LEGWORK modules and API reference

LEGWORK is composed of 7 different modules, each with a different focus. The diagram below illustrates how each module is connected to the others as well as listing the general purpose of each module. In particular, note that the source module provides a simple interface to the functions in all other modules!

The rest of this page contains the API reference for each individual function in every module, feel free to use the table of contents on the left to easily navigate to the function you need.

legwork.evol Module

Functions using equations from Peters and Mathews (1964) to calculate inspiral times and evolve binary parameters.

Functions

de_dt(e, times, beta, c_0)	Compute eccentricity time derivative
integrate_de_dt(args)	Wrapper that integrates legwork.evol.de_dt() with odeint
evol_circ([t_evol, n_step, timesteps, beta, ...])	Evolve an array of circular binaries for t_evol time
evol_ecc(ecc_i[, t_evol, n_step, timesteps, ...])	Evolve an array of eccentric binaries for t_evol time
get_t_merge_circ([beta, m_1, m_2, a_i, f_orb_i])	Computes the merger time for circular binaries
get_t_merge_ecc(ecc_i[, a_i, f_orb_i, beta, ...])	Computes the merger time for binaries
t_merge_mandel_fit(ecc_i)	A fit to the Peters 1964 merger time equation (5.14) by Ilya Mandel.
evolve_f_orb_circ(f_orb_i, m_c, t_evol[, ...])	Evolve orbital frequency for t_evol time.
check_mass_freq_input([beta, m_1, m_2, a_i, ...])	Check that mass and frequency input is valid
create_timesteps_array(a_i, beta[, ecc_i, ...])	Create an array of timesteps
determine_stationarity(f_orb_i, t_evol, ecc_i)	Determine whether a binary is stationary

Tip

Feeling a bit spun around by all this binary evolution? Check out our tutorial on using functions in the evol module here!

legwork.psd Module

Functions to compute various power spectral densities for sensitivity curves

Functions

load_response_function(f[, fstar])	Load in LISA response function from file
approximate_response_function(f, fstar)	Approximate LISA response function
power_spectral_density(f[, instrument, ...])	Calculates the effective power spectral density for all instruments.
lisa_psd(f[, t_obs, L, approximate_R, ...])	Calculates the effective LISA power spectral density sensitivity curve
tianqin_psd(f[, L, t_obs, approximate_R, ...])	Calculates the effective TianQin power spectral density sensitivity curve
get_confusion_noise(f, model[, t_obs])	Calculate the confusion noise for a particular model
get_confusion_noise_robson19(f[, t_obs])	Calculate the confusion noise using the model from Robson+19 Eq.
get_confusion_noise_huang20(f[, t_obs])	Calculate the confusion noise using the model from Huang+20 Table II.
get_confusion_noise_thiele21(f)	Calculate the confusion noise using the model from Thiele+20 Eq.

legwork.snr Module

Functions to calculate signal-to-noise ratio in four different cases

Functions

snr_circ_stationary(m_c, f_orb, dist, t_obs)	Computes SNR for circular and stationary sources
snr_ecc_stationary(m_c, f_orb, ecc, dist, ...)	Computes SNR for eccentric and stationary sources
snr_circ_evolving(m_1, m_2, f_orb_i, dist, ...)	Computes SNR for circular and stationary sources
snr_ecc_evolving(m_1, m_2, f_orb_i, dist, ...)	Computes SNR for eccentric and evolving sources.

legwork.source Module

A collection of classes for analysing gravitational wave sources

Classes

Source(m_1, m_2, ecc, dist[, n_proc, f_orb, ...])	Class for generic GW sources
Stationary(m_1, m_2, ecc, dist[, n_proc, ...])	Subclass for sources that are stationary
Evolving(m_1, m_2, ecc, dist[, n_proc, ...])	Subclass for sources that are evolving
VerificationBinaries()	Generate a Source class with the LISA verification binaries preloaded.

Class Inheritance Diagram

Tip

Unable to find the source of your issues? Never fear! Check out our tutorial on using the Source class here!

legwork.strain Module

Computes several types of gravitational wave strains

Functions

amplitude_modulation(position, polarisation, ...)	Computes the modulation of the strain due to the orbit averaged response of the detector to the position, polarisation, and inclination of the source using Cornish+03 Eq.42 and Babak+21.
h_0_n(m_c, f_orb, ecc, n, dist[, position, ...])	Computes strain amplitude
h_c_n(m_c, f_orb, ecc, n, dist[, position, ...])	Computes characteristic strain amplitude

Tip

Feeling a little strained trying to parse these docs? Check out our tutorial on using functions in the strain module here!

legwork.utils Module

A collection of miscellaneous utility functions

Functions

chirp_mass(m_1, m_2)	Computes chirp mass of binaries
peters_g(n, e)	Compute g(n, e) from Peters and Mathews (1963) Eq.20
peters_f(e)	f(e) from Peters and Mathews (1963) Eq.17
get_a_from_f_orb(f_orb, m_1, m_2)	Converts orbital frequency to semi-major axis
get_f_orb_from_a(a, m_1, m_2)	Converts semi-major axis to orbital frequency
get_a_from_ecc(ecc, c_0)	Convert eccentricity to semi-major axis
beta(m_1, m_2)	Compute beta defined in Peters and Mathews (1964) Eq.5.9
c_0(a_i, ecc_i)	Computes the c_0 factor in Peters and Mathews (1964) Eq.5.11
fn_dot(m_c, f_orb, e, n)	Rate of change of nth frequency of a binary
ensure_array(*args)	Convert arguments to numpy arrays
D_plus_squared(theta, phi)	Required for the detector responses <F_+^2>, <F_x^2>, <F_+F_x>
D_cross_squared(theta, phi)	Required for the detector responses <F_+^2>, <F_x^2>, <F_+F_x>
D_plus_D_cross(theta, phi)	Required for the detector responses <F_+^2>, <F_x^2>, <F_+F_x>
F_plus_squared(theta, phi, psi)	Compute the auto-correlated detector response for the plus polarization
F_cross_squared(theta, phi, psi)	Compute the auto-correlated detector response for the cross polarization

legwork.visualisation Module

Functions

plot_1D_dist(x[, weights, disttype, ...])	Plot a 1D distribution of x.
plot_2D_dist(x, y[, weights, disttype, fig, ...])	Plot a 2D distribution of x and y
plot_sensitivity_curve([frequency_range, ...])	Plot the LISA sensitivity curve
plot_sources_on_sc(f_dom, snr[, weights, ...])	Overlay stationary sources on the LISA sensitivity curve.

Tip

Not quite sure how things are working vis-à-vis visualisation? Check out our tutorial on using functions in the visualisation module here!