asdm.asdm_recoverable(u, bw, b, d, k)
except ValueError('reconstruction condition not satisfied'):
sys.exit()
output_count += 1
fig_title = 'Signal Encoded Using ASDM Encoder'
print fig_title
s = func_timer(asdm.asdm_encode)(u, dt, b, d, k)
pl.plot_encoded(t, u, s, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using ASDM Decoder'
print fig_title
u_rec = func_timer(asdm.asdm_decode)(s, dur, dt, bw, b, d, k)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Threshold-Insensitive ASDM Decoder'
print fig_title
u_rec_ins = func_timer(asdm.asdm_decode_ins)(s, dur, dt, bw, b)
pl.plot_compare(t, u, u_rec_ins, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Decoded Using Fast ASDM Decoder'
print fig_title
u_rec = func_timer(asdm.asdm_decode_fast)(s, dur, dt, bw, M, b, d, k)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
a = 1.0/0.015
c = 1.0/3.0
h_list = [[0,0],[0,0]]
h_list[0][0] = lambda t : 0
h_list[0][1] = lambda t : -c*np.exp(-a*t)*((a*t)**5/120.0-(a*t)**7/5040.0)*(t>=0)
h_list[1][0] = lambda t : c*np.exp(-a*t)*((a*t)**5/120.0-(a*t)**7/5040.0)*(t>=0)
h_list[1][1] = lambda t : 0
fig_title = 'Signal Encoded Using Coupled IAF Encoder'
print fig_title
s_list = func_timer(iaf.iaf_encode_coupled)(u, dt, [b1, b2], [d1, d2],
[k1, k2], h_list, [type1, type2])
output_count += 1
pl.plot_encoded(t, u, s_list[0], fig_title + ' #1',
output_name + str(output_count) + output_ext)
output_count += 1
pl.plot_encoded(t, u, s_list[1], fig_title + ' #2',
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Coupled IAF Decoder'
print fig_title
u_rec = func_timer(iaf.iaf_decode_coupled)(s_list, dur, dt,
[b1, b2], [d1, d2], [k1, k2],
h_list)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
w_list = map(list, np.reshape(randu(0.5, 1.0, N * M), (N, M)))
T_block = T / 2.0
T_overlap = T_block / 3.0
fig_title = 'Signal Encoded Using Real-Time Delayed IAF Encoder'
print fig_title
s_list = func_timer(rt.iaf_encode_delay)(u_list, T_block, t_start, dt, b_list,
d_list, k_list, a_list, w_list)
for i in xrange(M):
for j in xrange(N):
fig_title_out = fig_title + '\n(Signal #' + str(i+1) + \
', Neuron #' + str(j+1) + ')'
pl.plot_encoded(t_enc, u_list[i][k_start:k_end],
s_list[j][np.cumsum(s_list[j]) < T], fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Real-Time Delayed IAF Decoder'
print fig_title
u_rec_list = func_timer(rt.iaf_decode_delay)(s_list, T_block, T_overlap, dt,
b_list, d_list, k_list, a_list,
w_list)
for i in xrange(M):
fig_title_out = fig_title + ' (Signal #' + str(i + 1) + ')'
pl.plot_compare(t_enc, u_list[i][k_start:k_end],
u_rec_list[i][0:k_end - k_start], fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
M = 250
Omega = 2*np.pi*2000
T = 2*np.pi*M/Omega
dt = 1e-5
t = np.arange(0, T, dt)
u = tp.gen_trig_poly(T, dt, M)
am_rec = tp.get_dirichlet_coeffs(u, dt, M)
# Try to recover the Dirichlet coefficients of the generated signal
# using different methods. Note that this only works if u contains an
# entire period of the signal (i.e., arange(0, T, dt)):
print 'reconstructing signal from recovered coefficients..'
u_rec = tp.gen_trig_poly(T, dt, am_rec)
pl.plot_compare(t, u, u_rec, 'Signal Reconstruction',
output_name + str(output_count) + output_ext)
output_count += 1
# Create a filter:
h = make_gammatone(t, 16, 0)
hm = tp.get_dirichlet_coeffs(h, dt, M)
h_rec = tp.gen_trig_poly(T, dt, hm)
pl.plot_compare(t, h, h_rec, 'Filter Reconstruction',
output_name + str(output_count) + output_ext)
output_count += 1
# Filter the signal using FFTs:
v_fft = filter_trig_poly_fft(u, h)
pl.plot_signal(t, v_fft, 'Filtered Signal',
output_name + str(output_count) + output_ext)
asdm.asdm_recoverable(u, bw, b, d, k)
except ValueError('reconstruction condition not satisfied'):
sys.exit()
output_count += 1
fig_title = 'Signal Encoded Using Real-Time ASDM Encoder'
print fig_title
encoder = rt.ASDMRealTimeEncoder(dt, b, d, k)
s = func_timer(encoder)(u)
pl.plot_encoded(t, u, s, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Real-Time ASDM Decoder'
print fig_title
decoder = rt.ASDMRealTimeDecoder(dt, bw, b, d, k, N, M, K)
u_rec = func_timer(decoder)(s)
end = min(len(u), len(u_rec))
pl.plot_compare(t[:end], u[:end], u_rec[:end], fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Real-Time\nThreshold-Insensitive ASDM Decoder'
print fig_title
decoder = rt.ASDMRealTimeDecoderIns(dt, bw, b, N, M, K)
u_rec_ins = func_timer(decoder)(s)
end = min(len(u), len(u_rec_ins))
pl.plot_compare(t[:end], u[:end], u_rec_ins[:end], fig_title,
output_name + str(output_count) + output_ext)
M = 250
Omega = 2 * np.pi * 2000
T = 2 * np.pi * M / Omega
dt = 1e-5
t = np.arange(0, T, dt)
u = tp.gen_trig_poly(T, dt, M)
am_rec = tp.get_dirichlet_coeffs(u, dt, M)
# Try to recover the Dirichlet coefficients of the generated signal
# using different methods. Note that this only works if u contains an
# entire period of the signal (i.e., arange(0, T, dt)):
print 'reconstructing signal from recovered coefficients..'
