Accelerator Lattice

This example shows how to use the plot_latwiss function to represent your machine’s layout and optics functions in a double-axis plot.

In this example, we will showcase the functionality on a simple lattice, and then demonstrate the use of several parameters to control the plot on the example case of the LHC.

import matplotlib.pyplot as plt

from cpymad.madx import Madx

from pyhdtoolkit.cpymadtools import lhc, matching
from pyhdtoolkit.cpymadtools._generators import LatticeGenerator
from pyhdtoolkit.plotting.lattice import plot_latwiss
from pyhdtoolkit.plotting.styles import _SPHINX_GALLERY_PARAMS
from pyhdtoolkit.utils import logging

logging.config_logger(level="error")
plt.rcParams.update(_SPHINX_GALLERY_PARAMS)  # for readability of this tutorial

Let’s start by generating a simple lattice and setup your simulation:

n_cells: int = 24
base_lattice: str = LatticeGenerator.generate_base_cas_lattice()

madx = Madx(stdout=False)
madx.input(base_lattice)

matching.match_tunes_and_chromaticities(
    madx,
    sequence="CAS3",
    q1_target=6.335,
    q2_target=6.29,
    dq1_target=100,
    dq2_target=100,
    varied_knobs=["kqf", "kqd", "ksf", "ksd"],
)

Plotting the combined machine layout and optics functions is done in a single call to the plot_latwiss function. Here, we will also set the k0l_lim parameter to control the right-hand-side axis in the machine layout axis. The same can be done with the k1_lim parameter.

mu_x_cell = madx.table.summ.Q1[0] / n_cells
mu_y_cell = madx.table.summ.Q2[0] / n_cells
title = rf"Base Lattice, $\mu_{{x, cell}}={mu_x_cell:.3f}, \ \mu_{{y, cell}}={mu_y_cell:.3f}$"

plt.figure(figsize=(18, 11))
plot_latwiss(madx, title=title, k0l_lim=(-0.15, 0.15), k1l_lim=(-0.08, 0.08), disp_ylim=(-10, 125), lw=3)
plt.tight_layout()
plt.show()

madx.exit()

One can customise the plot more to their liking or needs thanks to the other function parameters. Let’s showcase this with the LHC lattice, that we set up below.

Important

This example requires the acc-models-lhc repository to be cloned locally. One can get it by running the following command:

git clone -b 2022 https://gitlab.cern.ch/acc-models/acc-models-lhc.git --depth 1

Here I set the 2022 branch for stability and reproducibility of the documentation builds, but you can use any branch you want.

lhc_madx: Madx = lhc.prepare_lhc_run3(
    opticsfile="acc-models-lhc/operation/optics/R2022a_A30cmC30cmA10mL200cm.madx",
    stdout=False
)

The plot_latwiss function gives the possibility to zoom on a region by providing the xlimits parameter. Let’s first determine the position of points of interest through the TWISS table:

lhc_madx.command.twiss()
twiss_df = lhc_madx.table.twiss.dframe()
twiss_df.name = twiss_df.name.apply(lambda x: x[:-2])
ip1s = twiss_df.s["ip1"]

We can now focus the plot in the Interaction Region 1 by providing the xlimits centered around the value we determined for ip1s.

Tip

In order to zoom on a region, one might be tempted to call the plot and run plt.xlim(...). However, when providing the xlimits parameter, plot_latwiss makes a sub-selection of the TWISS table before doing any plotting. This is provides a nice speedup to the plotting process, as only elements within the limits are rendered on the layout axis, instead of all elements (which can be a lot, and quite lengthy for big machines such as the LHC). It is therefore the recommended way to zoom on a region.

plt.figure(figsize=(18, 11))
plot_latwiss(
    lhc_madx,
    title="Interaction Region 1",
    disp_ylim=(-0.5, 2.5),
    xlimits=(ip1s - 457, ip1s + 457),
    k0l_lim=(-1.3e-2, 1.3e-2),
    k1l_lim=(-6.1e-2, 6.1e-2),
    lw=1.5,
)
plt.axvline(x=ip1s, color="grey", ls="--", lw=1.5, label="IP1")
plt.tight_layout()
plt.show()

Using the xoffset parameter, one can shift the longitudinal coordinate to be centered on 0, with the horizontal axis showing relative position to the given xoffset. This is useful here to zoom closely on IP1 and see the elements’ positions relative to the IP marker.

plt.figure(figsize=(18, 11))
plot_latwiss(
    lhc_madx,
    title="IP1 Surroundings",
    disp_ylim=(-3e-2, 3e-2),
    xoffset=ip1s,
    xlimits=(-85, 85),
    k0l_lim=(-4e-4, 4e-4),
    k1l_lim=(-6e-2, 6e-2),
    lw=1.5,
)
plt.axvline(x=0, color="grey", ls="--", lw=1.5, label="IP1")
plt.tight_layout()
plt.show()

When and only when the k2l_lim parameter is provided, the sextupolar elements are plotted on the lattice layout axis, and an additional scale is put to the right. This is useful to see sextupoles when zooming in, which you would not necessarily want to plot when looking at the big picture, to avoid overcrowding it. Similarly, providing the plot_bpms will add a small marker for BPM elements. Here it is showcased when looking at an LHC arc cell:

plt.rcParams.update({"axes.formatter.limits": (-2, 5)})  # convenience
plt.figure(figsize=(18, 11))
plot_latwiss(
    lhc_madx,
    title="LHC Arc Cell",
    plot_bpms=True,
    disp_ylim=(-0.5, 20),
    beta_ylim=(0, 200),
    k0l_lim=(-3e-2, 3e-2),
    k1l_lim=(-4e-2, 4e-2),
    k2l_lim=(-5e-2, 5e-2),
    lw=1.5,
    xlimits=(14_084.5, 14_191.3),
)
plt.tight_layout()
plt.show()

Let’s not forget to close the rpc connection to MAD-X:

lhc_madx.exit()

References

The use of the following functions, methods, classes and modules is shown in this example:

• lhc: prepare_lhc_run3, setup_lhc_orbit

• generators: LatticeGenerator

• matching: match_tunes_and_chromaticities

• lattice: plot_latwiss