def step_waves(I, f, duty, t, dt):
times = fsutil.create_times(t, dt)
wave = I * square(2 * np.pi * f * times - np.pi, duty=duty)
wave[wave < 0] = 0.0
return wave
def load_smith_lfps(name, channel_num):
"""Load Smith LFP data.
NOTE: .ns2 files must first be converted to .mat
"""
# Load data
fi = h5py.File(name)
# Parse time
fs = float(fi['Fs'].value)
n_samples = float(fi['nSamples'].value)
T = n_samples / fs
times = create_times(T, 1 / fs)
# Load data
data = fi['data'].value
return fs, times, data[:, channel_num]
def poisson_oscillation(t=1,
t_onset=0.2,
n_cycles=10,
rate=6,
f=8,
phi=0,
n=10,
dt=1e-3,
min_rate=0.0,
name=None,
seed=None):
"""Simulate a Poisson population, oscillating"""
# Define time
times = fsutil.create_times(t, dt=dt)
# Create rate wave, over times
osc = (rate / 2 * (1 + np.sin((times * f * 2 * np.pi) + phi))) + min_rate
# Truncate it to a n_cycle burst, starting at t_onset
if np.isclose(f, 0.0):
osc[:] = min_rate
else:
burst_l = (1 / float(f)) * n_cycles
m = np.logical_not(
np.logical_and(times >= t_onset, times <= (t_onset + burst_l)))
osc[m] = min_rate
# Sample osc with N poisson 'cells'
nrns = neurons.Spikes(n, t, dt=dt, seed=seed)
ns, ts = fsutil.to_spiketimes(times, nrns.poisson(osc))
if name is not None:
write_spikes(name, ns, ts)
return None
else:
return ns, ts
# -------------------------------------------------------------------
if __name__ == "__main__":
args = docopt(__doc__, version='alpha')
name = args['NAME']
base_par1 = args['STIM']
par1_path = os.path.dirname(base_par1)
par1_name = os.path.splitext(os.path.basename(base_par1))[0]
threshold = float(args['-t'])
# -------------------------------------------------------------------
t = 0.8
dt = 1e-3
times = create_times(t, dt)
n_stim = 36 * 10 # 36 trials 10 per block in Mathewson
rates = np.linspace(1, 100, 10)
sigma = 10
loc = ['r_E', ]
hits = np.zeros((n_stim, len(rates)))
false_alarms = np.zeros((n_stim, len(rates)))
d_primes = np.zeros((n_stim, len(rates)))
for i, r in enumerate(rates):
# Load params
n_pop, pops, names, inputs, backs, conns = parse(base_par1)