_xid.output_data,
name="x_i(t) approximated")
# evaluate approximation of x
evald_fem_x = sim.evaluate_approximation("fem_funcs",
q,
t,
spatial_domain,
name="x(z,t) simulation")
# pyqtgraph visualisations
win1 = vis.PgAnimatedPlot(
[evald_fem_x, evald_modal_xi, evald_appr_xi, evald_xd, evald_xi_desired],
dt=temporal_domain.step)
win2 = vis.PgSurfacePlot([evald_xd], title=evald_xd.name, grid_height=1)
win3 = vis.PgSurfacePlot([evald_fem_x], title=evald_fem_x.name, grid_height=1)
# plots
vis.MplSurfacePlot([evald_appr_xi])
vis.MplSlicePlot(
[evald_xd, evald_fem_x],
spatial_point=0,
legend_label=[evald_xd.name, evald_fem_x.name],
)
# show pyqtgraph and matplotlib plots/visualizations
pg.QtGui.QApplication.instance().exec_()
plt.show()
# input with feedback
control_law = sim.SimulationInputSum([traj, controller])
# determine (A,B) with modal-transfomation
A = np.diag(eig_values)
B = -a2 * np.array([eig_funcs[i].derive()(l) for i in range(n)])
ss = sim.StateSpace("eig_funcs", A, B, input_handle=control_law)
# evaluate desired output data
z_d = np.linspace(0, l, len(spatial_domain))
y_d, t_d = tr.gevrey_tanh(T, 80)
C = tr.coefficient_recursion(np.zeros(y_d.shape), y_d, param)
x_l = tr.power_series(z_d, t_d, C)
evald_traj = vis.EvalData([t_d, z_d], x_l, name="x(z,t) desired")
# simulate
t, q = sim.simulate_state_space(ss, initial_weights, temporal_domain)
# pyqtgraph visualization
evald_x = sim.evaluate_approximation("eig_funcs", q, t, spatial_domain,
name="x(z,t) with x(z,0)=" + str(init_profile))
win1 = vis.PgAnimatedPlot([evald_x, evald_traj], title="animation", dt=temporal_domain.step)
win2 = vis.PgSurfacePlot([evald_x], title=evald_x.name, grid_height=1)
win3 = vis.PgSurfacePlot([evald_traj], title=evald_traj.name, grid_height=1)
pg.QtGui.QApplication.instance().exec_()
# visualization
vis.MplSlicePlot([evald_x, evald_traj], time_point=1, legend_label=["$x(z,1)$", "$x_d(z,1)$"], legend_location=2)
plt.show()
d_approx_target_state=xd_ti_at_l,
integral_kernel_zz=int_kernel_zz(l),
original_beta=beta_i,
target_beta=beta_ti,
trajectory=traj,
scale=transform_i(-l))
# determine (A,B)
rad_pde = ut.get_parabolic_robin_weak_form("fem_funcs", "fem_funcs", controller, param, spatial_domain.bounds)
cf = sim.parse_weak_formulation(rad_pde)
ss_weak = cf.convert_to_state_space()
# simulate
t, q = sim.simulate_state_space(ss_weak, init_profile * np.ones(n_fem), temporal_domain)
# evaluate desired output data
y_d, t_d = tr.gevrey_tanh(T, 80)
C = tr.coefficient_recursion(y_d, alpha * y_d, param)
x_l = tr.power_series(np.array(spatial_domain), t_d, C)
evald_traj = vis.EvalData([t_d, spatial_domain], x_l, name="x(z,t) desired")
# pyqtgraph visualization
eval_d = sim.evaluate_approximation("fem_funcs", q, t, spatial_domain, name="x(z,t) with x(z,0)=" + str(init_profile))
win1 = vis.PgAnimatedPlot([eval_d, evald_traj], title="animation", dt=temporal_domain.step)
win2 = vis.PgSurfacePlot([eval_d], title=eval_d.name, grid_height=1)
win3 = vis.PgSurfacePlot([evald_traj], title=evald_traj.name, grid_height=1)
pg.QtGui.QApplication.instance().exec_()
# matplotlib visualization
vis.MplSlicePlot([evald_traj, eval_d], spatial_point=0, legend_label=["$x_d(0,t)$", "$x(0,t)$"])
plt.show()
rad_pde = ut.get_parabolic_robin_weak_form("fem_funcs", "fem_funcs",
controller, param,
spatial_domain.bounds)
cf = sim.parse_weak_formulation(rad_pde)
ss_weak = cf.convert_to_state_space()
# simulate
t, q = sim.simulate_state_space(ss_weak, np.zeros((len(fem_funcs))),
temporal_domain)
# pyqtgraph visualization
evald_x = sim.evaluate_approximation("fem_funcs",
q,
t,
spatial_domain,
name="x(z,t)")
win1 = vis.PgAnimatedPlot([evald_x],
title="animation",
dt=temporal_domain.step * .25)
win2 = vis.PgSurfacePlot(evald_x, title=evald_x.name, grid_height=1)
pg.QtGui.QApplication.instance().exec_()
# visualization
vis.MplSlicePlot([evald_x],
time_point=1,
legend_label=["$x(z,1)$"],
legend_location=1)
vis.MplSurfacePlot(evald_x)
plt.show()