omc3 0.27.0

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

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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

• Autogenerated docs via Sphinx can be found at https://pylhc.github.io/omc3/.
• General documentation of the OMC Team is located at https://pylhc.github.io/.

Installing

The package is deployed on PyPI and can easily be installed via pip:

python -m 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
python -m pip install --editable "omc3[all]"

Functionality

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

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.