Quick Start Guide

This guide will walk you through your first experience with PyMAD-NG. By the end, you’ll know how to run MAD-NG commands from Python, send and receive data, and perform a basic optics calculation.

Step 1: Create a MAD Instance

Start by importing and creating an instance of the pymadng.MAD object:

from pymadng import MAD
mad = MAD()

This automatically launches a MAD-NG process and connects it to Python.

Step 2: Load a Sequence

PyMAD-NG supports two approaches: a high-level (pythonic) interface and a low-level (script-driven) interface.

High-Level API:

mad.MADX.load("'fodo.seq'", "'fodo.mad'")
mad["seq"] = mad.MADX.seq

Low-Level API:

mad.send("MADX:load('fodo.seq', 'fodo.mad')")
mad.send("seq = MADX.seq")

Step 3: Set Up a Beam

High-Level:

mad.seq.beam = mad.beam()

Low-Level:

mad.send("seq.beam = beam {}")

Step 4: Run a Twiss Calculation

High-Level:

mad["tbl", "flw"] = mad.twiss(sequence=mad.seq)

Low-Level:

mad.send("tbl, flw = twiss {sequence=seq}")
mad.send("py:send(tbl)")
tbl = mad.recv()

Step 5: Analyse the Results

Convert the resulting MAD table to a Pandas DataFrame:

df = mad.tbl.to_df()
print(df.head())

Step 6: Visualise the Optics

Plot the beta function using Matplotlib:

import matplotlib.pyplot as plt
plt.plot(df["s"], df["beta11"])
plt.xlabel("s [m]")
plt.ylabel("Beta Function")
plt.title("Twiss Beta11 vs s")
plt.grid(True)
plt.show()

Additional Tips

  • If you want to send MAD-NG commands directly:

    mad.send("py:send(math.sin(1))")
print(mad.recv())

  • Always match send() and recv() properly to avoid blocking communication.

What Next?

Now that you’ve completed your first PyMAD-NG workflow, explore: