RoiBaView

RoiBaView is an interactive Python tool for plotting, analyzing, and exploring ROI-based signals from experiments like Calcium Imaging and Biomedical Data Acquisition.
Visualize, detect peaks, sync data with videos, and manage your datasets with an intuitive graphical interface.

Try RoiBaView Online (Docs & Demo)
Install from PyPI

Features

• Import and visualize ROI-based time-series data (from CSV files)
• Peak detection with adjustable parameters and live preview
• Video viewer for syncing recorded videos (or TIFF stacks) with data traces
• CSV file format converter (handle different separators)
• Built-in video converter powered by ffmpeg
• Easy-to-use interface for data management and export
• Designed for experiments like calcium imaging, electrophysiology, and more

Installation

1. Create a new environment (e.g., using Anaconda):

   conda create -n roibaview
   

2. Activate the environment and ensure pip is installed:

   conda activate roibaview
   conda install pip
   

3. Install RoiBaView via PyPI:

   pip install roibaview
   

Importing Data

You can import data from CSV files:

• Go to File → Import CSV file...
• Supported structure:

• Specify the sampling rate (Hz) when prompted.
• If the dataset is global (same trace across ROIs), check the Global Dataset option.

Note: Only CSVs with comma (,) separator are supported.
Need to convert from another separator? Use:
Tools → Convert CSV files

Plotting and Data Analysis

• After importing, your datasets appear on the left sidebar.
• Left-click to activate a dataset.
• Right-click for more options (rename, modify, etc.)

Peak Detection

• Activate a dataset, then click Detect Peaks.
• Customize detection parameters and watch live detection updates.
• Export detected peak information to CSV.

Video Viewer & Video Converter

• Open the Video Viewer under Tools → Open Video Viewer.
• Connect video frames to ROI data for synchronized viewing.
• Video Converter (Tools → Open Video Converter) lets you:
  • Convert between formats using FFmpeg (you must have it installed).

Download FFmpeg from https://ffmpeg.org/.

Documentation & Links

• PyPI Project Page
• Live Documentation + Website
• Source Code on GitHub

Author

Developed by Nils Brehm (2025)
License: MIT License

Keywords: Python ROI viewer, Calcium Imaging Visualization, Biomedical Signal Viewer, Interactive Data Analysis, Open-Source Visualization