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 nan 0.161590504
1 nan 86.624171184
2 nan 103.976190647
3 nan 126.745468155
4 3.33333 128.023536643
5 nan 133.70390886
6 nan 141.592689539
... ... ...
43 3.33333 887.266545542
44 3.33333 888.350082208
45 nan 920.759651573
46 nan 925.641137716
47 nan 930.965877212
48 nan 932.383088088
49 6.66667 947.37014258
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 0x7f8c28f755b0>
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 0.52754 0.44763 0.47996
-0.9 0.52754 0.44763 0.47996
-0.8 0.52754 0.44763 0.47996
-0.7 0.52754 0.44763 0.47996
-0.6 0.52754 0.44763 0.47996
-0.5 0.52754 0.44763 0.47996
-0.4 0.52754 0.44763 0.47996
... ... ... ...
0.4 0.52754 0.45617 0.47996
0.5 0.52754 0.45617 0.47996
0.6 0.52754 0.45617 0.47996
0.7 0.52754 0.45617 0.47996
0.8 0.52754 0.45617 0.47996
0.9 0.52754 0.45617 0.47996
1.0 0.52754 0.45617 0.47996
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 [[-0.784254 ... 0.99717 ] ...]
0 [[0.02879 ... 0.589427] ...]
1 [[ 0.133747 ... -0.946685] ...]
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)