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
- Notebooks and tutorials : https://peyrachelab.github.io/pynapple/notebooks/pynapple-quick-start/
- Collaborative repository: https://github.com/PeyracheLab/pynacollada
The best way to install pynapple is with pip within a new conda environment :
$ conda create --name pynapple pip python=3.8 $ conda activate pynapple $ pip install pynapple
or directly from the source code:
$ conda create --name pynapple pip python=3.8 $ conda activate pynapple $ # 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 .
Note The package is now using a pyproject.toml file for installation and dependencies management. If you want to run the tests, use pip install -e .[dev]
This procedure will install all the dependencies including
- pynwb 2.0
For spyder users, it is recommended to install spyder after installing pynapple with :
$ conda create --name pynapple pip python=3.8 $ conda activate pynapple $ pip install pynapple $ pip install spyder $ spyder
Warning note for Windows users: on a multi-user Windows, make sure you open the conda prompt with administrative access:
run as administrator; otherwise directory paths for some dependencies may be missing from the PYTHONPATH environment variable. The most common is the error in importing PyQt5. In case of such errors, right click on your conda prompt and select
run as administrator, activate your pynapple environment, and install the said package again (e.g. pip install PyQt) so that the paths are properly saved by Windows.
After installation, the package can imported:
$ python >>> import pynapple as nap
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()
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.
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.