LISO

Author: Jun Zhu, zhujun981661@gmail.com

LISO (LInac Simulation and Optimization) is a library which provides a unified interface for numerical simulations, experiments and data management in the era of big data and artificial intelligence. LISO was initially created only for simulation and optimization back in 2017. After a more than two years interruption, it is live again but aims at:

1. providing a high-level API to run a large numbers of beam dynamics simulations using a combination of different codes;
2. providing a unified IO for the simulated and experimental data;
3. providing an interface for deep learning and deep reinforcement learning studies on accelerator physics.

Documentation

The full documentation can be found at

https://liso.readthedocs.io/

Getting started

Installation

$ pip install liso

Use LISO in your experiments

Parameter scan

from liso import EuXFELInterface, MachineScan
from liso import doocs_channels as dc

m = EuXFELInterface()

m.add_control_channel('XFEL.RF/LLRF.CONTROLLER/VS.GUN.I1/PHASE.SAMPLE', dc.FLOAT,
                      write_address='XFEL.RF/LLRF.CONTROLLER/CTRL.GUN.I1/SP.PHASE')
m.add_control_channel('XFEL.RF/LLRF.CONTROLLER/VS.A1.I1/PHASE.SAMPLE', dc.FLOAT,
                      write_address='XFEL.RF/LLRF.CONTROLLER/CTRL.A1.I1/SP.PHASE')

m.add_diagnostic_channel('XFEL.DIAG/CAMERA/OTRC.64.I1D/IMAGE_EXT_ZMQ', dc.ARRAY, 
                         shape=(1750, 2330), dtype='uint16')

sc = MachineScan(m)
sc.add_param('XFEL.RF/LLRF.CONTROLLER/VS.GUN.I1/PHASE.SAMPLE', lb=-3, ub=3)
sc.add_param('XFEL.RF/LLRF.CONTROLLER/VS.A1.I1/PHASE.SAMPLE', lb=-3, ub=3)

sc.scan(4000, n_tasks=8)

Reading experimental data from files

from liso import open_run

run = open_run('r0001')

# Get an overview of the run
run.info()

# Loop over a run
for pid, data in run:
    pass

# Access a single macropulse
# - by id
pid, data = run.from_id(123456789)
# - by index
pid, data = run.from_index(0)

# Access a channel data
ch_data = run.channel('XFEL.RF/LLRF.CONTROLLER/VS.GUN.I1/PHASE.SAMPLE')
# and convert it to a numpy array
ch_data_array = ch_data.numpy()

Use LISO to run simulations

Building a linac

from liso import Linac

linac = Linac(2000)

linac.add_beamline('astra',
                   name='injector',
                   swd='../astra_files',
                   fin='injector.in',
                   template='injector.in.000',
                   pout='injector.0450.001')

Running a parameter scan or a jitter study

from liso import LinacScan


sc = LinacScan(linac)

sc.add_param('gun_gradient', start=120, stop=140, num=10, sigma=-0.001)
sc.add_param('gun_phase', start=-10, stop=10, num=20, sigma=0.1)

sc.scan()

Reading simulated data from files

from liso import open_sim

sim = open_sim('./')

# Get an overview of the simulation
sim.info()

# Loop over the simulated data
for sid, data in sim:
    pass

# Access a single simulation
# - by id
sid, data = sim.from_id(1)
# - by index
sid, data = sim.from_index(0)

# Access a phasespace data
ch_data = sim.channel('injector/out', ['x', 'px'])
# and convert it to a numpy array
ch_data_array = ch_data.numpy()

Cite LISO

A BibTeX entry that you can use to cite it in a publication:

@misc{liso,
  Author = {J. Zhu},
  Title = {LISO},
  Year = {2020},
  publisher = {GitHub},
  journal = {/~https://github.com/zhujun98/liso},
  version = {...}
}