nwb-video-widgets 0.1.1

Interactive Jupyter widgets for NWB video and pose visualization

nwb-video-widgets

Interactive Jupyter widgets for NWB video and pose estimation visualization. Built with anywidget for compatibility across JupyterLab, Jupyter Notebook, VS Code, and Google Colab.

Table of Contents

• Installation
• Video Player Widgets
• Pose Estimation Widgets

Installation

For local only NWB file usage:

pip install nwb-video-widgets

For DANDI integration and streaming support:

pip install nwb-video-widgets[dandi]

Video Player Widgets

Multi-camera synchronized video player with configurable layout (Row, Column, or Grid).

Features:

• Interactive settings panel for video selection
• Multiple layout modes (Row, Column, Grid)
• Synchronized playback across all videos
• Session time display with NWB timestamps

DANDI Streaming

Use NWBDANDIVideoPlayer for videos hosted on DANDI:

from dandi.dandiapi import DandiAPIClient
from nwb_video_widgets import NWBDANDIVideoPlayer

client = DandiAPIClient()
dandiset = client.get_dandiset("000409", "draft")
asset = dandiset.get_asset_by_path("sub-NYU-39/sub-NYU-39_ses-..._behavior.nwb")

widget = NWBDANDIVideoPlayer(asset=asset)
widget

Local Files

Use NWBLocalVideoPlayer for local NWB files:

from pynwb import read_nwb
from nwb_video_widgets import NWBLocalVideoPlayer

nwbfile = read_nwb("experiment.nwb")
widget = NWBLocalVideoPlayer(nwbfile)
widget

Fixed Grid Layout

When you know exactly which videos you want to display and how to arrange them, use the video_grid parameter to bypass the interactive settings panel. This is useful for:

• Reproducible notebooks where you want consistent output
• Presentations or demos with predetermined layouts
• Embedding widgets in dashboards or reports

The video_grid parameter accepts a 2D list where each inner list represents a row of videos:

# Single row of three cameras
widget = NWBLocalVideoPlayer(
    nwbfile,
    video_grid=[["VideoLeftCamera", "VideoBodyCamera", "VideoRightCamera"]]
)

# 2x2 grid layout
widget = NWBLocalVideoPlayer(
    nwbfile,
    video_grid=[
        ["VideoLeftCamera", "VideoRightCamera"],
        ["VideoBodyCamera", "VideoTopCamera"],
    ]
)

# Asymmetric grid (2 videos on top, 1 on bottom)
widget = NWBLocalVideoPlayer(
    nwbfile,
    video_grid=[
        ["VideoLeftCamera", "VideoRightCamera"],
        ["VideoBodyCamera"],
    ]
)

The same parameter works with NWBDANDIVideoPlayer:

widget = NWBDANDIVideoPlayer(
    asset=asset,
    video_grid=[["VideoLeftCamera", "VideoRightCamera"]]
)

Video names that don't exist in the NWB file are silently skipped.

Custom Video Labels

By default, the video name from the NWB file is displayed under each video. Use the video_labels parameter to provide custom display names:

widget = NWBLocalVideoPlayer(
    nwbfile,
    video_grid=[["VideoLeftCamera", "VideoRightCamera"]],
    video_labels={
        "VideoLeftCamera": "Left",
        "VideoRightCamera": "Right",
    }
)

Videos not in the dictionary will display their original name.

---

Pose Estimation Widgets

Overlays DeepLabCut keypoints on streaming video with support for camera selection.

Features:

• Camera selection via settings panel
• Keypoint visibility toggles (All/None/individual)
• Label display toggle
• Session time display (NWB timestamps)
• Custom keypoint colors via colormap or explicit hex values
• Supports split files (videos in raw file, pose in processed file)

DANDI Streaming

Use NWBDANDIPoseEstimationWidget for DANDI-hosted files:

from dandi.dandiapi import DandiAPIClient
from nwb_video_widgets import NWBDANDIPoseEstimationWidget

client = DandiAPIClient()
dandiset = client.get_dandiset("000409", "draft")

# Single file (videos + pose in same file)
asset = dandiset.get_asset_by_path("sub-.../sub-..._combined.nwb")
widget = NWBDANDIPoseEstimationWidget(asset=asset)

# Or split files (videos in raw, pose in processed)
raw_asset = dandiset.get_asset_by_path("sub-.../sub-..._desc-raw.nwb")
processed_asset = dandiset.get_asset_by_path("sub-.../sub-..._desc-processed.nwb")
widget = NWBDANDIPoseEstimationWidget(
    asset=processed_asset,
    video_asset=raw_asset,
)
widget

Local Files

Use NWBLocalPoseEstimationWidget for local NWB files:

from pynwb import read_nwb
from nwb_video_widgets import NWBLocalPoseEstimationWidget

# Single file
nwbfile = read_nwb("experiment.nwb")
widget = NWBLocalPoseEstimationWidget(nwbfile)
widget

# Or split files
nwbfile_raw = read_nwb("raw.nwb")
nwbfile_processed = read_nwb("processed.nwb")
widget = NWBLocalPoseEstimationWidget(
    nwbfile=nwbfile_processed,
    video_nwbfile=nwbfile_raw,
)
widget

Parameters:

Parameter	Type	Description
keypoint_colors	str or dict	Matplotlib colormap name (e.g., 'tab10') or dict mapping keypoint names to hex colors
default_camera	str	Camera to display initially