Main Entrypoints

Harmonic Analysis

Entrypoint Hole in One

Entrypoint Measure Optics

Created on 11/07/18

author:Lukas Malina

Top-level script, which computes various lattice optics parameters from frequency spectra

class measure_optics.InputFiles(files_to_analyse)

Stores the input files, provides methods to gather quantity specific data

Public methods:

get_dpps(plane) get_joined_frame(plane, columns, zero_dpp=False, how=’inner’) get_columns(frame, column) get_data(frame, column)

dpps(plane)

Gathers measured DPPs from input files corresponding to given plane :param plane: “X” or “Y”

Returns:numpy array of DPPs

static get_columns(frame, column)

Returns list of columns of frame corresponding to column in original files :param frame: joined frame :param column: name of column in original files

Returns:list of columns

get_data(frame, column)

Returns data in columns of frame corresponding to column in original files :param frame: joined frame :param column: name of column in original files

Returns:data in numpy array corresponding to column in original files

joined_frame(plane, columns, zero_dpp=False, how='inner')

Constructs merged DataFrame from InputFiles :param plane: “X” or “Y” :param columns: list of columns from input files :param zero_dpp: if True merges only zero-dpp files, default is False :param how: way of merging: ‘inner’ (intersection) or ‘outer’ (union), default is ‘inner’

Returns:merged DataFrame from InputFiles

measure_optics.measure_optics(input_files, measure_input)

Main function to compute various lattice optics parameters from frequency spectra :param input_files: InputFiles object containing frequency spectra files (linx/y) :param measure_input: OpticsInput object containing analysis settings

Returns:

Amplitude Detuning

Entrypoint Amplitude Detuning Analysis

Entrypoint for amplitude detuning analysis.

This module provides functionality to run amplitude detuning analysis with additionally getting BBQ data from timber, averaging and filtering this data and subtracting it from the measurement data.

Furthermore, the orthogonal distance regression is utilized to get a linear fit from the measurements.

Also, plotting functionality is integrated, for the amplitude detuning as well as for the bbq data.

author:Joschua Dilly

amplitude_detuning_analysis.analyse_with_bbq_corrections(*args, **kwargs)

Create amplitude detuning analysis with BBQ correction from timber data.

Keyword Arguments:  

• Required --
• beam (int) -- Which beam to use. Flags: --beam
• kickac_path (str) -- Location of the kickac file Flags: --kickac
• plane (str) -- Plane of the kicks. ‘X’ or ‘Y’. Flags: --plane Choices: XY
• Optional --
• ampdet_plot_out (str) -- Save the amplitude detuning plot here. Flags: --ampdetplot
• ampdet_plot_show -- Show the amplitude detuning plot. Flags: --ampdetplotshow Action: store_true
• ampdet_plot_xmax (float) -- Maximum action (x-axis) in amplitude detuning plot. Flags: --ampdetplotxmax
• ampdet_plot_xmin (float) -- Minimum action (x-axis) in amplitude detuning plot. Flags: --ampdetplotxmin
• ampdet_plot_ymax (float) -- Maximum tune (y-axis) in amplitude detuning plot. Flags: --ampdetplotymax
• ampdet_plot_ymin (float) -- Minimum tune (y-axis) in amplitude detuning plot. Flags: --ampdetplotymin
• bbq_out (str) -- Output location to save bbq data as tfs-file Flags: --bbqout
• bbq_plot_full -- Plot the full bqq data with interval as lines. Flags: --bbqplotfull Action: store_true
• bbq_plot_out (str) -- Save the bbq plot here. Flags: --bbqplot
• bbq_plot_show -- Show the bbq plot. Flags: --bbqplotshow Action: store_true
• bbq_plot_two -- Two plots for the bbq plot. Flags: --bbqplottwo Action: store_true
• debug -- Activates Debug mode Flags: --debug Action: store_true
• fine_cut (float) -- Cut (i.e. tolerance) of the tune for the fine cleaning. Flags: --finecut
• fine_window (int) -- Length of the moving average window. (# data points) Flags: --finewindow
• kickac_out (str) -- If given, writes out the modified kickac file Flags: --kickacout
• label (str) -- Label to identify this run. Flags: --label
• logfile (str) -- Logfile if debug mode is active. Flags: --logfile
• timber_in -- Fill number of desired data or path to presaved tfs-file Flags: --timberin
• timber_out (str) -- Output location to save fill as tfs-file Flags: --timberout
• tune_cut (float) -- Cuts for the tune. For BBQ cleaning. Flags: --tunecut
• tune_x (float) -- Horizontal Tune. For BBQ cleaning. Flags: --tunex
• tune_x_max (float) -- Horizontal Tune minimum. For BBQ cleaning. Flags: --tunexmax
• tune_x_min (float) -- Horizontal Tune minimum. For BBQ cleaning. Flags: --tunexmin
• tune_y (float) -- Vertical Tune. For BBQ cleaning. Flags: --tuney
• tune_y_max (float) -- Vertical Tune minimum. For BBQ cleaning. Flags: --tuneymax
• tune_y_min (float) -- Vertical Tune minimum. For BBQ cleaning. Flags: --tuneymin
• window_length (int) -- Length of the moving average window. (# data points) Flags: --window Default: 20

