Skip to content

Quickstart

New releases 🔥

pynapple >= 0.7

Pynapple now implements signal processing. For example, to filter a 1250 Hz sampled time series between 10 Hz and 20 Hz:

nap.apply_bandpass_filter(signal, (10, 20), fs=1250)
New functions includes power spectral density and Morlet wavelet decomposition. See the documentation for more details.

pynapple >= 0.6

Starting with 0.6, IntervalSet objects are behaving as immutable numpy ndarray. Before 0.6, you could select an interval within an IntervalSet object with:

new_intervalset = intervalset.loc[[0]] # Selecting first interval

With pynapple>=0.6, the slicing is similar to numpy and it returns an IntervalSet

new_intervalset = intervalset[0]

pynapple >= 0.4

Starting with 0.4, pynapple rely on the numpy array container approach instead of Pandas for the time series. Pynapple builtin functions will remain the same except for functions inherited from Pandas.

This allows for a better handling of returned objects.

Additionaly, it is now possible to define time series objects with more than 2 dimensions with TsdTensor. You can also look at this notebook for a demonstration of numpy compatibilities.

Basic Usage

After installation, you can now import the package: 

$ python
>>> import pynapple as nap

You'll find an example of the package below. Click here to download the example dataset. The folder includes a NWB file containing the data.

import matplotlib.pyplot as plt
import numpy as np

import pynapple as nap

# LOADING DATA FROM NWB
data = nap.load_file("A2929-200711.nwb")

spikes = data["units"]
head_direction = data["ry"]
wake_ep = data["position_time_support"]

# COMPUTING TUNING CURVES
tuning_curves = nap.compute_1d_tuning_curves(
    spikes, head_direction, 120, minmax=(0, 2 * np.pi)
)


# PLOT
plt.figure()
for i in spikes:
    plt.subplot(3, 5, i + 1, projection="polar")
    plt.plot(tuning_curves[i])
    plt.xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2])

plt.show()
Shown below, the final figure from the example code displays the firing rate of 15 neurons as a function of the direction of the head of the animal in the horizontal plane.

pic1