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  9.98
1        nan      28.06
2        nan      37.86
3        nan      84.67
4        6.66667  84.95
5        nan      105.15
6        nan      137.35
...      ...      ...
43       nan      913.85
44       nan      924.17
45       nan      927.07
46       3.33333  927.48
47       3.33333  930.75
48       nan      938.21
49       nan      986.77

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 0x7fc755340ce0>

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        0.635302  0.53789   0.531024
-0.9        0.635302  0.53789   0.525155
-0.8        0.635302  0.53789   0.525155
-0.7        0.635302  0.598673  0.525155
-0.6        0.635302  0.598673  0.530617
-0.5        0.635302  0.598673  0.530617
-0.4        0.635302  0.598673  0.530617
...
0.4         0.635302  0.590042  0.530617
0.5         0.635302  0.598075  0.530617
0.6         0.635302  0.598075  0.530617
0.7         0.635302  0.598075  0.513559
0.8         0.635302  0.598075  0.513559
0.9         0.635302  0.598075  0.513559
1.0         0.635302  0.598075  0.513559
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          [[-0.897378 ... -0.29746 ] ...]
0           [[-0.35805  ...  0.302188] ...]
1           [[ 1.295916 ... -0.714257] ...]
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)