Overview

The core functionality of hsi-wizard includes:

  • DataCube Class: Manage, track, and process HSI data using a powerful object-oriented model

  • Spectral Visualization: Plot and explore spectral data through interactive and static visualizations

  • Clustering and Analytics: Apply clustering techniques and perform exploratory data analysis

  • File Format Support: Seamless integration with various formats, including .nrrd, .pickle, .csv, and .xlsx

For a detailed breakdown of each module, refer to the Modules section below.

Installation

You can install hsi-wizard via pip or compile it from source.

Install via pip

To install the latest release using pip:

pip install hsi-wizard

Install from Source

To install from source:

python -m pip install -U pip setuptools wheel             # Install/update build tools
git clone https://github.com/BlueSpacePotato/hsi-wizard   # Clone the repository
cd hsi-wizard                                             # Navigate into the project directory
python -m venv .venv                                      # Create a virtual environment
source .venv/bin/activate                                 # Activate the environment
pip install -e .                                          # Install in editable mode

Comparison with with Existing Tools

Most existing hyperspectral‐imaging packages—whether open‑source (e.g. PySptools, SPy, HyDe) or commercial (ENVI, Spectronon)—are aimed at geospatial tasks or GUI‑driven workflows, with limited scripting, batch automation or biomedical support.

In contrast, hsi‑wizard is an open‑source, Python‐native toolkit built for biomedical imaging. It handles ENVI, CSV, NRRD, TDMS and other formats; offers multimodal fusion, protocol logging and scriptable pipelines; and delivers fully reproducible, automated processing of diverse spectral datasets—bridging the gap between low‑level algorithm libraries and rigid GUI systems.

Comparison of Hyperspectral Imaging (HSI) Tools

Attribute

HSI-Wizard

PySptools

HyDe

Spectral Python (SPy)

ENVI

Category

Medical Imaging

Geospatial & Research Spectral Analysis

Preprocessing & Denoising

General-Purpose Data I/O & Basic Handling

Commercial All-rounder

Key Features

End-to-end medical pipeline, DataCube support, merging & analytics

Endmember extraction, spectral unmixing, wavelet & DNN denoising, energy-efficient implementations

Read/display/manipulate HSI files, BIP/BIL/BSQ formats, basic classification tools

Advanced spectral processing & classification, full GUI & Python API, ArcGIS integration

ENVI (.hdr/.img), GeoTIFF, HDF, ASCII – extensive commercial support

File Support

ENVI (.hdr/.img), images, CSV, FSM, NRRD, Pickle (.pkl), TDMS, XLSX

ENVI (.hdr/.img)

None built-in (requires user-defined NumPy loaders)

ENVI (.hdr/.img), BIL, BIP, BSQ

ENVI (.hdr/.img), GeoTIFF, HDF, ASCII – broad support

Licensing / Cost

Open-source (MIT)

Open-source (MIT)

Open-source (BSD-3-Clause)

Open-source (BSD-3)

Proprietary, paid

Geospatial limitation

This package does not support geospatial tracking or coordinate processing. If you require GIS integration or full remote-sensing capabilities, consider complementary tools.

Contributing

We welcome contributions from the medical imaging and bioinformatics communities! Please follow these guidelines to help streamline collaboration.

Reporting Issues

To report bugs, unexpected behavior, or feature requests:

  1. Search existing issues to check for duplicates

  2. Open a new issue with: - Clear title and description - Steps to reproduce the issue - Expected vs. actual behavior - Version details (hsi-wizard, Python, OS)

  3. Add appropriate labels (e.g., bug, enhancement, documentation)

Getting Help

For help with examples, usage questions, or general discussion:

  • GitHub Discussions: Post under the Support category in our Discussions board.

Contributing Code

If you want to contribute code:

  1. Fork the repository and create a new branch

  2. Make your changes with clear commits

  3. Push your branch and open a Pull Request against main

  4. We’ll review your contribution and provide feedback or merge