Pynaviz

Python Neural Analysis Visualization

Pynaviz provides interactive, high-performance visualizations designed to work seamlessly with Pynapple time series and video data. It allows synchronized exploration of neural signals and behavioral recordings. It is built on top of pygfx, a modern GPU-based rendering engine.

Installation

We recommend using the Qt-based interface for the best interactive experience:

pip install pynaviz[qt]

If Qt is not available on your system, you can still use the fallback rendering engine (via PyGFX):

pip install pynaviz

Quick start

From the command line

$ pynaviz [files ...] [-l layout.json] [-f FORMAT]

From a Python script

from pynaviz import scope

The scope function accepts many input types:

See the User Guide for more details.

Keyboard shortcuts

Global

Per-dock (active when the mouse is over the canvas)

Basic usage

This example demonstrates how to create some example time series and launch the visualization GUI:

import pynapple as nap
import numpy as np
from pynaviz import scope

# Create some example time series
tsd = nap.Tsd(t=np.arange(100), d=np.random.randn(100))

# Create a TsdFrame with metadata
tsdframe = nap.TsdFrame(
    t=np.arange(10000),
    d=np.random.randn(10000, 10),
    metadata={"label": np.random.randn(10)}
)

# Launch the visualization GUI
scope(globals())

This will launch an interactive viewer where you can inspect time series, event data, and video tracks in a synchronized environment.