def save_plots(target_state, best_state, cost_progress, *, modes, offset=-0.11, l=5,
out_dir='sim_results', ID='state_learner', **kwargs):
"""Generate and save plots"""
if modes == 1:
# generate a wigner function plot of the target state
fig1, ax1 = wigner_3D_plot(target_state, offset=offset, l=l)
fig1.savefig(os.path.join(out_dir, ID+'_targetWigner.png'))
# generate a wigner function plot of the learnt state
fig2, ax2 = wigner_3D_plot(best_state, offset=offset, l=l)
fig2.savefig(os.path.join(out_dir, ID+'_learntWigner.png'))
# generate a wavefunction plot of the target state
figW1, axW1 = wavefunction_plot(target_state, l=l)
figW1.savefig(os.path.join(out_dir, ID+'_targetWavefunction.png'))
# generate a wavefunction plot of the learnt state
figW2, axW2 = wavefunction_plot(best_state, l=l)
figW2.savefig(os.path.join(out_dir, ID+'_learntWavefunction.png'))
elif modes == 2:
# generate a 3D wavefunction plot of the target state
figW1, axW1 = two_mode_wavefunction_plot(target_state, l=l)
figW1.savefig(os.path.join(out_dir, ID+'_targetWavefunction.png'))
# generate a 3D wavefunction plot of the learnt state
figW2, axW2 = two_mode_wavefunction_plot(best_state, l=l)
figW2.savefig(os.path.join(out_dir, ID+'_learntWavefunction.png'))
# generate a cost function plot
figC, axC = plot_cost(cost_progress)
figC.savefig(os.path.join(out_dir, ID+'_cost.png'))
def save_plots(target_unitary,
learnt_unitary,
eq_state_learnt,
eq_state_target,
cost_progress,
*,
modes,
offset=-0.11,
l=5,
out_dir='sim_results',
ID='gate_synthesis',
**kwargs):
"""Generate and save plots"""
square = not kwargs.get('maps_outside', True)
if modes == 1:
# generate a wigner function plot of the target state
fig1, ax1 = wigner_3D_plot(eq_state_target, offset=offset, l=l)
fig1.savefig(os.path.join(out_dir, ID + '_targetWigner.png'))
# generate a wigner function plot of the learnt state
fig2, ax2 = wigner_3D_plot(eq_state_learnt, offset=offset, l=l)
fig2.savefig(os.path.join(out_dir, ID + '_learntWigner.png'))
# generate a matrix plot of the target and learnt unitaries
figW1, axW1 = one_mode_unitary_plots(target_unitary,
learnt_unitary,
square=square)
figW1.savefig(os.path.join(out_dir, ID + '_unitaryPlot.png'))
elif modes == 2:
# generate a 3D wavefunction plot of the target state
figW1, axW1 = two_mode_wavefunction_plot(eq_state_target, l=l)
figW1.savefig(os.path.join(out_dir, ID + '_targetWavefunction.png'))
# generate a 3D wavefunction plot of the learnt state
figW2, axW2 = two_mode_wavefunction_plot(eq_state_learnt, l=l)
figW2.savefig(os.path.join(out_dir, ID + '_learntWavefunction.png'))
# generate a matrix plot of the target and learnt unitaries
figM1, axM1 = two_mode_unitary_plots(target_unitary,
learnt_unitary,
square=square)
figM1.savefig(os.path.join(out_dir, ID + '_unitaryPlot.png'))
# generate a cost function plot
figC, axC = plot_cost(cost_progress)
figC.savefig(os.path.join(out_dir, ID + '_cost.png'))