snr_circ_evolving

legwork.snr.snr_circ_evolving(m_1, m_2, f_orb_i, dist, t_obs, n_step, t_merge=None, interpolated_g=None, interpolated_sc=None, **kwargs)

Computes SNR for circular and stationary sources

Parameters:

m_1float/array

Primary mass

m_2float/array

Secondary mass

f_orb_ifloat/array

Initial orbital frequency

distfloat/array

Distance to the source

t_obsfloat

Total duration of the observation

n_stepint

Number of time steps during observation duration

t_mergefloat/array

Time until merger

interpolated_gfunction

A function returned by scipy.interpolate.RectBivariateSpline that computes g(n,e) from Peters (1964). Default is None and uses exact g(n,e) in this case.

interpolated_scfunction

A function returned by scipy.interpolate.interp1d that computes the LISA sensitivity curve. Default is None and uses exact values. Note: take care to ensure that your interpolated function has the same LISA observation time as t_obs and uses the same instrument.

**kwargsvarious

Keyword args are passed to legwork.psd.power_spectral_density(), see those docs for details on possible arguments.

Returns:

snfloat/array

SNR for each binary