Spectrum Analyzer

A Streamlit web application for analyzing optical spectra of emitters, filters, and detectors.

Features

• Visualize and analyze various light emitter spectra (LEDs, Lamps, Lasers)
• Apply optical filters with customizable parameters:
  • Band-pass filters with adjustable center wavelength and bandwidth
  • Long-pass filters with customizable cutoff wavelength
  • Short-pass filters with customizable cutoff wavelength
• Apply detector response curves to see how detectors "see" the filtered light
• Calculate and display key spectral statistics:
  • Peak wavelength
  • Full Width at Half Maximum (FWHM)
  • Relative intensity
• Support for both library filters and custom filter creation
• Interactive visualizations using Plotly

Installation

1. Clone the repository:

git clone <repository-url>
cd <repository-directory>

2. Create and activate a virtual environment (optional but recommended):

# Using venv
python -m venv venv
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

Usage

1. Run the Streamlit app:

streamlit run main.py

2. Open your web browser and navigate to the URL shown in the terminal (typically http://localhost:8501)

3. Use the sidebar to:

   • Upload your own data in Parquet format or use the included sample data
   • Select emitters to analyze
   • Add and configure filters
   • Enable detector response if needed

4. The main panel will display:

   • Emitter spectra
   • Filter and detector transmission/sensitivity curves
   • Resulting filtered spectrum for each emitter
   • Spectral statistics in the expandable section

Data Format

The application expects data in Parquet format with specific column naming conventions:

• For emitters:

  • Identifier columns: "company" and "device_id"
  • Type column: "type" (e.g., "LED", "Lamp", "Laser")
  • Wavelength column: "wave_nm"
  • Intensity column: "int_au"

• For filters:

  • Identifier columns: "company" and "device_id"
  • Type column: "type" (should contain keywords like "long", "short", "band")
  • Wavelength column: "wave_nm"
  • Transmission column: "int_au"

• For detectors:

  • Identifier columns: "company" and "device_id"
  • Type column: "type"
  • Wavelength column: "wave_nm"
  • Sensitivity column: "int_au"

Configuration

The application uses a configuration file (config.toml) to customize behavior:

• Data paths
• Spectrum wavelength range and step size
• Default filter parameters
• Data structure definition and keywords for column identification

Utility Scripts

The project includes utility scripts for working with data files:

• parquet_reader.py: Converts Parquet files to Excel format
• ods_to_parquet.py: Converts Excel files to Parquet format

Requirements

• Python 3.7+
• Streamlit
• Polars
• NumPy
• Plotly
• Loguru
• Pandas

Project Structure

.
├── data/
│   ├── light_sources.parquet   # Sample data
│   ├── light_sources.xlsx      # Source data (Excel format)
│   └── light_sources.ods       # Alternative source data (ODS format)
├── manual_scripts/
│   ├── ods_to_parquet.py       # Utility to convert Excel to Parquet
│   └── parquet_reader.py       # Utility to read Parquet data
├── config.toml                 # Application configuration
├── main.py                     # Main Streamlit application
├── README.md                   # This documentation
└── requirements.txt            # Python dependencies

License

MIT

Contributing

[Include contribution guidelines here]