constellationnet/plot.py

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 = 16

# 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.Dark2

# Restore model from file
model = constellation.ConstellationNet(
    order=order,
    encoder_layers_sizes=(8, 4),
    decoder_layers_sizes=(4, 8),
    channel_model=constellation.GaussianChannel()
)

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

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

constellation = model.get_constellation()
util.plot_constellation(
    ax, constellation,
    model.channel, model.decoder,
    grid_step=0.001, noise_samples=5000
)

pyplot.show()