from .utils import _isi
from .binning import binned_spiketrain
import numpy as np
from numpy.random import random
[docs]def shuffled_isi_spiketrain(spiketrain: np.ndarray):
"""
Return a surrogate spiketrain with shuffled inter spike intervals.
Args:
spiketrain: The parent array spiketrain from which to create the surrogate
Returns:
A numpy array spiketrain containing spiketimes in seconds
"""
spiketrain = np.copy(spiketrain)
t_start = np.min(spiketrain)
isi: np.ndarray = _isi(spiketrain)
np.random.shuffle(isi)
return np.cumsum(isi) + t_start
[docs]def shuffled_isi_spiketrains(spiketrain: np.ndarray, n: int = 1):
"""
Return n surrogate spiketrains with shuffled inter spike intervals.
Args:
spiketrain: The parent array spiketrain from which to create the surrogate
n: The number of surrogate spiketrains to return
Returns:
A list of numpy array spiketrain containing spiketimes in seconds
"""
return [shuffled_isi_spiketrain(spiketrain) for _ in range(n)]
[docs]def jitter_spiketrain(
spiketrain: np.ndarray,
jitter_window_size: float,
t_start: float = None,
t_stop: float = None,
):
"""
Return a jitter spiketrain surrogate from a parent spiketrain.
Jitter spiketrains contain similar firing rate dynamics to the parent spiketrain but with spiketimes
randomised. This is done by binning spikecounts over time and then randomising spiketimes within
time bins.
Args:
spiketrain: The parent numpy spiketrain.
jitter_window_size: The size of the timebins in seconds
t_start: If specified, spikes before this limit will be discarded
t_stop: If specified, spikes after this limit will be discarded
Returns:
A numpy array spiketrain containing spiketimes in seconds
"""
if t_start is None:
t_start = spiketrain[0]
if t_stop is None:
t_stop = spiketrain[-1]
edges, values = binned_spiketrain(
spiketrain, fs=(1 / jitter_window_size), t_start=t_start, t_stop=t_stop
)
jittered = np.sort(
np.concatenate(
[
edge + (random(val) * jitter_window_size)
for edge, val in zip(edges, values)
if val != 0
]
)
)
return jittered[jittered < t_stop]
[docs]def jitter_spiketrains(
spiketrain: np.ndarray,
n: int,
jitter_window_size: float,
t_start: float = None,
t_stop: float = None,
):
"""
Return a jitter spiketrain surrogate from a parent spiketrain.
Jitter spiketrains contain similar firing rate dynamics to the parent spiketrain but with spiketimes
randomised. This is done by binning spikecounts over time and then randomising spiketimes within
time bins.
Args:
spiketrain: The parent numpy spiketrain.
jitter_window_size: The size of the timebins in seconds
t_start: If specified, spikes before this limit will be discarded
t_stop: If specified, spikes after this limit will be discarded
Returns:
A list of numpy array spiketrain containing spiketimes in seconds
"""
if t_start is None:
t_start = spiketrain[0]
if t_stop is None:
t_stop = spiketrain[-1]
return [
jitter_spiketrain(
spiketrain,
jitter_window_size=jitter_window_size,
t_start=t_start,
t_stop=t_stop,
)
for _ in range(n)
]