import constellation
from constellation import util
import torch
from matplotlib import pyplot
import matplotlib
from mpl_toolkits.axisartist.axislines import SubplotZero

# Number learned symbols
order = 4

# File in which the trained model is saved
input_file = 'output/constellation-order-{}.pth'.format(order)

# Color map used for decision regions
color_map = matplotlib.cm.Set1

# Restore model from file
model = constellation.ConstellationNet(
    order=order,
    encoder_layers_sizes=(4,),
    decoder_layers_sizes=(4,),
    channel_model=constellation.GaussianChannel()
)

model.load_state_dict(torch.load(input_file))
model.eval()

# Extract encoding
with torch.no_grad():
    encoded_vectors = model.encoder(
        util.messages_to_onehot(
            torch.arange(0, order),
            order
        )
    )

# Setup plot
fig = pyplot.figure()
ax = SubplotZero(fig, 111)
fig.add_subplot(ax)

# Extend axes symmetrically around zero so that they fit data
extent = max(
    abs(encoded_vectors.min()),
    abs(encoded_vectors.max())
) * 1.05
ax.set_xlim(-extent, extent)
ax.set_ylim(-extent, extent)

# Hide borders
for direction in ['left', 'bottom', 'right', 'top']:
    ax.axis[direction].set_visible(False)

# Show zero-centered axes
for direction in ['xzero', 'yzero']:
    axis = ax.axis[direction]
    axis.set_visible(True)
    axis.set_axisline_style("-|>")

# Configure axes ticks and labels
ax.annotate(
    'I', (1, 0.5), xycoords='axes fraction',
    xytext=(25, 0), textcoords='offset points',
    va='center', ha='right'
)

ax.axis['xzero'].major_ticklabels.set_backgroundcolor('white')
ax.axis['xzero'].major_ticklabels.set_ha('center')
ax.axis['xzero'].major_ticklabels.set_va('top')

ax.annotate(
    'Q', (0.5, 1), xycoords='axes fraction',
    xytext=(0, 25), textcoords='offset points',
    va='center', ha='center'
)

ax.axis['yzero'].major_ticklabels.set_rotation(-90)
ax.axis['yzero'].major_ticklabels.set_backgroundcolor('white')
ax.axis['yzero'].major_ticklabels.set_ha('left')
ax.axis['yzero'].major_ticklabels.set_va('center')

# Add a single tick on 0
ax.set_xticks(ax.get_xticks()[ax.get_xticks() != 0])
ax.set_yticks(ax.get_yticks()[ax.get_yticks() != 0])

ax.annotate(
    '0', (0, 0),
    xytext=(15, -10), textcoords='offset points',
    va='center', ha='center'
)

ax.grid()

# Plot decision regions
color_norm = matplotlib.colors.BoundaryNorm(range(order + 1), order)

step = 0.001 * extent
grid_range = torch.arange(-extent, extent, step)
grid_y, grid_x = torch.meshgrid(grid_range, grid_range)
grid_images = model.decoder(torch.stack((grid_x, grid_y), dim=2)).argmax(dim=2)

ax.imshow(
    grid_images, extent=(-extent, extent, -extent, extent),
    aspect="auto",
    origin="lower",
    cmap=color_map,
    norm=color_norm,
    alpha=0.15
)

# Plot encoded vectors
ax.scatter(
    *zip(*encoded_vectors.tolist()),
    zorder=10,
    c=range(len(encoded_vectors)),
    edgecolor='black',
    cmap=color_map,
    norm=color_norm,
)

for row in range(order):
    ax.annotate(
        row + 1, encoded_vectors[row],
        xytext=(5, 5), textcoords='offset points',
        backgroundcolor='white', zorder=9
    )

pyplot.show()