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(
data=ts1,
tref=stim,
minmax=(-0.1, 0.2),
time_unit="s")
print(peth)
Index rate ref_times
------- ------- -------------
0 nan 2.8213517
1 nan 32.884225312
2 nan 136.909498055
3 nan 185.216290848
4 nan 189.768584365
5 nan 205.766833585
6 nan 209.624400063
... ... ...
43 nan 826.585931456
44 nan 864.462282199
45 3.33333 904.594369054
46 nan 930.850262099
47 nan 988.181206168
48 nan 995.397525743
49 3.33333 995.452207077
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 0x7f5b13a7ac60>
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 0.3729 0.40108 0.20938
-0.9 0.3729 0.40108 0.20938
-0.8 0.3729 0.40108 0.20938
-0.7 0.3729 0.40108 0.20938
-0.6 0.3729 0.40108 0.20938
-0.5 0.3729 0.40108 0.20938
-0.4 0.3729 0.40108 0.20938
... ... ... ...
0.4 0.45843 0.36422 0.20938
0.5 0.45843 0.36422 0.18603
0.6 0.45843 0.36422 0.18603
0.7 0.45843 0.36422 0.18603
0.8 0.45843 0.36422 0.18603
0.9 0.45843 0.36422 0.18603
1.0 0.45843 0.36422 0.18603
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 [[0.524134 ... 0.590647] ...]
0 [[1.211455 ... 1.514878] ...]
1 [[-0.878432 ... -0.908275] ...]
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)