def _run_model(model, dbspl, cf, model_pars):
duration = 100e-3
onset = 10e-3
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250,250,250))
model_pars.setdefault('seed', 0)
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=duration,
pad=0,
dbspl=dbspl
)
anf = model(
sound=sound,
cf=cf,
**model_pars
)
### We want to make sure the the output CF is equal to the desired
### CF.
real_cf, = np.unique(anf['cf'])
assert real_cf == cf
hsr = anf[anf['type']=='hsr']
hsr = th.trim(hsr, onset, None)
si_hsr = th.vector_strength(hsr, cf)
msr = anf[anf['type']=='msr']
msr = th.trim(msr, onset, None)
si_msr = th.vector_strength(msr, cf)
lsr = anf[anf['type']=='lsr']
lsr = th.trim(lsr, onset, None)
si_lsr = th.vector_strength(lsr, cf)
# print(si_hsr)
# th.plot_raster(anf)
# th.show()
vss = {
'hsr': si_hsr,
'msr': si_msr,
'lsr': si_lsr,
}
return vss
def _run_model(model, dbspl, cf, model_pars):
duration = 100e-3
onset = 10e-3
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250, 250, 250))
model_pars.setdefault('seed', 0)
sound = wv.ramped_tone(fs=fs,
freq=cf,
duration=duration,
pad=0,
dbspl=dbspl)
anf = model(sound=sound, cf=cf, **model_pars)
# th.plot_raster(anf)
# th.show()
# We want to make sure the the output CF is equal to the desired
# CF.
real_cf, = np.unique(anf['cf'])
assert real_cf == cf
vss = {}
for typ, group in anf.groupby('type'):
trimmed = th.trim(group, onset, None)
vs = th.vector_strength(trimmed, cf)
vss[typ] = vs
return vss
def _run_model(model, fm, cf, dbspl, model_pars, m):
duration = 0.6
onset = 10e-3
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250, 0, 0))
model_pars.setdefault('seed', 0)
sound = wv.amplitude_modulated_tone(
fs=fs,
fm=fm,
fc=cf,
m=m,
duration=duration,
dbspl=dbspl,
)
anf = model(sound=sound, cf=cf, **model_pars)
trimmed = th.trim(anf, onset)
vs = th.vector_strength(trimmed, fm)
gain = 20 * np.log10(2 * vs / m)
return gain
def _run_model(model, fm, cf, dbspl, model_pars, m):
duration = 0.6
onset = 10e-3
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250, 0, 0))
model_pars.setdefault('seed', 0)
sound = wv.amplitude_modulated_tone(
fs=fs,
fm=fm,
fc=cf,
m=m,
duration=duration,
dbspl=dbspl,
)
anf = model(
sound=sound,
cf=cf,
**model_pars
)
trimmed = th.trim(anf, onset)
vs = th.vector_strength(trimmed, fm)
gain = 20 * np.log10(2*vs / m)
return gain
def main():
### Load spike trains
spike_trains = load_anf_zilany2014()
print(spike_trains.head())
### Calculate vector strength
cf, = spike_trains.cf.unique()
onset = 10e-3 # ms
trimmed = th.trim(spike_trains, onset, None)
vs = th.vector_strength(trimmed, freq=cf)
print()
print("Vector strength: {}".format(vs))
### Plot raster plot
th.plot_raster(spike_trains)
### Show the plot
th.show() # Equivalent to plt.show()
def test_vector_strength():
### Uniform spikes
trains = th.make_trains(
[np.arange(0, 1, 1/3600)]
)
si = th.vector_strength(
trains,
freq=10
)
assert_almost_equal(si, 0)
### Perfect synchrony
trains = th.make_trains(
[np.zeros(100)],
duration=0.1
)
si = th.vector_strength(
trains,
freq=10
)
assert_equal(si, 1)
### Carefully chosen
trains = th.make_trains(
[np.tile([0, 0.25], 10)]
)
si = th.vector_strength(
trains,
freq=1
)
assert_equal(si, np.sqrt(2)/2)
def _run_model(model, dbspl, cf, model_pars):
duration = 100e-3
onset = 10e-3
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250,250,250))
model_pars.setdefault('seed', 0)
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=duration,
pad=0,
dbspl=dbspl
)
anf = model(
sound=sound,
cf=cf,
**model_pars
)
# th.plot_raster(anf)
# th.show()
### We want to make sure the the output CF is equal to the desired
### CF.
real_cf, = np.unique(anf['cf'])
assert real_cf == cf
vss = {}
for typ,group in anf.groupby('type'):
trimmed = th.trim(group, onset, None)
vs = th.vector_strength(trimmed, cf)
vss[typ] = vs
return vss
def main():
# Load spike trains
spike_trains = load_anf_zilany2014()
print(spike_trains.head())
# Calculate vector strength
cf, = spike_trains.cf.unique()
onset = 10e-3
trimmed = th.trim(spike_trains, onset, None)
vs = th.vector_strength(trimmed, freq=cf)
print()
print("Vector strength: {}".format(vs))
# Plot raster plot
th.plot_raster(spike_trains)
# Show the plot
th.show() # Equivalent to plt.show()