amplitude_detuning_analysis.plot_bbq_data(*args, **kwargs)

Plot BBQ wrapper.

Keyword Arguments:  

• Required --
• input -- BBQ data as data frame or tfs file. Flags: --in
• Optional --
• interval (str) -- x_axis interval that was used in calculations. Flags: --interval
• output (str) -- Save figure to this location. Flags: --out
• show -- Show plot. Flags: --show Action: store_true
• two_plots -- Plot two axis into the figure. Flags: --two Action: store_true
• xmax (str) -- Upper x-axis limit. (yyyy-mm-dd HH:mm:ss.mmm) Flags: --xmax
• xmin (str) -- Lower x-axis limit. (yyyy-mm-dd HH:mm:ss.mmm) Flags: --xmin
• ymax (float) -- Upper y-axis limit. Flags: --ymax
• ymin (float) -- Lower y-axis limit. Flags: --ymin

Fullresponse Creation

Entrypoint Fullresponse Pandas

Provides a response generation wrapper. The response matrices can be either created by response_madx or analytically via TwissResponse.

author:Joschua Dillys

generate_fullresponse_pandas.create_response(*args, **kwargs)

Entry point for creating pandas-based response matrices.

The response matrices can be either created by response_madx or TwissResponse.

Keyword Arguments:  

• Required --
• model_dir (str) -- Path to the model directory. Flags: [‘-m’, ‘--model_dir’]
• outfile_path (str) -- Name of fullresponse file. Flags: [‘-o’, ‘--outfile’]
• Optional --
• creator (str) -- Create either with madx or analytically from twiss file. Flags: --creator Choices: (‘madx’, ‘twiss’) Default: madx
• debug -- Print debug information. Flags: --debug Action: store_true
• delta_k (float) -- Delta K1L to be applied to quads for sensitivity matrix (madx-only). Flags: [‘-k’, ‘--deltak’] Default: 2e-05
• optics_params (str) -- List of parameters to correct upon (e.g. BBX BBY; twiss-only). Flags: --optics_params
• variable_categories -- List of the variables classes to use. Flags: --variables Default: ['MQM', 'MQT', 'MQTL', 'MQY']

Correction

Entrypoint Correct Iterative

Iterative Correction Scheme.

The response matrices \(R_{O}\) for the observables \(O\) (e.g. BBX, MUX, …) are loaded from a file and then the equation

(1)\[R_{O} \cdot \delta var = O_{meas} - O_{model}\]

is being solved for \(\delta var\) via a chosen method (at the moment only numpys pinv, which creates a pseudo-inverse via svd is used).

The response matrices are hereby merged into one matrix for all observables to solve vor all \(\delta var\) at the same time.

To normalize the observables to another weigths (W) can be applied.

Furthermore, an errorcut, specifying the maximum errorbar for a BPM to be used, and modelcut, specifying the maximum distance between measurement and model for a BPM to be used, can be defined. Data from BPMs outside of those cut-values will be discarded. These cuts are defined for each observable separately.

After each iteration the model variables are changed by \(-\delta var\) and the observables are recalculated by Mad-X. (1) is then solved again.

author:Lukas Malina, Joschua Dilly

Possible problems and notes:

• do we need error cut, when we use error-based weights? probably not anymore
• error-based weights default? likely - but be carefull with low tune errors vs

svd cut in pseudoinverse

• manual creation of pd.DataFrame varslist, deltas? maybe

tunes in tfs_pandas single value or a column?

• Minimal strength removed
• Check the output files and when they are written
• There should be some summation/renaming for iterations
• For two beam correction
• The two beams can be treated separately until the calcultation of correction
• Values missing in the response (i.e. correctors of the other beam) shall be

treated as zeros

• Missing a part that treats the output from LSA

global_correct_iterative.global_correction(*args, **kwargs)

Do the global correction. Iteratively.

