def sim_oscillation(n_seconds, fs, freq, cycle='sine', **cycle_params):
"""Simulate an oscillation.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Signal sampling rate, in Hz.
freq : float
Oscillation frequency.
cycle : {'sine', 'asine', 'sawtooth', 'gaussian', 'exp', '2exp'}
What type of oscillation cycle to simulate.
See `sim_cycle` for details on cycle types and parameters.
**cycle_params
Parameters for the simulated oscillation cycle.
Returns
-------
sig : 1d array
Simulated oscillation.
Examples
--------
Simulate a continuous oscillation at 5 hz:
>>> sig = sim_oscillation(n_seconds=1, fs=500, freq=5)
"""
# Figure out how many cycles are needed for the signal, & length of each cycle
n_cycles = int(np.ceil(n_seconds * freq))
n_seconds_cycle = int(np.ceil(fs / freq)) / fs
# Create oscillation by tiling a single cycle of the desired oscillation
osc_cycle = sim_cycle(n_seconds_cycle, fs, cycle, **cycle_params)
sig = np.tile(osc_cycle, n_cycles)
# Truncate the length of the signal to be the number of expected samples
n_samps = int(n_seconds * fs)
sig = sig[:n_samps]
return sig
def sim_bursty_oscillation(n_seconds,
fs,
freq,
enter_burst=.2,
leave_burst=.2,
cycle='sine',
**cycle_params):
"""Simulate a bursty oscillation.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
freq : float
Oscillation frequency, in Hz.
enter_burst : float, optional, default: 0.2
Probability of a cycle being oscillating given the last cycle is not oscillating.
leave_burst : float, optional, default: 0.2
Probability of a cycle not being oscillating given the last cycle is oscillating.
cycle : {'sine', 'asine', 'sawtooth', 'gaussian', 'exp', '2exp'}
What type of oscillation cycle to simulate.
See `sim_cycle` for details on cycle types and parameters.
**cycle_params
Parameters for the simulated oscillation cycle.
Returns
-------
sig : 1d array
Simulated bursty oscillation.
Notes
-----
This function takes a 'tiled' approach to simulating cycles, with evenly spaced
and consistent cycles across the whole signal, that are either oscillating or not.
If the cycle length does not fit evenly into the simulated data length,
then the last few samples will be non-oscillating.
Examples
--------
Simulate a bursty oscillation, with a low probability of bursting:
>>> sig = sim_bursty_oscillation(n_seconds=10, fs=500, freq=5, enter_burst=0.2, leave_burst=0.8)
Simulate a bursty oscillation, with a high probability of bursting:
>>> sig = sim_bursty_oscillation(n_seconds=10, fs=500, freq=15, enter_burst=0.8, leave_burst=0.4)
Simulate a bursty oscillation, of sawtooth waves:
>>> sig = sim_bursty_oscillation(n_seconds=10, fs=500, freq=10, cycle='sawtooth', width=0.3)
"""
# Determine number of samples & cycles
n_samples = int(n_seconds * fs)
n_seconds_cycle = (1 / freq * fs) / fs
# Make a single cycle of an oscillation
osc_cycle = sim_cycle(n_seconds_cycle, fs, cycle, **cycle_params)
n_samples_cycle = len(osc_cycle)
n_cycles = int(np.floor(n_samples / n_samples_cycle))
# Determine which periods will be oscillating
is_oscillating = _make_is_osc(n_cycles, enter_burst, leave_burst)
# Fill in the signal with cycle oscillations, for all bursting cycles
sig = np.zeros([n_samples])
for is_osc, cycle_ind in zip(is_oscillating,
range(0, n_samples, n_samples_cycle)):
if is_osc:
sig[cycle_ind:cycle_ind + n_samples_cycle] = osc_cycle
return sig