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 nan 0.766272919
1 3.33333 21.468630865
2 nan 53.314449709
3 3.33333 64.835773482
4 3.33333 66.29494006
5 3.33333 73.384913702
6 nan 122.16215032
... ... ...
43 nan 838.716955634
44 nan 861.978416405
45 nan 873.17284197
46 nan 876.243890603
47 3.33333 911.70960862
48 6.66667 978.832354393
49 nan 994.857494195
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 0x7f59eb9c3980>
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.
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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 0.45653 0.44195 0.56859
-0.9 0.45653 0.44195 0.56859
-0.8 0.45653 0.44195 0.56859
-0.7 0.45653 0.44195 0.56859
-0.6 0.45653 0.44195 0.56859
-0.5 0.45653 0.45104 0.56859
-0.4 0.45653 0.45104 0.58551
... ... ... ...
0.4 0.45653 0.46013 0.58551
0.5 0.45653 0.46013 0.58551
0.6 0.45653 0.46013 0.58551
0.7 0.45653 0.46013 0.58551
0.8 0.45653 0.46013 0.58551
0.9 0.45653 0.46013 0.58551
1.0 0.45653 0.46013 0.58551
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 [[-0.534289 ... 1.687164] ...]
0 [[0.77695 ... 0.554556] ...]
1 [[-0.548893 ... -2.4883 ] ...]
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)