PyJMad

Yet another python wrapper around JMad using JPype. PyJMad aims for compatibility with other CERN Java-Python bridges, using cmmnbuild-dep-manager (and access to CERN resources) for easy side-by-side installation. A particular focus lies on interactive usage and integration with IPython/Jupyter.

Set up:

import pyjmad
jmad = pyjmad.JMad()

Explore model packs

JMad model packs are now stored as Git repos, and accessed through jmad-modelpack-service - the "previous" style of loading models from the Java class path is still supported through the special "INTERNAL" model pack. At the moment, repositories must be hosted on a GitLab service, as they are accessed through the GitLab REST API.

jmad.model_packs

Example output:

ModelPackService [ 
 -> Enabled Repos: 
    - https://gitlab.cern.ch/jmad-modelpacks-cern
    - jmad:internal
 -> Available ModelPacks: 
    - INTERNAL
    - jmad-modelpack-lhc
    - jmad-modelpack-sps
    - jmad-modelpack-leir
    - jmad-modelpack-ps
]

The models are accessed through indexing model_packs. Jupyter/IPython autocompletion is supported at all stages.

model_def = jmad.model_packs['jmad-modelpack-lhc'].branches['master'].models['LHC 2017']

Add a custom model pack repository:

jmad.model_packs.add_repository('https://gitlab.cern.ch/jmad-repo-michi-testing')

Setup a Model:

md = jmad.model_packs['jmad-modelpack-lhc'].branches['master'].models['LHC 2017']
jmad.create_model(md)

Set a Sequence and Optic to use:

lhcModel.sequence = 'lhcb1'
lhcModel.optic = 'R2017a_A11mC11mA10mL10m'

Twissing:

twiss = lhcModel.twiss(variables=('S','BETX','BETY','X','Y'))

This will return a namedtuple:

• twiss.summary is a dict of the twiss summary
• twiss.data is a Pandas DataFrame of the twiss results

Plot it:

plt.figure()
plt.plot(res.data.S, res.data.BETX)
plt.plot(res.data.S, res.data.BETY)
plt.show()

Show and/or edit strengths:

lhcModel.strengths

Set strengths (jupyter/ipython autocompletion hints supported):

lhcModel.strengths['on_x1'] = 140
lhcModel.strengths['on_x5'] = 140
print(lhcModel.strengths['on_x5'])

Note that a new strength is automatically created if you assign it a value; a warning is issued in this case:

lhcModel.strengths['on_xx5_v'] = 140

INFO:root:Creating new MAD-X strength on_xx5_v

Deal with Elements:

print(lhcModel.elements)
corrector = lhcModel.elements['MCBCH.10L1.B1']
corrector.h_kick = 4.2e-6  # rad
# or equivalent
corrector.attributes['hkick'] = 4.2e-6
print(corrector.attributes)

(jupyter/ipython autocompletion hints supported, both on elements and attributes)

List certain range of accelerator elements:

lhcModel.elements['BPM.10L1.B1':'BPM.10R1.B1']

Matching:

from pyjmad.matching import *
mr = lhcModel.match(GlobalConstraint(Q1=62.28, Q2=60.31),
                    Vary('KQT4.L3', step=0.000001),
                    Vary('KQT4.R3', step=0.000001))

mr = lhcModel.match(LocalConstraint('IP1', BETX=0.45, BETY=0.45),
                    Vary('KQX.L1', step=0.000001),
                    Vary('KQX.R1', step=0.000001))

Open a JMad GUI

The GUI will share the state with the python script and can be used for interactive exploration. Note that on Mac OS X this currently blocks the main python thread due to Swing/Cocoa/GUI API limitations.

jmad.open_jmad_gui()