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lcs2d_plot_phase_joint.py
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lcs2d_plot_phase_joint.py
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from casadi import *
import matplotlib.pyplot as plt
import matplotlib.colors as clr
from util.logger import save_data, load_data
from diagnostics.lcs_analysis import LCSAnalyser
# ---------------------------- load the saved data ------------------------
save_dir = 'results'
data_name = 'results_info_'
# plot color
fcmap = plt.get_cmap('tab20')
rcmap = plt.get_cmap('tab10')
plt.rcParams.update({'font.size': 12})
# lcs analysis
analyser = LCSAnalyser()
# ---------------------------- print some basics ------------------------
rlcs_aux_id = range(25)
for k in rlcs_aux_id:
iter_data = load_data(data_name=data_name + str(k), save_dir=save_dir)
# load data
fmpc_dir_batch = iter_data['fmpc_dir_batch']
flcs_n_unique_mode = iter_data['flcs_n_unique_mode']
flcs_unique_mode_id = iter_data['flcs_unique_mode_id']
fmpc_lam_batch = iter_data['fmpc_lam_batch']
rmpc_dir_batch = iter_data['rmpc_dir_batch']
rcs_n_unique_mode = iter_data['rlcs_n_unique_mode']
rmpc_model_lam_batch = iter_data['rmpc_model_lam_batch']
rmpc_stat = analyser.modeChecker_manual(rmpc_model_lam_batch)
rlcs_unique_mode_id = rmpc_stat['unique_mode_id']
state_batch = iter_data['state_batch']
gridx = iter_data['gridx']
gridy = iter_data['gridy']
num_x, num_y = gridx.shape
print('\nfull lcs mode #:', flcs_n_unique_mode)
print('reduced lcs mode #:', rcs_n_unique_mode)
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
fmpc_dir_u = fmpc_dir_batch[:, 0].reshape((num_x, num_y))
fmpc_dir_v = fmpc_dir_batch[:, 1].reshape((num_x, num_y))
rmpc_dir_u = rmpc_dir_batch[:, 0].reshape((num_x, num_y))
rmpc_dir_v = rmpc_dir_batch[:, 1].reshape((num_x, num_y))
fmpc_strm_color = clr.to_rgba_array('black')[0]
fmpc_strm_color[-1] = 0.5
fmpc_strm_color = (*fmpc_strm_color,)
fmpc_strm_linewidth = 2
rmpc_strm_color = (clr.to_rgba_array('black')[0])
rmpc_strm_color[-1] = 0.5
rmpc_strm_color = (*rmpc_strm_color,)
rmpc_strm_linewidth = 2
fmpc_strm = ax[0].quiver(gridx[0::2, 0::2], gridy[0::2, 0::2],
fmpc_dir_u[0::2, 0::2], fmpc_dir_v[0::2, 0::2],
color='tab:blue',
scale=5, scale_units='inches',
width=0.006)
rmpc_strm = ax[0].quiver(gridx[0::2, 0::2], gridy[0::2, 0::2],
rmpc_dir_u[0::2, 0::2], rmpc_dir_v[0::2, 0::2],
color='tab:orange',
scale=5, scale_units='inches',
width=0.006)
ax[0].legend([fmpc_strm, rmpc_strm], ['Full-order MPC', 'Reduced-order MPC'],
loc='lower left', fontsize=10, framealpha=0.6)
ax[0].set_xlabel(r'$x_1$')
ax[0].set_ylabel(r'$x_2$')
ax[0].set_title('Phase plot comparison \n between full-order and reduced-order MPC', fontsize=15, pad=15)
ax[1].quiver(state_batch[:, 0], state_batch[0::1, 1],
fmpc_dir_batch[0::1, 0], fmpc_dir_batch[0::1, 1],
# color='black', alpha=0.6,
color=fcmap(flcs_unique_mode_id[0::1]),
scale=8, scale_units='inches',
width=0.006
)
ax[1].set_title('Phase plot and hybrid modes (colors) \n of full-order MPC', fontsize=15, pad=15)
ax[1].set_xlabel(r'$x_1$')
ax[1].set_ylabel(r'$x_2$')
ax[2].quiver(state_batch[0::1, 0], state_batch[0::1, 1],
rmpc_dir_batch[0::1, 0], rmpc_dir_batch[0::1, 1],
# color='black', alpha=0.6,
color=rcmap(rlcs_unique_mode_id[0::1]),
scale=8, scale_units='inches',
width=0.006
)
ax[2].set_title('Phase plot and hybrid modes (colors) \n of reduced-order MPC', fontsize=15, pad=15)
ax[2].set_xlabel(r'$x_1$')
ax[2].set_ylabel(r'$x_2$')
fig.suptitle('Learning iteration ' + str(k), fontsize=20)
plt.tight_layout()
# plt.savefig('./results/img/iter' + str(k) + '.png', dpi=100)
plt.show()