🌈 Spectral Phasors Analysis 📈

A Python application for spectral phasor analysis built with Panel 🎨 and HoloViews 📊. This tool allows you to explore spectral phasors through simulations (single/multi‑Gaussian) and analyze real experimental data via CSV spectra or TIFF hyperspectral stacks. 🧪

✨ Features

• 🧪 Simulations: Interactive single and multi‑component Gaussian simulations with optional noise.
• 📄 Spectra Analysis: Import CSV files containing spectral data.
• 🖼️ Image Analysis: Import TIFF hyperspectral stacks for pixel‑wise phasor analysis with bidirectional selection (Image ↔ Phasor).

🌐 Quick Preview

You can get a quick preview of the application on GitHub Pages 👀

But we recommend running a full local version for the best experience and complete functionality! 🚀

🛠️ Prerequisites

The project uses uv ⚡ for fast Python package management. Ensure it is installed on your system.

🚀 Installation

# 📂 Clone the repository
git clone https://github.com/DBP008/spectral-phasors-analysis.git
cd spectral-phasors-analysis

# 📦 Install dependencies and run the app
uv sync

🏃 Running the Application

uv run phasor_panel.py

The application will launch automatically in your default browser 🌐 (usually at http://localhost:5900).

🛠️ Usage Overview

• 🌍 Global Parameters: Set the wavelength range and harmonic number.
• 📑 Tabs:
  • 1️⃣ Tab 1 – Single Gaussian simulation.
  • 2️⃣ Tab 2 – Multi‑Gaussian simulation with optional noise.
  • 3️⃣ Tab 3 – Upload a CSV file to visualise experimental spectra.
  • 4️⃣ Tab 4 – Upload a TIFF stack for image‑based phasor analysis.
  • 5️⃣ Tab 5 – Two‑component phasor deconvolution.
  • 6️⃣ Tab 6 – Three‑component phasor deconvolution.
• 🖱️ Interactive Plots: Click or box‑select points on the phasor plot to view corresponding spectra, and vice‑versa.

📂 Sample Data

Sample datasets are provided in the data/ folder to help you get started:

• Denaturation.csv: Example fluorescence spectra data acquired with a Cary Eclipse Fluorescence Spectrometer 🧬.
• .tif files: Example hyperspectral stacks (lambda‑xy scans) acquired with a Leica confocal microscope 🔬.