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.
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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(
timestamps=ts1,
tref=stim,
minmax=(-0.1, 0.2),
time_unit="s")
print(peth)
Index rate ref_times
------- ------- -----------
0 3.33333 53.02
1 3.33333 81.94
2 nan 115.7
3 3.33333 123.9
4 10.0 124.72
5 nan 162.86
6 nan 164.9
... ... ...
43 nan 893.58
44 nan 912.37
45 3.33333 946.54
46 3.33333 979.1
47 6.66667 983.96
48 3.33333 989.6
49 3.33333 990.35
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.mean(peth.count(0.01), 1) / 0.01, linewidth=3, color="red")
plt.xlim(-0.1, 0.2)
plt.ylabel("Rate (spikes/sec)")
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 0x7fdc36f3b440>
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 0.13011 0.35044 0.29868
-0.9 0.13011 0.35044 0.29868
-0.8 0.13011 0.35044 0.29868
-0.7 0.13011 0.35044 0.29868
-0.6 0.13011 0.35044 0.29868
-0.5 0.13011 0.35044 0.29868
-0.4 0.13011 0.35044 0.29868
... ... ... ...
0.4 0.19517 0.35044 0.29868
0.5 0.19517 0.35044 0.29868
0.6 0.19517 0.35044 0.32456
0.7 0.19517 0.35044 0.32456
0.8 0.19517 0.35044 0.32456
0.9 0.19517 0.35044 0.32456
1.0 0.19517 0.35044 0.32456
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(
timeseries=features,
tref=events,
minmax=(-1, 1))
print(perievent)
Time (s)
---------- -------------------------------
-1 [[0.669094 ... 1.075644] ...]
0 [[ 0.228045 ... -0.536211] ...]
1 [[ 0.663527 ... -0.252951] ...]
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)