f = 100
bw = 2*np.pi*f
t = np.arange(0, dur, dt)
np.random.seed(0)
noise_power = None
comps = 10
if noise_power == None:
fig_title = 'IAF Input Signal with No Noise'
else:
fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power
print fig_title
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power, comps)
u /= max(u)
pl.plot_signal(t, u, fig_title,
output_name + str(output_count) + output_ext)
b = 4
d = 0.75
k = 0.01
b1 = b # bias
d1 = d # threshold
k1 = k # integration constant
type1 = 1 # ON-type neuron
b2 = -b # bias
d2 = -d # threshold
k2 = k # integration constant
type2 = -1 # OFF-type neuron
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)
dur = 0.1
dt = 1e-6
f = 32
bw = 2 * np.pi * f
t = np.arange(0, dur, dt)
np.random.seed(0)
noise_power = None
if noise_power == None:
fig_title = 'ASDM Input Signal with No Noise'
else:
fig_title = 'ASDM Input Signal with %d dB of Noise' % noise_power
print fig_title
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power)
pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext)
b = 3.5 # bias
d = 0.7 # threshold
k = 0.01 # scaling factor
M = 5 # number of bins for fast decoding algorithm
try:
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
t_start = 0.02
t_end = t_start + T
if t_end > dur:
raise ValueError('t_start is too large')
k_start = int(np.round(t_start / dt))
k_end = int(np.round(t_end / dt))
t_enc = np.arange(k_start, k_end, dtype=np.float) * dt
u_list = []
for i in xrange(M):
fig_title_in = fig_title + ' (Signal #' + str(i + 1) + ')'
print fig_title_in
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power, comps)
u /= max(u)
u *= 1.5
pl.plot_signal(t_enc, u[k_start:k_end], fig_title_in,
output_name + str(output_count) + output_ext)
u_list.append(u)
output_count += 1
t = np.arange(len(u_list[0]), dtype=np.float) * dt
# Define neuron parameters:
def randu(a, b, *d):
"""Create an array of the given shape and propagate it with random
samples from a uniform distribution over ``[a, b)``."""
if a >= b:
raise ValueError('b must exceed a')
return a + (b - a) * np.random.rand(*d)
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)
t_start = 0.02
t_end = t_start+T
if t_end > dur:
raise ValueError('t_start is too large')
k_start = int(np.round(t_start/dt))
k_end = int(np.round(t_end/dt))
t_enc = np.arange(k_start, k_end, dtype=np.float)*dt
u_list = []
for i in xrange(M):
fig_title_in = fig_title + ' (Signal #' + str(i+1) + ')'
print fig_title_in
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power, comps)
u /= max(u)
u *= 1.5
pl.plot_signal(t_enc, u[k_start:k_end], fig_title_in,
output_name + str(output_count) + output_ext)
u_list.append(u)
output_count += 1
t = np.arange(len(u_list[0]), dtype=np.float)*dt
# Define neuron parameters:
def randu(a, b, *d):
"""Create an array of the given shape and propagate it with random
samples from a uniform distribution over ``[a, b)``."""
if a >= b:
raise ValueError('b must exceed a')
return a+(b-a)*np.random.rand(*d)
b_list = list(randu(2.3, 3.3, N))
output_dir = 'images/'
# Define algorithm parameters and input signal:
dur = 0.1
dt = 1e-6
f = 32
bw = 2 * np.pi * f
t = np.arange(0, dur, dt)
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 = 'images/'
# Define algorithm parameters and input signal:
dur = 0.1
dt = 1e-6
f = 32
bw = 2*np.pi*f
t = np.arange(0, dur, dt)
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,
