Python func_timerの例

コード例 #1

dur = 0.1
dt = 1e-6
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

コード例 #2

ファイル: iaf_pop_spline_demo.py プロジェクト: bionet/ted.python

# 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
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)
pl.plot_signal(t, u, fig_title,
               output_name + str(output_count) + output_ext)

# Test leaky IAF algorithms:

b1 = 3.5   # bias
d1 = 0.7   # threshold
R1 = 10.0  # resistance
C1 = 0.01  # capacitance

b2 = 3.4   # bias
d2 = 0.8   # threshold
R2 = 9.0   # resistance
C2 = 0.01  # capacitance

コード例 #3

ファイル: iaf_demo.py プロジェクト: era1erushe/ted.python

# 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
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)
pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext)

b = 3.5  # bias
d = 0.7  # threshold
R = 10.0  # resistance
C = 0.01  # capacitance

try:
    iaf.iaf_recoverable(u, bw, b, d, R, C)
except ValueError('reconstruction condition not satisfied'):
    sys.exit()

M = 5  # number of bins for fast decoding algorithm
L = 5  # number of recursions for recursive decoding algorithm

コード例 #4

ファイル: asdm_pop_demo.py プロジェクト: era1erushe/ted.python

# 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
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)

b1 = 3.5  # bias
d1 = 0.7  # threshold
k1 = 0.01  # scaling factor

try:
    asdm.asdm_recoverable(u, bw, b1, d1, k1)
except ValueError('reconstruction condition not satisfied'):
    sys.exit()

b2 = 3.4  # bias
d2 = 0.8  # threshold
k2 = 0.01  # scaling factor

コード例 #5

ファイル: iaf_trig_cuda_demo.py プロジェクト: era1erushe/ted.python

# 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
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)
pl.plot_signal(t, u, fig_title,
               output_name + str(output_count) + output_ext)

b = 3.5   # bias
d = 0.7   # threshold
R = 10.0  # resistance
C = 0.01  # capacitance

try:
    iaf.iaf_recoverable(u, bw, b, d, R, C)
except ValueError('reconstruction condition not satisfied'):
    sys.exit()

M = 31 # 2*M+1 trigonometric polynomials are used in the reconstruction

コード例 #6

ファイル: iaf_demo.py プロジェクト: bionet/ted.python

# 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
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)
pl.plot_signal(t, u, fig_title,
               output_name + str(output_count) + output_ext)

b = 3.5   # bias
d = 0.7   # threshold
R = 10.0  # resistance
C = 0.01  # capacitance

try:
    iaf.iaf_recoverable(u, bw, b, d, R, C)
except ValueError('reconstruction condition not satisfied'):
    sys.exit()

M = 5 # number of bins for fast decoding algorithm
L = 5 # number of recursions for recursive decoding algorithm

コード例 #7

ファイル: iaf_pop_trig_cuda_demo.py プロジェクト: bionet/ted.python

# Define algorithm parameters and input signal:
dur = 0.1
dt = 1e-6
f = 64
bw = 2*np.pi*f
t = np.arange(0, dur, dt)

np.random.seed(0)

noise_power = None
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)
pl.plot_signal(t, u, fig_title,
               output_name + str(output_count) + output_ext)

# Trigonometric polynomial order:
M = 75

# Test leaky IAF algorithms:

b1 = 7.5   # bias
d1 = 0.7   # threshold
R1 = 10.0  # resistance
C1 = 0.01  # capacitance

try:
    iaf.iaf_recoverable(u, bw, b1, d1, R1, C1)

コード例 #8

# 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
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'

コード例 #9

ファイル: iaf_pop_trig_demo.py プロジェクト: era1erushe/ted.python

# 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
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)
pl.plot_signal(t, u, fig_title,
               output_name + str(output_count) + output_ext)

# Trigonometric polynomial order:
M = 32

# Test leaky IAF algorithms:

b1 = 3.5   # bias
d1 = 0.7   # threshold
R1 = 10.0  # resistance
C1 = 0.01  # capacitance

try:
    iaf.iaf_recoverable(u, bw, b1, d1, R1, C1)

