Introduction to PyMAD-NG

What is PyMAD-NG?

PyMAD-NG is a Python interface for MAD-NG (Methodical Accelerator Design - Next Generation), a powerful software for simulating and analysing particle accelerators. PyMAD-NG enables seamless communication between Python and MAD-NG, allowing users to script, automate, and interactively control MAD-NG simulations from Python.

PyMAD-NG provides a pythonic API that simplifies interaction with MAD-NG while maintaining high performance. Whether you’re performing optics calculations, beam dynamics simulations, or machine tuning, PyMAD-NG offers the flexibility to work efficiently with MAD-NG from within Python.

Why Use PyMAD-NG?

PyMAD-NG is designed for scientists, engineers, and researchers working on accelerator physics and beam dynamics. It offers several advantages over traditional MAD-NG workflows:

  • Pythonic Interface - Write MAD-NG scripts using intuitive Python commands.

  • Efficient Communication - Uses pipes for fast data exchange between Python and MAD-NG.

  • Seamless Data Handling - Convert MAD-NG tables into Pandas DataFrames for analysis.

  • High Performance - Designed for handling large datasets and computationally intensive simulations.

  • Flexible APIs - Use either a high-level API (more Pythonic) or a low-level API (more control).

  • Jupyter Notebook Support - Work interactively with MAD-NG in a Python notebook.

  • MAD-X Compatibility - Load MAD-X sequences and interact with them in MAD-NG.

How PyMAD-NG Works

PyMAD-NG operates by launching a MAD-NG process in the background and establishing a two-way communication channel between Python and MAD-NG.

  • Sending Commands - You can send MAD-NG commands as Python strings.

  • Receiving Results - Data from MAD-NG can be retrieved into Python for further analysis.

  • MAD Objects in Python - PyMAD-NG exposes MAD-NG objects as Python objects for easy manipulation.

Example Workflow

  1. Initialise PyMAD-NG:

    from pymadng import MAD
mad = MAD()

  2. Load a Sequence & Perform Calculations:

    mad.MADX.load("'lhc_as-built.seq'", "'lhc_as-built.mad'")
mad["tbl", "flw"] = mad.twiss(sequence=mad.MADX.lhcb1)

  3. Retrieve Data from MAD-NG:

    df = mad.tbl.to_df()  # Convert twiss table to a Pandas DataFrame
print(df.head())

  4. Visualise Results in Python:

    import matplotlib.pyplot as plt
plt.plot(df["s"], df["beta11"])
plt.xlabel("s (m)")
plt.ylabel("$\beta_x$-function")
plt.show()

Key Features of PyMAD-NG

Feature

Description

Pythonic Interface

Interact with MAD-NG using Python objects.

High-Level & Low-Level API

Choose between a simple or customisable approach.

Efficient Data Handling

Convert MAD-NG tables (mtable) into Pandas DataFrames.

Two-Way Communication

Send commands to MAD-NG and retrieve results.

MAD-X Compatibility

Import and work with MAD-X sequences.

MAD-8 Compatibility

Import and work with MAD-8 sequences.

Performance Optimised

Supports large datasets and numerical computations.

Jupyter Notebook Support

Use PyMAD-NG interactively within Jupyter.

Who Should Use PyMAD-NG?

If you work with MAD-NG and want to leverage Python’s ecosystem (NumPy, Pandas, Matplotlib, etc.), PyMAD-NG is the perfect tool for you.

Next Steps

Now that you have an overview of PyMAD-NG, you can dive into: