LASSO correlation

The least absolute shrinkage and selection operator (LASSO) is a regression technique using machine learning that tracks slow correlations among a large collection of time-domain data streams. For gravitational-wave detector characterisation, this technique is used to find correlations between environmental sensors and any noise in the primary strain channel.

The gwdetchar.lasso module provides the following functions:

find_outliers(ts[, N]) Find outliers within a TimeSeries
remove_outliers(ts[, N]) Find and remove outliers within a TimeSeries
fit(data, target[, alpha]) Fit some data to the target using a Lasso model
find_alpha(data, target) Find the best alpha value to use for the given data
remove_flat(tsdict) Remove flat timeseries from a TimeSeriesDict
remove_bad(tsdict) Remove data that cannot be scaled from a TimeSeriesDict

The gwdetchar.lasso.plot module also provides functions for efficiently writing plots of LASSO data products:


Command-line utility


This utility requires authentication with LIGO.ORG credentials for archived frame data access.


The gwdetchar-lasso-correlation tool searches for long, slow correlations between one channel identified as a primary (typically gravitational-wave strain) and several other (typically thousands of) auxiliary channels. For a full explanation of the available command-line arguments and options, you can run

$ gwdetchar-lasso-correlation --help
Traceback (most recent call last):
  File "/home/docs/checkouts/", line 45, in <module>
    from gwdetchar.lasso import plot as gwplot
  File "/home/docs/checkouts/", line 39, in <module>
    tex_settings = get_gwpy_tex_settings()
  File "/home/docs/checkouts/", line 52, in get_gwpy_tex_settings
    rcParams.get('text.latex.preamble', []) + GWPY_TEX_MACROS),
TypeError: can only concatenate str (not "list") to str