plot_sources_on_sc

legwork.visualisation.plot_sources_on_sc(f_dom, snr, weights=None, snr_cutoff=0, t_obs='auto', instrument='LISA', custom_psd=None, L='auto', approximate_R=False, confusion_noise='auto', fig=None, ax=None, show=True, sc_vis_settings={}, **kwargs)

Overlay stationary sources on the LISA sensitivity curve.

Each source is plotted at its max snr harmonic frequency such that that its height above the curve is equal to it signal-to-noise ratio. For circular sources this is frequency is simply twice the orbital frequency.

Parameters

f_domfloat/array

Dominant harmonic frequency (f_orb * n_dom where n_dom is the harmonic with the maximum snr). You may find the legwork.source.Source.max_snr_harmonic() attribute useful (which gets populated) after legwork.source.Source.get_snr().

snrfloat/array

Signal-to-noise ratio

weightsfloat/array, optional, default=None

Statistical weights for each source, default is equal weights

snr_cutofffloat

SNR above which to plot binaries (default is 0 such that all sources are plotted)

instrument: {{ `LISA`, `TianQin`, `custom` }}

Instrument to use. LISA is used by default. Choosing custom uses custom_psd to compute PSD.

custom_psdfunction

Custom function for computing the PSD. Must take the same arguments as legwork.psd.lisa_psd() even if it ignores some.

t_obsfloat

Observation time (default auto)

Lfloat

Arm length in metres

approximate_Rboolean

Whether to approximate the response function (default: no)

confusion_noisevarious

Galactic confusion noise. Acceptable inputs are either one of the values listed in legwork.psd.get_confusion_noise(), “auto” (automatically selects confusion noise based on instrument - ‘robson19’ if LISA and ‘huang20’ if TianQin), or a custom function that gives the confusion noise at each frequency for a given mission length where it would be called by running noise(f, t_obs) and return a value with units of inverse Hertz

fig: `matplotlib Figure`

A figure on which to plot the distribution. Both ax and fig must be supplied for either to be used

ax: `matplotlib Axis`

An axis on which to plot the distribution. Both ax and fig must be supplied for either to be used

showboolean

Whether to immediately show the plot or only return the Figure and Axis

sc_vis_settingsdict

Dictionary of parameters to pass to plot_sensitivity_curve(), e.g. {“y_quantity”: “h_c”} will plot characteristic strain instead of ASD

**kwargsvarious

This function is a wrapper on legwork.visualisation.plot_2D_dist() and each kwarg is passed directly to this function. For example, you can write disttype=”kde” for a kde density plot instead of a scatter plot.

Returns

figmatplotlib Figure

The figure on which the distribution is plotted

axmatplotlib Axis

The axis on which the distribution is plotted