PYthon Neural Analysis Package Pour Laboratoires d’Excellence
Project description
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: GNU General Public License v3
- Documentation: https://peyrachelab.github.io/pynapple
Getting Started
Requirements
- Python 3.6+
- Pandas 1.0.3+
- numpy 1.17+
- scipy 1.3+
- numba 0.46+
- tabulate
Installation
pynapple can be installed with pip:
$ pip install pynapple
or directly from the source code:
$ # clone the repository
$ git clone https://github.com/PeyracheLab/pynapple.git
$ cd pynapple
$ # Install in editable mode with `-e` or, equivalently, `--editable`
$ pip install -e .
Basic Usage
After installation, the package can imported:
$ python
>>> import pynapple as nap
An example of the package can be seen below. The exemple data can be found here.
import numpy as np
import pandas as pd
import pynapple as nap
from matplotlib.pyplot import *
data_directory = '/your/path/to/A2929-200711'
# LOADING DATA
data = nap.load_session(data_directory, 'neurosuite')
spikes = data.spikes
position = data.position
wake_ep = data.epochs['wake']
# COMPUTING TUNING CURVES
tuning_curves = nap.compute_1d_tuning_curves(group = spikes,
feature = position['ry'],
ep = position['ry'].time_support,
nb_bins = 120,
minmax=(0, 2*np.pi) )
# PLOT
figure()
for i in spikes:
subplot(6,7,i+1, projection = 'polar')
plot(tuning_curves[i])
show()
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 developped by Guillaume Viejo (https://github.com/gviejo) and other members of the Peyrache Lab.
Logo: Sofia Skromne Carrasco, 2021.
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