u_rec = tp.gen_trig_poly(T, dt, am_rec)
pl.plot_compare(t, u, u_rec, 'Signal Reconstruction',
output_name + str(output_count) + output_ext)
output_count += 1
# Create a filter:
h = make_gammatone(t, 16, 0)
hm = tp.get_dirichlet_coeffs(h, dt, M)
h_rec = tp.gen_trig_poly(T, dt, hm)
pl.plot_compare(t, h, h_rec, 'Filter Reconstruction',
output_name + str(output_count) + output_ext)
output_count += 1
# Filter the signal using FFTs:
v_fft = filter_trig_poly_fft(u, h)
pl.plot_signal(t, v_fft, 'Filtered Signal',
output_name + str(output_count) + output_ext)
a_list = map(list, np.reshape(np.random.exponential(0.003, N*M), (N, M)))
w_list = map(list, np.reshape(randu(0.5, 1.0, N*M), (N, M)))
fig_title = 'Signal Encoded Using Delayed IAF Encoder'
print fig_title
s_list = func_timer(iaf.iaf_encode_delay)(u_list, t_start, dt, b_list, d_list,
k_list, a_list, w_list)
for i in xrange(M):
for j in xrange(N):
fig_title_out = fig_title + '\n(Signal #' + str(i+1) + \
', Neuron #' + str(j+1) + ')'
pl.plot_encoded(t_enc, u_list[i][k_start:k_end],
s_list[j][np.cumsum(s_list[j])<T],
fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Delayed IAF Decoder'
print fig_title
u_rec_list = func_timer(iaf.iaf_decode_delay)(s_list, T, dt,
b_list, d_list, k_list,
a_list, w_list)
for i in xrange(M):
fig_title_out = fig_title + ' (Signal #' + str(i+1) + ')'
pl.plot_compare(t_enc, u_list[i][k_start:k_end],
u_rec_list[i][0:k_end-k_start], fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
np.random.seed(0)
noise_power = None
fig_title = 'IAF input signal'
print fig_title
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power)
pl.plot_signal(t, u, fig_title, output_dir + 'overview_input' + output_ext)
b = 3.5 # bias
d = 0.7 # threshold
R = np.inf # resistance
C = 0.01 # capacitance
try:
iaf.iaf_recoverable(u, bw, b, d, R, C)
except ValueError('reconstruction condition not satisfied'):
sys.exit()
fig_title = 'Signal encoded by IAF neuron'
print fig_title
s = func_timer(iaf.iaf_encode)(u, dt, b, d, R, C)
pl.plot_encoded(t, u, s, fig_title,
output_dir + 'overview_encoded' + output_ext)
fig_title = 'Decoded signal and reconstruction error'
print fig_title
u_rec = func_timer(iaf.iaf_decode)(s, dur, dt, bw, b, d, R, C)
pl.plot_compare(t, u, u_rec, fig_title,
output_dir + 'overview_decoded' + output_ext)
noise_power = None
fig_title = 'IAF input signal';
print fig_title
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power)
pl.plot_signal(t, u, fig_title, output_dir + 'overview_input' + output_ext)
b = 3.5 # bias
d = 0.7 # threshold
R = np.inf # resistance
C = 0.01 # capacitance
try:
iaf.iaf_recoverable(u, bw, b, d, R, C)
except ValueError('reconstruction condition not satisfied'):
sys.exit()
fig_title = 'Signal encoded by IAF neuron'
print fig_title
s = func_timer(iaf.iaf_encode)(u, dt, b, d, R, C)
pl.plot_encoded(t, u, s, fig_title,
output_dir + 'overview_encoded' + output_ext)
fig_title = 'Decoded signal and reconstruction error'
print fig_title
u_rec = func_timer(iaf.iaf_decode)(s, dur, dt, bw, b, d, R, C)
pl.plot_compare(t, u, u_rec, fig_title,
output_dir + 'overview_decoded' + output_ext)
