PyART.models.ringdown_fits_noncirc¶

Functions to read and use the Noncircular fits from Carullo+23 https://arxiv.org/abs/2309.07228

Mostly taken from Greg’s repo, without all the frills https://github.com/GCArullo/noncircular_BBH_fits

Attributes¶

`fit_dim`
`nu`

Functions¶

`select_fitting_quantities`(dataset_type, quantity_to_fit)
`select_template_model`(dataset_type)
`read_fit_coefficients`(quantity_to_fit, ...[, ...])
`template`(coeffs, fitting_quantities_dict[, template_model])
`eval_fit`(quantity_to_fit, fitting_quantities_dict, ...)	Perform a fit of a given quantity based on a chosen dataset.

Module Contents¶

PyART.models.ringdown_fits_noncirc.fit_dim = 4[source]¶

PyART.models.ringdown_fits_noncirc.select_fitting_quantities(dataset_type, quantity_to_fit)[source]¶

PyART.models.ringdown_fits_noncirc.select_template_model(dataset_type)[source]¶

PyART.models.ringdown_fits_noncirc.read_fit_coefficients(quantity_to_fit, fitting_quantities_string, template_model, fit_dim, catalogs=['RIT', 'SXS', 'ET'], dataset_type=['non-spinning-equal-mass'], coeffs_dir='./noncircular_BBH_fits/Fitting_coefficients')[source]¶

PyART.models.ringdown_fits_noncirc.template(coeffs, fitting_quantities_dict, template_model='rational')[source]¶

PyART.models.ringdown_fits_noncirc.eval_fit(quantity_to_fit, fitting_quantities_dict, fitting_quantities_string, dataset='non-spinning', databases=['RIT', 'SXS', 'ET'], verbose=False)[source]¶

Perform a fit of a given quantity based on a chosen dataset. Choose between:

quantities_to_fit = [‘A_peak22’, ‘omega_peak22’, ‘Mf’, ‘af’]

dataset_types = [‘aligned-spins-equal-mass’,’non-spinning-equal-mass’, ‘non-spinning’]

PyART.models.ringdown_fits_noncirc.nu = 0.25[source]¶