DIRAS+

DIRAS+ (Dynamic Iterative Reweighted Autoregressive Spectral baseline correction algorithm) is an advanced baseline correction pipeline for Raman spectra that combines the original DIRAS algorithm with machine learning-based parameter optimization.

Overview

DIRAS+ extends the original DIRAS algorithm by using a deep learning encoder and XGBoost regressor to automatically predict optimal regularization parameters for each spectrum, eliminating the need for manual parameter tuning.

Installation

Prerequisites

• Python 3.8 or higher
• pip or conda package manager

Quick Installation

# Clone the repository
git clone https://github.com/Aradhye-sys/DIRAS_Plus.git
cd DIRAS_Plus

# Install dependencies
pip install -r requirements.txt

Alternative: Conda Installation

# Create conda environment (if environment.yml exists)
conda env create -f environment.yml
conda activate diras-plus

# Or install manually
conda install numpy scipy pandas matplotlib xgboost tensorflow joblib

Dependencies

• numpy - Numerical computing
• scipy - Scientific computing and sparse matrices
• pandas - Data manipulation
• matplotlib - Plotting and visualization
• xgboost - Gradient boosting for parameter prediction
• tensorflow>=2.12 - Deep learning framework
• joblib - Model serialization

Quick Start

Basic Usage

import numpy as np
import pandas as pd
from src.DIRAS import DIRAS
from src.DIRAS_plus import diras_plus_xgb

# Load your spectral data
# First column: wavenumbers, remaining columns: spectral intensities
df = pd.read_csv("your_spectra.csv")
wavenumbers = df.iloc[:, 0].to_numpy()
spectra = df.iloc[:, 1:].to_numpy()

# Apply DIRAS+ with automatic parameter prediction
lam_pred, baseline, corrected = diras_plus_xgb(
    wavenumber=wavenumbers,
    spectra=spectra,
    diras_fn=DIRAS,
    encoder_path="models/encoder.keras",
    xgb_path="models/XGBoost.joblib"
)

# Access results
print(f"Predicted lambda values: {lam_pred}")
print(f"Baseline shape: {baseline.shape}")
print(f"Corrected spectra shape: {corrected.shape}")

Demo Script

Run the included demo to see DIRAS+ in action:

python demo_run.py

This will:

1. Load synthetic spectral data
2. Apply DIRAS with fixed parameters
3. Apply DIRAS+ with ML-predicted parameters
4. Display comparison plots

Project Structure

DIRAS_Plus/
├── data/
│   └── Synthetic_spectra.csv    # Example spectral data
├── models/
│   ├── encoder.keras            # Pre-trained encoder model
│   └── XGBoost.joblib          # Pre-trained XGBoost model
├── src/
│   ├── DIRAS.py                # Original DIRAS algorithm
│   ├── DIRAS_plus.py           # DIRAS+ with ML enhancement
│   └── CITATION.cff            # Citation information
├── demo_run.py                 # Demo script
├── requirements.txt            # Python dependencies
└── README.md                   # This file

API Reference

DIRAS(y, lam=1e6, ar_order=50, omega=0.05, zeta=2, ratio=1e-6, eps=1e-6)

Original DIRAS baseline correction algorithm.

Parameters:

• y: Input signal array
• lam: Regularization parameter
• ar_order: Autoregressive model order
• omega: Weight for positive residuals
• zeta: Weight for negative residuals
• ratio: Convergence threshold
• eps: Numerical stability constant

Returns:

• b: Estimated baseline array

diras_plus_xgb(wavenumber, spectra, diras_fn, encoder_path, xgb_path, **kwargs)

DIRAS+ algorithm with ML-based parameter prediction.

Parameters:

• wavenumber: Wavenumber axis array
• spectra: Spectral data matrix
• diras_fn: DIRAS function reference
• encoder_path: Path to encoder model
• xgb_path: Path to XGBoost model
• pre_lam: Pre-processing lambda value
• ar_order: Autoregressive model order
• omega: Weight parameter for positive residuals
• zeta: Weight parameter for negative residuals
• spline_s: Spline smoothing parameter
• pad_len: Padding length for encoder input

Returns:

• lam: Predicted lambda values
• baseline: Estimated baselines
• corrected: Baseline-corrected spectra

Performance

DIRAS+ typically provides:

• Automatic parameter optimization without manual tuning
• Consistent performance across different spectral types
• Scalable processing for large datasets

Contributing

We welcome contributions! Please feel free to:

• Report bugs and issues
• Suggest new features
• Submit pull requests
• Improve documentation

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Note: This is research software. Please ensure proper validation for your specific use case.