Using the Zgoubidoo low-level interface

Test

Reading Zgoubi output files

TODO

zgoubidoo.outputs.read_fai_file(filename: str = 'zgoubi.fai', path: str = '.') → pandas.core.frame.DataFrame[source]

Function to read Zgoubi .fai files.

Reads the content of a Zgoubi .fai file (‘faisceau’, ‘beam’ file) and formats it as a valid Pandas DataFrame with headers.

Example

>>> read_fai_file()
Parameters

  • filename – the name of the file

  • path – the path to the .fai file

Returns

a Pandas DataFrame with the .fai file content.

Raises

a FileNotFoundError in case the file is not found. –

zgoubidoo.outputs.read_plt_file(filename: str = 'zgoubi.plt', path: str = '.') → pandas.core.frame.DataFrame[source]

Function to read Zgoubi .plt files.

Reads the content of a Zgoubi .plt file (‘plot’ file) and formats it as a valid Pandas DataFrame with headers.

Notes

each coordinate is converted from the Zgoubi internal unit system onto the SI system. This means, in particular, that the positions are in meters and the angles in mrad.

The special columns have the following meaning:

  • LET: one character string (for tagging (groups of) particles)

  • IREP is an index which indicates a symmetry with respect to the median plane. For instance,

if Z(I + 1) = −Z(I), then normally IREP(I + 1) = IREP(I ). Consequently the coordinates of particle I + 1 will not be obtained from ray-tracing but instead deduced from those of particle I by simple symmetry. This saves on computing time.

Example

>>> read_plt_file()
Parameters

  • filename – the name of the file

  • path – the path to the .plt file

Returns

a Pandas DataFrame with the .plt file content.

Raises

a FileNotFoundError in case the file is not found. –

zgoubidoo.outputs.read_srloss_file(filename: str = 'zgoubi.SRLOSS.out', path: str = '.') → pandas.core.frame.DataFrame[source]

Read Zgoubi SRLOSS files to a DataFrame.

Reads the content of a Zgoubi SRLOSS (synchrotron radiation losses) file (produced with SRPrint) and formats it as a valid Pandas DataFrame with headers.

Example

>>> read_srloss_file()
Parameters

  • filename – the name of the file

  • path – the path to the SRLOSS file

Returns

a Pandas DataFrame with the SRLOSS file content.

Raises

a FileNotFoundException in case the file is not found. –

zgoubidoo.outputs.read_srloss_steps_file(filename: str = 'zgoubi.SRLOSS.STEPS.out', path: str = '.') → pandas.core.frame.DataFrame[source]

Read Zgoubi SRLOSS STEPS files to a DataFrame.

Reads the content of a Zgoubi SRLOSS STEPS (synchrotron radiation losses for each integration steps) file (produced with SRPrint) and formats it as a valid Pandas DataFrame with headers.

Example

>>> read_srloss_steps_file()
Parameters

  • filename – the name of the file

  • path – the path to the SRLOSS STEPS file

Returns

a Pandas DataFrame with the SRLOSS STEPS file content.

Raises

a FileNotFoundException in case the file is not found. –

zgoubidoo.outputs.read_matrix_file(filename: str = 'zgoubi.MATRIX.out', path: str = '.') → pandas.core.frame.DataFrame[source]

Read Zgoubi MATRIX files to a DataFrame.

Reads the content of a Zgoubi matrix file (parent from a Twiss command) and formats it as a valid Pandas DataFrame with headers.

Notes

the resulting DataFrame uses SI units.

Example

>>> read_matrix_file()
Parameters

  • filename – the name of the file

  • path – the path to the zgoubi.MATRIX.out file

Returns

a Pandas DataFrame with the zgoubi.MATRIX.out file content.

Raises

a FileNotFoundError in case the file is not found. –

zgoubidoo.outputs.read_optics_file(filename: str = 'zgoubi.OPTICS.out', path: str = '.') → pandas.core.frame.DataFrame[source]

Read Zgoubi OPTICS files to a DataFrame.

Reads the content of a Zgoubi optics file (parent from a Optics) and formats it as a valid Pandas DataFrame with headers.

Notes

the resulting DataFrame uses SI units.

Example

>>> read_optcs_file()
Parameters

  • filename – the name of the file

  • path – the path to the zgoubi.OPTICS.out file

Returns

a Pandas DataFrame with the zgoubi.OPTICS.out file content.

Raises

a FileNotFoundError in case the file is not found. –

Computing Twiss parameters

Step-by-step computation of the transfer matrix and Twiss parameters from Zgoubi tracks.

The functions in this module perform a first-order analysis of the dynamics, via the computation of the transfer matrix and its parametrizations.

The standard uncoupled Twiss parametrization (including off-momentum effects, aka. dispersion) is the default option.

Additional formalisms for the parametrization of fully coupled transfer matrices are also available (Teng, Ripken, etc.).

Example

import numpy as np import pandas as pd import zgoubidoo from zgoubidoo.commands import * _ = zgoubidoo.ureg

zgoubidoo.twiss.compute_transfer_matrix(beamline: zgoubidoo.input.Input, tracks: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Constructs the step-by-step transfer matrix from tracking data (finite differences). The approximation uses the O(3) formula (not just the O(1) formula) and therefore makes use of all the 11 particles.

Parameters

  • beamline – the Zgoubidoo Input beamline

  • tracks – tracking data

Returns

a Panda DataFrame representing the transfer matrix

Example

Here is a typical example to call compute_transfer_matrix:

>>> tracks = zgoubidoo.read_plt_file()
>>> zi = zgoubidoo.Input()
>>> matrix = zgoubidoo.twiss.compute_transfer_matrix(zi, tracks)