pynapple 0.11.4

PYthon Neural Analysis Package.

pynapple is a light-weight python library for neurophysiological data analysis. The goal is to offer a versatile set of tools to study typical data in the field, i.e. time series (spike times, behavioral events, etc.) and time intervals (trials, brain states, etc.). It also provides users with generic functions for neuroscience such as tuning curves and cross-correlograms.

• Free software: MIT License
• Documentation: https://pynapple.org

Note :page_with_curl: If you are using pynapple, please cite the following paper

Learning pynapple

Workshops are regularly organized by the center for Computational Neuroscience of the Flatiron institute to teach pynapple & NeMos to new users.

The next workshop will take place in NYC in 2027.

New release :fire:

pynapple >= 0.10.0

Tuning curves computation have been generalized to n-dimensions with the function compute_tuning_curves. It can now return a xarray DataArray instead of a Pandas DataFrame.

pynapple >= 0.8.2

The objects IntervalSet, TsdFrame and TsGroup inherits a new metadata class. It is now possible to add labels for each interval of an IntervalSet, each column of a TsdFrame and each unit of a TsGroup.

See the documentation for more details

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.

Community

To ask any questions or get support for using pynapple, please consider joining our slack. Please send an email to thepynapple[at]gmail[dot]com to receive an invitation link.

Using pynapple with Claude Code

If you write pynapple code with Claude Code, the pynapple skill teaches it the library's idioms, so it reaches for restrict, IntervalSet and compute_tuning_curves instead of falling back on pandas-style loops:

/plugin marketplace add pynapple-org/claude-skills
/plugin install using-pynapple@pynapple-skills
/reload-plugins

Claude invokes the skill automatically once it detects pynapple code.

Getting Started

Installation

The best way to install pynapple is with pip inside a new conda environment:

$ conda create --name pynapple pip python=3.11
$ conda activate pynapple
$ pip install pynapple

Running pip install pynapple will install all the dependencies, including:

• pandas
• numpy
• scipy
• numba
• pynwb 2.0
• tabulate
• h5py
• xarray

For development, see the contributor guide for steps to install from source code.

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_tuning_curves(
    spikes, head_direction, 120, epochs=wake_ep, range=(0, 2 * np.pi)
)

# PLOT
g=tuning_curves.plot(
    row="unit", 
    col_wrap=5, 
    subplot_kws={"projection": "polar"}, 
    sharey=False
)
plt.xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2])
g.set_titles("")
g.set_xlabels("")
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.

Credits

Special thanks to Francesco P. Battaglia (https://github.com/fpbattaglia) for the development of the original TSToolbox (https://github.com/PeyracheLab/TStoolbox) and neuroseries (https://github.com/NeuroNetMem/neuroseries) packages, the latter constituting the core of pynapple.

This package was developed by Guillaume Viejo (https://github.com/gviejo) and other members of the Peyrache Lab.

Contributing

We welcome contributions, including documentation improvements. For more information, see the contributor guide.