layer1 = GrFNN(params1,
frequency_range=(50,200),
num_oscs=200,
stimulus_conn_type='active')
layer2 = GrFNN(params2,
frequency_range=(50,200),
num_oscs=200)
# create a connection matrix
# C = make_connections(layer1, layer2, 1, 1.005, self_connect=True)
C = np.eye(len(layer2.f), len(layer1.f))
# Make the model
model = Model()
model.add_layer(layer1, input_channel=0) # layer one will receive the external stimulus
model.add_layer(layer2) # layer 2 is a hidden layer (no external input)
# connect the layers
conn = model.connect_layers(layer1, layer2, C, '1freq', self_connect=True)
plot_connections(conn, title='Connection matrix (abs)')
# prepare real-time plots
GrFNN_RT_plot(layer1, update_interval=0.005, title='First Layer')
GrFNN_RT_plot(layer2, update_interval=0.005, title='Second Layer')
# 3. Run the model
model.run(s, t, dt)
# plt.plot(t, np.real(s))
# Create a GrFNN layer
layer = GrFNN(params,
frequency_range=(0.5,2),
num_oscs=200,
stimulus_conn_type='linear')
# store layer's states
layer.save_states = True # True by default, but it can be disabled to save memory
print(layer)
# create the model and add the layer
model = Model()
model.add_layer(layer, input_channel=0)
# setup real-time plot
GrFNN_RT_plot(layer, update_interval=0.2, title='Single Layer')
# run the model
model.run(s, t, dt)
# plot time-frequency representation (magnitude and phase)
tf_detail(layer.Z, t, layer.f, t_detail=[np.max(t)/2, np.max(t)], x=np.real(s))
tf_detail(layer.Z, t, layer.f, x=np.real(s), display_op=np.angle)
plt.show()
