Perievent#
The perievent module allows to re-center time series and timestamps data around a particular event as well as computing events (spikes) trigger average.
Show code cell content
import pynapple as nap
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
custom_params = {"axes.spines.right": False, "axes.spines.top": False}
sns.set_theme(style="ticks", palette="colorblind", font_scale=1.5, rc=custom_params)
Peri-Event Time Histogram (PETH)#
stim = nap.Tsd(
t=np.sort(np.random.uniform(0, 1000, 50)),
d=np.random.rand(50), time_units="s"
)
ts1 = nap.Ts(t=np.sort(np.random.uniform(0, 1000, 2000)), time_units="s")
The function compute_perievent align timestamps to a particular set of timestamps.
peth = nap.compute_perievent(
data=ts1,
tref=stim,
minmax=(-0.1, 0.2),
time_unit="s")
print(peth)
Index rate ref_times
------- ------- -----------
0 3.33333 1.5293
1 nan 9.41442
2 nan 17.30314
3 nan 32.11224
4 10.0 41.87182
5 nan 148.64493
6 3.33333 165.73857
... ... ...
43 nan 865.12852
44 nan 901.04928
45 nan 920.91548
46 3.33333 940.85299
47 nan 943.82649
48 nan 973.80072
49 6.66667 993.12957
The returned object is a TsGroup. The column ref_times is a
metadata column that indicates the center timestamps.
Raster plot#
It is then easy to create a raster plot around the times of the
stimulation event by calling the to_tsd function of pynapple
to “flatten” the TsGroup peth.
plt.figure(figsize=(10, 6))
plt.subplot(211)
plt.plot(np.sum(peth.count(0.01), 1), linewidth=3, color="red")
plt.xlim(-0.1, 0.2)
plt.ylabel("Count")
plt.axvline(0.0)
plt.subplot(212)
plt.plot(peth.to_tsd(), "|", markersize=20, color="red", mew=4)
plt.xlabel("Time from stim (s)")
plt.ylabel("Stimulus")
plt.xlim(-0.1, 0.2)
plt.axvline(0.0)
<matplotlib.lines.Line2D at 0x7f12dfec64a0>
The same function can be applied to a group of neurons.
In this case, it returns a dict of TsGroup
Event trigger average#
The function compute_event_trigger_average compute the average feature around a particular event time.
Show code cell content
group = {
0: nap.Ts(t=np.sort(np.random.uniform(0, 100, 10))),
1: nap.Ts(t=np.sort(np.random.uniform(0, 100, 20))),
2: nap.Ts(t=np.sort(np.random.uniform(0, 100, 30))),
}
tsgroup = nap.TsGroup(group)
eta = nap.compute_event_trigger_average(
group=tsgroup,
feature=stim,
binsize=0.1,
windowsize=(-1, 1))
print(eta)
Time (s) 0 1 2
---------- ------- ------- -------
-1.0 0.36753 0.60177 0.64921
-0.9 0.36753 0.60177 0.64921
-0.8 0.36753 0.60177 0.64921
-0.7 0.36753 0.60177 0.64921
-0.6 0.36753 0.60177 0.64921
-0.5 0.38961 0.63925 0.64921
-0.4 0.38961 0.63925 0.64921
...
0.4 0.42958 0.64477 0.63194
0.5 0.42958 0.64477 0.63194
0.6 0.42958 0.64477 0.63194
0.7 0.42958 0.64477 0.63194
0.8 0.42958 0.64477 0.63194
0.9 0.42958 0.64477 0.63194
1.0 0.42958 0.64477 0.63574
dtype: float64, shape: (21, 3)
Peri-Event continuous time series#
The function nap.compute_perievent_continuous align a time series of any dimensions around events.
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features = nap.TsdFrame(t=np.arange(0, 100), d=np.random.randn(100,6))
events = nap.Ts(t=np.sort(np.random.uniform(0, 100, 5)))
perievent = nap.compute_perievent_continuous(
data=features,
tref=events,
minmax=(-1, 1))
print(perievent)
Time (s)
---------- -------------------------------
-1 [[0.117985 ... 1.343123] ...]
0 [[ 0.848519 ... -1.408934] ...]
1 [[-1.0103 ... -1.205193] ...]
dtype: float64, shape: (3, 5, 6)
The object perievent is now of shape (number of bins, (dimensions of input), number of events ) :
print(perievent.shape)
(3, 5, 6)