Keyword Arguments:  

• Required --
• meas_dir -- Path to the directory containing the measurement files. Flags: --meas_dir
• model_dir -- Path to the dir containing the model (twiss.dat or twiss_elements.dat) to use. Flags: --model_dir
• Optional --
• beta_file_name -- Prefix of the beta file to use. E.g.: getkmodbeta Flags: --beta_file_name Default: getbeta
• debug -- Print debug information. Flags: --debug Action: store_true
• eps (float) -- (Not implemented yet) Convergence criterion. If \(<|\Delta(PARAM \cdot WEIGHT)|> < \epsilon\), stop iteration. Flags: --eps Default: None
• errorcut (float) -- Reject BPMs whose error bar is higher than the corresponding input. Input in order of optics_params. Flags: --error_cut
• fullresponse_path -- Path to the fullresponse binary file. If not given, calculates the response analytically. Flags: --fullresponse
• max_iter (int) -- Maximum number of correction re-iterations to perform. A value of 0 means the correction is calculated once (like in the old days). Flags: --max_iter Default: 3
• method (str) -- Optimization method to use. Flags: --method Choices: [‘pinv’, ‘omp’] Default: pinv
• min_corrector_strength (float) -- Minimum (absolute) strength of correctors. Flags: --min_corrector_strength Default: 0.
• modelcut (float) -- Reject BPMs whose deviation to the model is higher than the correspoding input. Input in order of optics_params. Flags: --model_cut
• n_correctors (int) -- Maximum number of correctors to use. (Method: ‘omp’) Flags: --n_correctors
• optics_file -- Path to the optics file to use, usually modifiers.madx. If not present will default to model_path/modifiers.madx Flags: --optics_file
• optics_params (str) -- List of parameters to correct upon (e.g. BBX BBY) Flags: --optics_params Default: ['MUX', 'MUY', 'BBX', 'BBY', 'NDX', 'Q']
• output_path -- Path to the directory where to write the output files, will default to the --meas input path. Flags: --output_dir Default: None
• svd_cut (float) -- Cutoff for small singular values of the pseudo inverse. (Method: ‘pinv’) Singular values smaller than \(rcond \cdot largest_singular_value\) are set to zero Flags: --svd_cut Default: 0.01
• use_errorbars -- If True, it will take into account the measured errorbars in the correction. Flags: --use_errorbars Action: store_true
• variable_categories -- List of names of the variables classes to use. Flags: --variables Default: ['MQM', 'MQT', 'MQTL', 'MQY']
• virt_flag -- If true, it will use virtual correctors. Flags: --virt_flag Action: store_true
• weights (float) -- Weights to apply to each measured quantity. Input in order of optics_params. Flags: --weights

Cleaning

Entrypoint Isolation Forest

Created on May 4, 2018 Application of Isolation Forest algorithm for the diagnostics of BPM signal. Original paper: Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation forest.” Data Mining, 2008. ICDM‘08. Eighth IEEE International Conference.

@author: Elena Fol

iforest_for_bpms.remove_bpms_from_file(paths, bad_bpm_names, plane)

Writes a backup of the original .lin files (e.g .linx --> .linx.notcleaned) and removes the BPNs identified by iForest as bad. :param paths: original lin files :param bad_bpm_names: list of the names of bad BPMs identified by iForest

iforest_for_bpms.revert_forest_cleaning(files)

Reverts the cleaning. The backup files are renamed back to the original names (e.g .linx.notcleaned --> .linx) :param paths: list of files, where bad bpms identified by iForest are removed

MADX

Entrypoint Madx Wrapper

module:madx.madx_wrapper

Runs MADX with a file or a string as an input, it processes @required macros. If defined, writes the processed MADX script and logging output into files. TODO: write tests

exception madx_wrapper.MadxError

madx_wrapper.resolve_and_run_file(input_file, output_file=None, log_file=None, madx_path='/home/travis/build/pylhc/Beta-Beat.src/madx/bin/madx-linux64-gnu', cwd=None)

Runs MADX in a subprocess.

madx_wrapper.input_file

MADX input file

madx_wrapper.output_file

If given writes resolved MADX script.

madx_wrapper.log_file

If given writes MADX logging output.

madx_wrapper.madx_path

Path to MADX executable

madx_wrapper.resolve_and_run_string(input_string, output_file=None, log_file=None, madx_path='/home/travis/build/pylhc/Beta-Beat.src/madx/bin/madx-linux64-gnu', cwd=None)

Runs MADX in a subprocess.

madx_wrapper.input_string

MADX input string

madx_wrapper.output_file

If given writes resolved MADX script.

madx_wrapper.log_file

If given writes MADX logging output.

madx_wrapper.madx_path

Path to MADX executable

JWS

Entrypoint JWS