コード例 #10

ファイル: gen_band_limited_demo.py プロジェクト: era1erushe/ted.python

output_name = 'gen_band_limited_demo_'
output_count = 0
output_ext = '.png'

print 'creating test signal..'
dur = 1.0
fs = 1e4
dt = 1.0/fs
f = 10
t = np.arange(0, dur, dt)

np.random.seed(0)
out_count = 0
fig_title = 'test signal with no noise'
print fig_title
u = func_timer(bl.gen_band_limited)(dur, dt, f)
pl.plot_signal(t, u, fig_title,
               output_name + str(output_count) + output_ext)

np.random.seed(0)
output_count += 1
noise_power = 1
fig_title = 'test 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)

np.random.seed(0)
output_count += 1
noise_power = -5

コード例 #11

ファイル: iaf_pop_trig_cuda_demo.py プロジェクト: era1erushe/ted.python

# Define algorithm parameters and input signal:
dur = 0.1
dt = 1e-6
f = 64
bw = 2 * np.pi * f
t = np.arange(0, dur, dt)

np.random.seed(0)

noise_power = None
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)
pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext)

# Trigonometric polynomial order:
M = 75

# Test leaky IAF algorithms:

b1 = 7.5  # bias
d1 = 0.7  # threshold
R1 = 10.0  # resistance
C1 = 0.01  # capacitance

try:
    iaf.iaf_recoverable(u, bw, b1, d1, R1, C1)
except ValueError('reconstruction condition not satisfied'):

コード例 #12

ファイル: iaf_delay_demo.py プロジェクト: bionet/ted.python

N = 9 # number of neurons

# Starting and ending points of interval that is encoded:
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:

コード例 #13

ファイル: asdm_demo.py プロジェクト: bionet/ted.python

# 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
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

コード例 #14

# 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
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)
pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext)

# Test leaky IAF algorithms:

b1 = 3.5  # bias
d1 = 0.7  # threshold
R1 = 10.0  # resistance
C1 = 0.01  # capacitance

b2 = 3.4  # bias
d2 = 0.8  # threshold
R2 = 9.0  # resistance
C2 = 0.01  # capacitance

output_count += 1

コード例 #15

# 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
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)

# Define encoding parameters:
dte = dt
quad_method = 'trapz'

b = 3.5   # bias
d = 0.7   # threshold
k = 0.01  # scaling factor

# Define real time decoder stitching parameters:
N = 10
M = 2
K = 1

コード例 #16

ファイル: iaf_pop_demo.py プロジェクト: bionet/ted.python

# 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
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)
pl.plot_signal(t, u, fig_title,
               output_name + str(output_count) + output_ext)

# Test leaky IAF algorithms:

b1 = 3.5   # bias
d1 = 0.7   # threshold
R1 = 10.0  # resistance
C1 = 0.01  # capacitance

try:
    iaf.iaf_recoverable(u, bw, b1, d1, R1, C1)
except ValueError('reconstruction condition not satisfied'):
    sys.exit()

コード例 #17

ファイル: iaf_delay_rt_demo.py プロジェクト: era1erushe/ted.python

N = 9  # number of neurons

# Starting and ending points of interval that is encoded:
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)``."""

コード例 #18

ファイル: iaf_coupled_demo.py プロジェクト: bionet/ted.python

dur = 0.1
dt = 1e-6
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

コード例 #19

ファイル: asdm_pop_demo.py プロジェクト: bionet/ted.python

# 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
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)

b1 = 3.5   # bias
d1 = 0.7   # threshold
k1 = 0.01  # scaling factor

try:
    asdm.asdm_recoverable(u, bw, b1, d1, k1)
except ValueError('reconstruction condition not satisfied'):
    sys.exit()

b2 = 3.4   # bias
d2 = 0.8   # threshold
k2 = 0.01  # scaling factor