DDR S-Parameter Analysis Tool

A modular Python tool for generating, processing, and visualizing DDR channel S-parameters (Touchstone files), with Excel reporting.

Features

• Touchstone Generation: Create realistic example S-parameter files for DDR channels.
• Touchstone Processing: Read Touchstone files, extract S-parameters, and calculate metrics (insertion loss, return loss, crosstalk, TDR, etc.).
• Plotting & Excel Embedding: Plot S-parameter curves, save figures, and embed them in a well-organized Excel report.
• Easy Integration: Feed your own processed data from other programs and generate reports directly (see below).

Project Structure

.
├── main.py                  # Main workflow script (includes simplified API)
├── example_simplified_api.py # Example of using the simplified API
├── touchstone_generator.py  # Generate example Touchstone files
├── touchstone_processor.py  # Read/process Touchstone files, calculate metrics
├── plotting_utils.py        # Plotting and Excel embedding utilities
├── sparams/                 # Folder for generated Touchstone files
├── figures/                 # Folder for generated figures
├── requirements.txt         # Python dependencies
└── README.md                # This file

Quick Start

1. Install dependencies:

   pip install -r requirements.txt

2. Run the main workflow:

   python main.py

   This will:

   • Generate example Touchstone files in sparams/
   • Read and process the files
   • Plot S-matrix figures and save them in figures/
   • Create an Excel report (ddr_report.xlsx) with all S-matrix, metrics, and TDR plots

3. Open ddr_report.xlsx to view the results.

How It Works

• Touchstone Generation: touchstone_generator.py provides functions to create realistic multi-port S-parameter files for DDR channel analysis.
• Touchstone Processing: touchstone_processor.py reads Touchstone files and calculates metrics of interest.
• Plotting & Excel: plotting_utils.py handles all plotting and Excel embedding, ensuring a visually appealing report.
• Orchestration: main.py ties everything together for a seamless workflow.

Integrating with Other Programs

You can use your own code to load/process Touchstone files or generate S-parameter/metrics/TDR data, and then feed that data directly into the reporting workflow. This is useful if you already have a custom loader or want to connect with other tools.

Minimal Example: Generate a Report from Your Own Data

Simplified API (Recommended)

from main import run_report_from_external_data_simplified

# Prepare your data as dictionaries (see below for structure)
s_matrix_data = { ... }   # dict: model_name -> s-matrix data
tdr_data = { ... }        # dict: model_name -> TDR data
model_names = [ ... ]     # list of model names (order for plotting)

# Call the simplified API - metrics are calculated automatically from S-matrix data
run_report_from_external_data_simplified(
    s_matrix_data,
    tdr_data,
    model_names,
    output_prefix="my_external_analysis"  # Optional: prefix for output files
)

Full API (if you want to provide pre-calculated metrics)

from main import run_report_from_external_data

# Prepare your data as dictionaries
s_matrix_data = { ... }   # dict: model_name -> s-matrix data
metrics_data = { ... }    # dict: model_name -> metrics data (optional)
tdr_data = { ... }        # dict: model_name -> TDR data
model_names = [ ... ]     # list of model names (order for plotting)

# Call the full API
run_report_from_external_data(
    s_matrix_data,
    metrics_data,
    tdr_data,
    model_names,
    output_prefix="my_external_analysis"
)

Data Structure Example

• S-matrix data: s_matrix_data[model_name]["(row,col)"]["s_parameter_db"] (list of dB values)
• TDR data: tdr_data[model_name][signal]["tdr_left"], tdr_right, time_axis_ns, etc.
• Metrics data: Automatically calculated from S-matrix data using the same algorithms as the internal workflow
• See the code in s_parameter_processor.py for details, or run the internal workflow once and inspect the generated *_data.pkl files for examples.

Tip:

You can use the HierarchicalDataCollector and DataOrganizer utilities to help organize your data if needed.

Example Script:

Run python example_simplified_api.py to see a complete example of using the simplified API with minimal data preparation.

Customization

• To analyze your own Touchstone files, place them in sparams/ and update MODEL_NAME in main.py.
• To change the number of ports or figure size, adjust the parameters in main.py and plotting_utils.py.
• To add new metrics or plots, extend touchstone_processor.py and plotting_utils.py as needed.

Requirements

• Python 3.7+
• numpy
• matplotlib
• xlsxwriter
• scikit-rf (optional, for robust Touchstone parsing)

Install all dependencies with:

pip install -r requirements.txt

License

This tool is provided as-is for educational and research purposes.

Contributing

Contributions and suggestions are welcome! Open an issue or submit a pull request to improve the tool.