pynaviz 0.2.0

Life made easier.

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.

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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]

Example	Argument	Notes
$ pynaviz	(no arguments)	Opens an empty viewer
$ pynaviz data.nwb	files	One or more .nwb files; objects unpacked individually
$ pynaviz data.npz	files	One or more .npz files; must contain a single pynapple object each
$ pynaviz recording.mp4	files	One or more video files (.mp4, .avi, .mov, .mkv)
$ pynaviz data.nwb recording.mp4	files	Multiple files of different types can be mixed
$ pynaviz recording.plx	files	Ephys file; format auto-detected via nap.EphysReader
$ pynaviz rec/	files	Directory; auto-detected as NeuroScopeIO if .dat + .xml are present
$ pynaviz rec/ -f NeuroScopeIO	files + -f	Directory with explicit Neo IO format
$ pynaviz recording.plx -f PlexonIO	files + -f	Ephys file with explicit format
$ pynaviz -l layout.json	-l / --layout	Restore a previously saved layout (.json)
$ pynaviz data.nwb -l layout.json	files + -l	Load files and restore layout simultaneously

From a Python script

from pynaviz import scope

The scope function accepts many input types:

Example	Input type	Notes
scope({"lfp": tsdframe, "spikes": tsgroup})	dict	Keys become display names in the variable panel
scope([tsdframe, tsgroup, interval_set])	list / tuple	Names inferred from class (TsdFrame, TsGroup, …)
scope(tsgroup)	nap.TsGroup	Collection of spike trains. Same for all pynapple objects (Tsd, TsdFrame, …)
scope(nap.load_file("data.nwb"))	nap.NWBFile	All contained objects unpacked individually
scope(nap.EphysReader("rec/", format="NeuroScopeIO"))	nap.EphysReader	All contained objects unpacked individually
scope("data.nwb")	str / pathlib.Path — .nwb	Loaded via pynapple, objects unpacked
scope("data.npz")	str / pathlib.Path — .npz	Must contain a single pynapple object
scope("recording.mp4")	str / pathlib.Path — video	.mp4, .avi, .mov, .mkv supported
scope("recording.plx")	str / pathlib.Path — ephys file	Loaded via nap.EphysReader; format auto-detected
scope("rec/")	str / pathlib.Path — directory	Directory passed to nap.EphysReader; auto-detects NeuroScopeIO

See the User Guide for more details.

Keyboard shortcuts

Global

Shortcut	Action
Space	Play / pause
Ctrl+S	Save layout
Ctrl+O	Load layout

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

Shortcut	Action
r	Reset view
← / →	Pan left / right by one page
y	Lock / unlock y-axis
x	Lock / unlock x-axis
Ctrl+← / Ctrl+→	Jump to previous / next superposed epoch (requires an IntervalSet overlay)
i / d	Increase / decrease contrast (TsdFrame) or marker size (TsGroup)
n / p	Jump to next / previous interval or timestamp (IntervalSet & Ts)

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.