Skip to main content

An accelerator physics tools package for the OMC team at CERN.

Project description

3

Tests Code Climate coverage Code Climate maintainability (percentage) GitHub last commit GitHub release DOI

This is the python-tool package of the Optics Measurements and Corrections team (OMC) at CERN.

Most of the codes are generic and not limited to CERN accelerators, and the package can easily be used for your favorite circular accelerator. To see how to adapt this for your machine, see our documentation, Model section. To contribute, see our guidelines on the OMC website.

Documentation

Installing

Installation is easily done via pip:

pip install omc3

For development purposes, we recommend creating a new virtual environment and installing from VCS in editable mode with all extra dependencies (cern for packages only available in the CERN GPN, test for pytest and relevant plugins, and doc for packages needed to build documentation)

git clone https://github.com/pylhc/omc3
pip install --editable "omc3[all]"

Codes can then be run with either python -m omc3.SCRIPT --FLAG ARGUMENT or calling the .py file directly.

Functionality

Main Scripts

Main scripts to be executed lie in the /omc3 directory. These include:

  • hole_in_one.py to perform frequency analysis on turn by turn BPM data and infer optics (and more) for a given accelerator.
  • kmod_importer.py to average, import and calculate lumi-imbalace K-modulation results.
  • knob_extractor.py to extract from NXCALS the value of given knobs in the machine at a given time.
  • model_creator.py to generate optics models required for optics analysis.
  • global_correction.py to calculate corrections from measurement files.
  • response_creator.py to provide correction response files.
  • tbt_converter.py to convert different turn by turn data types to SDDS, potentially adding noise.
  • amplitude_detuning_analysis.py to perform amp. det. analysis on optics data with tune correction.
  • madx_wrapper.py to start a MAD-X run with a file or string as input.

Plotting Scripts

Plotting scripts for analysis outputs can be found in /omc3/plotting:

  • plot_spectrum.py to generate plots from files generated by frequency analysis.
  • plot_bbq.py to generate plots from files generated by BBQ analysis.
  • plot_amplitude_detuning.py to generate plots from files generated by amplitude detuning analysis.
  • plot_optics_measurements.py to generate plots from files generated by optics_measurements.
  • plot_tfs.py all-purpose tfs-file plotter.
  • plot_kmod_results.py to plot the beta and waist of the K-modulation results.

Other Scripts

Other general utility scripts are in /omc3/scripts:

  • update_nattune_in_linfile.py to update the natural tune columns in the lin files by finding the highest peak in the spectrum in a given interval.
  • write_madx_macros.py to generate MAD-X tracking macros with observation points from a TWISS file.
  • merge_kmod_results.py to merge LSA results files created by kmod, and add the luminosity imbalance if the 4 needed IP/Beam files combination are present.
  • fake_measurement_from_model.py to create a fake measurement based on a model TWISS file.
  • betabeatsrc_output_converter.py to convert outputs from our old codes to omc3's new standardized format.
  • linfile_clean.py to automatically clean given columns in lin files.
  • kmod_average.py to calculate the average of multiple K-modulation measurements.
  • kmod_import.py to import a K-modulation measurement into an optics-measurement directory.
  • kmod_lumi_imbalace.py to calculate the luminosity imbalance between two IPs from averaged K-modulation files.
  • bad_bpms_summary.py to collect and summarize the bad BPMs from GUI runs.

Example use for these scripts can be found in the tests files. Documentation including relevant flags and parameters can be found at https://pylhc.github.io/omc3/.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

omc3-0.20.0.tar.gz (20.4 MB view details)

Uploaded Source

Built Distribution

omc3-0.20.0-py3-none-any.whl (20.8 MB view details)

Uploaded Python 3

File details

Details for the file omc3-0.20.0.tar.gz.

File metadata

  • Download URL: omc3-0.20.0.tar.gz
  • Upload date:
  • Size: 20.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for omc3-0.20.0.tar.gz
Algorithm Hash digest
SHA256 2bbf2fc0ee2e50c800e65e28e1f11070c6a2bb33f79664fed33306879c523140
MD5 cc7ad7c250d9c9879af1140c311862d1
BLAKE2b-256 5d31a07f17b9e7556a1aee48777524fc0c6bfcd4bb0518fe46e288e12e4bf2b7

See more details on using hashes here.

File details

Details for the file omc3-0.20.0-py3-none-any.whl.

File metadata

  • Download URL: omc3-0.20.0-py3-none-any.whl
  • Upload date:
  • Size: 20.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for omc3-0.20.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ea2010ab7743d2783254c2815ab2234ddb1c5b7f9c818b1d86ded674f1a6fe27
MD5 6536a11c1527dd9bae7f5f571c9c8165
BLAKE2b-256 29fc6285dd3b640fbe0a4b1537f92a2f6067eaadd0d6827fe78c2bdd70a98d7d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page