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   0             170.09
      1   6.66667       182.33
      2   0             186.04
      3   6.66667       203.57
      4   6.66667       288.25
      5   0             289.45
      6   3.33333       293.42
      7   0             296.44
      8   3.33333       320.43
      9   3.33333       362.05
     10   0             412.22
     11   3.33333       429.92
     12   3.33333       461.02
     13   3.33333       465.8
     14   0             487.93
     15   0             495.72
     16   3.33333       496.27
     17   3.33333       499.53
     18   0             524.52
     19   0             529.15
     20   0             545.65
     21   6.66667       549.85
     22   0             590.68
     23   0             602.23
     24   3.33333       645.86
     25   0             659.97
     26   0             664.74
     27   0             665.01
     28   0             671.03
     29   3.33333       675.13
     30   0             689.31
     31   3.33333       714.83
     32   6.66667       725.76
     33   0             744.26
     34   0             750.22
     35  10             768.97
     36   0             787.15
     37   3.33333       788
     38   0             795.03
     39   3.33333       829.2
     40   0             832.31
     41   0             836.51
     42   0             859.59
     43   3.33333       911.36
     44   3.33333       922.04
     45   0             922.74
     46   3.33333       927.94
     47   3.33333       930.27
     48   0             931.9
     49   0             973.17

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 0x7fb402c2e990>

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    0    0
-0.9          0    0    0
-0.8          0    0    0
-0.7          0    0    0
-0.6          0    0    0
-0.5          0    0    0
-0.4          0    0    0
...
0.4           0    0    0
0.5           0    0    0
0.6           0    0    0
0.7           0    0    0
0.8           0    0    0
0.9           0    0    0
1.0           0    0    0
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          [[-0.061692 ... -0.178024] ...]
0           [[-0.436613 ... -0.466229] ...]
1           [[-0.765981 ...  1.281521] ...]
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)