fast_lisa_subtraction.simulation.catalog module

class fast_lisa_subtraction.simulation.catalog.SourceCatalog(catalog_path=None, catalog_df=None, use_gpu=True, verbose=True, **gbgpu_kwargs)[source]

Bases: object

Generate GW waveforms from a Galactic binary catalogue.

Parameters:

  • catalog_path (str or os.PathLike, optional) – Path to the catalogue file. Used if catalog_df is not provided.

  • catalog_df (pandas.DataFrame, optional) – In-memory catalogue. If provided, catalog_path is ignored.

  • use_gpu (bool, optional) – If True, attempt to use CuPy for GPU acceleration.

  • verbose (bool, optional) – If True, enable progress and status logging.

  • **gbgpu_kwargs (dict) – Additional keyword arguments forwarded to gbgpu.gbgpu.GBGPU.

property Nbinaries

Number of binaries in the catalogue.

Returns:

Number of rows in the catalogue.

Return type:

int

static compute_total_snr(AET, PSD, f, wvf_freqs)[source]

Compute the network SNR in A/E/T channels.

Parameters:

  • AET (dict) – Dictionary containing waveforms in the A/E/T channels.

  • PSD (dict) – Dictionary containing noise PSDs for the A/E/T channels.

  • f (numpy.ndarray or cupy.ndarray) – Frequency array for the noise PSD.

  • wvf_freqs (numpy.ndarray or cupy.ndarray) – Batched frequency arrays for the waveforms.

Returns:

Total network SNR for each waveform.

Return type:

numpy.ndarray or cupy.ndarray

Notes

The per-channel SNR is computed as

\[\mathrm{SNR}_X = 4\,\Delta f \sum_k \frac{|h_X(f_k)|^2}{S_X(f_k)},\]

and the network SNR is

\[\mathrm{SNR} = \sqrt{\mathrm{SNR}_A + \mathrm{SNR}_E + \mathrm{SNR}_T}.\]
generate_catalogue(Nbatch=10000, Nmax_binaries=None, Tobs=1, AET=True, outdir=None, save=True, snr_threshold=None, noise_model='SciRDv1', duty_cycle=1, **gbgpu_kwargs)[source]

Generate waveforms for the full catalogue.

Parameters:

  • Nbatch (int, optional) – Number of binaries per batch.

  • Nmax_binaries (int or None, optional) – Maximum number of binaries to generate. If None, the full catalogue is used; otherwise a random subset is sampled.

  • Tobs (float, optional) – Observation time in years.

  • AET (bool, optional) – If True, generate A/E/T channels; otherwise X/Y/Z.

  • outdir (str or os.PathLike or None, optional) – Output directory. If None, uses the catalogue directory.

  • save (bool, optional) – If True, save waveforms and catalogue to HDF5.

  • snr_threshold (float or None, optional) – Threshold SNR for detectability cuts. If None, no cut is applied.

  • noise_model (str, optional) – Noise model name passed to get_noise_model.

  • duty_cycle (float, optional) – Detector duty cycle.

  • **gbgpu_kwargs (dict) – Additional arguments forwarded to GBGPU.run_wave.

Returns:

Dictionary containing the sum of generated waveforms for each TDI channel along with metadata.

Return type:

dict

generate_template(params, dt, Tobs, channels, **gbgpu_kwargs)[source]

Generate waveforms for a batch of binaries.

Parameters:

  • params (numpy.ndarray or cupy.ndarray) – Array containing the parameters of the binaries.

  • dt (float) – Data cadence in seconds.

  • Tobs (float) – Observation time in seconds.

  • channels (list of str) – TDI channels to generate.

  • **gbgpu_kwargs (dict) – Additional arguments forwarded to GBGPU.run_wave.

Returns:

(batch_freqs, ch_wvfs) where batch_freqs is the frequency array for each waveform and ch_wvfs is a dictionary of channel waveforms.

Return type:

tuple

static get_batch_params(cat_df, index)[source]

Extract parameter arrays for a batch of binaries.

Parameters:

  • cat_df (pandas.DataFrame) – Catalogue containing binary parameters.

  • index (array-like) – Indices of the rows to extract.

Returns:

Array of shape (9, N) with parameters ordered as [amp, f0, fdot, fddot, phi0, iota, psi, lam, beta_sky].

Return type:

numpy.ndarray

static get_frequency_array(Tobs, dt, min_freq=None, max_freq=None)[source]

Construct a frequency array for a given observation time and cadence.

Parameters:

  • Tobs (float) – Observation time in seconds.

  • dt (float) – Sampling cadence in seconds.

  • min_freq (float or None, optional) – Minimum frequency to include.

  • max_freq (float or None, optional) – Maximum frequency to include.

Returns:

Frequency array spanning the requested range.

Return type:

numpy.ndarray or cupy.ndarray

save_catalogue(outdir, cat, sum_wvf, Nbinaries)[source]

Save generated waveform and source catalogues to HDF5.

Parameters:

  • outdir (str or os.PathLike or None) – Output directory. If None, the catalogue directory is used.

  • cat (pandas.DataFrame) – Source catalogue to save.

  • sum_wvf (dict) – Summed waveform dictionary, including metadata entries.

  • Nbinaries (int) – Total number of binaries used in the simulation.

Return type:

None