dingo.gw.noise.synthetic package
Submodules
dingo.gw.noise.synthetic.asd_parameterization module
- dingo.gw.noise.synthetic.asd_parameterization.curve_fit(data, std, delta_f=None)
Fit a Lorentzian to the PSD.
- Parameters:
data (dict) – Dictionary containing the PSD, broadband noise, and frequency grid.
std (float) – Standard deviation of the Gaussian noise.
delta_f (float) – Truncation parameter for Lorentzians. Set to None if non-positive value is passed.
- dingo.gw.noise.synthetic.asd_parameterization.fit_broadband_noise(domain, psd, num_spline_positions, sigma, f_min=20)
Fit a spline to the broadband noise of a PSD.
- Parameters:
domain (Domain) – Domain object containing the frequency grid.
psd (array_like) – PSD to be parameterized.
num_spline_positions (int) – Number of spline positions.
sigma (float) – Standard deviation of the Gaussian noise used for the spline fit.
f_min (float, optional) – position of the first node for the spline fi
- dingo.gw.noise.synthetic.asd_parameterization.fit_spectral(frequencies, psd, broadband_noise, num_spectral_segments, sigma, delta_f)
Fit Lorentzians to the spectral features of a PSD.
- Parameters:
frequencies (array_like) – Frequency grid.
psd (array_like) – PSD to be parameterized.
broadband_noise (array_like) – Broadband noise of the PSD.
num_spectral_segments (int) – Number of spectral segments.
sigma (float) – Standard deviation of the Gaussian noise used for the spline fit.
delta_f (float) – Truncation parameter for Lorentzians. Set to None if non-positive value is passed.
- dingo.gw.noise.synthetic.asd_parameterization.parameterize_asd_dataset(real_dataset, parameterization_settings, num_processes, verbose)
Parameterize a dataset of ASDs using a spline fit to the broadband noise and Lorentzians for the spectral features.
- Parameters:
real_dataset (ASDDataset) – Dataset containing the ASDs to be parameterized.
parameterization_settings (dict) – Dictionary containing the settings for the parameterization.
num_processes (int) – Number of processes to use for parallelization.
verbose (bool) – If True, print progress bars.
- dingo.gw.noise.synthetic.asd_parameterization.parameterize_asds_parallel(asds, domain, parameterization_settings, pool=None, verbose=False)
Helper function to be called for parallel ASD parameterization.
- Parameters:
asds (array_like) – Array containing the ASDs to be parameterized.
domain (Domain) – Domain object containing the frequency grid.
parameterization_settings (dict) – Dictionary containing the settings for the parameterization.
pool (Pool, optional) – Pool object for parallelization. If None, the function is not parallelized.
verbose (bool) – If True, print progress bars.
- dingo.gw.noise.synthetic.asd_parameterization.parameterize_single_psd(real_psd, domain, parameterization_settings)
Parameterize a single ASD using a spline fit to the broadband noise and Lorentzians for the spectral features.
- Parameters:
real_psd (array_like) – PSD to be parameterized.
domain (Domain) – Domain object containing the frequency grid.
parameterization_settings (dict) – Dictionary containing the settings for the parameterization.
dingo.gw.noise.synthetic.asd_sampling module
- class dingo.gw.noise.synthetic.asd_sampling.KDE(parameter_dict, sampling_settings)
Bases:
objectKernel Density Estimation (KDE) class for sampling ASDs.
- Parameters:
parameter_dict (dict) – Dictionary containing the parameters of the ASDs used for fitting the synthetic distribution.
sampling_settings (dict) – Dictionary containing the settings for the sampling.
- fit(weights=None)
Fit the KDEs to the parameters saved in ‘self.parameter_dict’. :param weights: Weights for the KDEs. If None, all weights are set to 1. :type weights: array_like, optional
- dingo.gw.noise.synthetic.asd_sampling.get_rescaling_params(filenames, parameterization_settings)
Get the parameters of the ASDs that are used for rescaling. :param filenames: Dictionary containing the paths to the ASD files. :type filenames: dict :param parameterization_settings: Dictionary containing the settings for the parameterization. :type parameterization_settings: dict
dingo.gw.noise.synthetic.generate_dataset module
- dingo.gw.noise.synthetic.generate_dataset.generate_dataset(real_dataset, settings: Dict, num_samples, num_processes: int, verbose: bool)
Generate a synthetic ASD dataset from an existing dataset of real ASDs.
- Parameters:
real_dataset (ASDDataset) – Existing dataset of real ASDs.
settings (dict) – Dictionary containing the settings for the parameterization and sampling.
num_processes (int) – Number of processes to use in pool for parallel parameterization.
verbose (bool) – Whether to print progress information.
- dingo.gw.noise.synthetic.generate_dataset.main()
- dingo.gw.noise.synthetic.generate_dataset.parse_args()
dingo.gw.noise.synthetic.utils module
- dingo.gw.noise.synthetic.utils.get_index_for_elem(arr, elem)
- dingo.gw.noise.synthetic.utils.lorentzian_eval(x, f0, A, Q, delta_f=None)
Evaluates a Lorentzian function at the given frequencies. :param x: Frequencies at which the Lorentzian is evaluated. :type x: array_like :param f0: Center frequency of the Lorentzian. :type f0: float :param A: Amplitude of the Lorentzian. :type A: float :param Q: Parameter determining the width of the Lorentzian :type Q: float :param delta_f: If given, the Lorentzian is truncated :type delta_f: float, optional
- Return type:
array_like
- dingo.gw.noise.synthetic.utils.reconstruct_psds_from_parameters(parameters_dict, domain, parameterization_settings)
Reconstructs the PSDs from the parameters. :param parameters_dict: Dictionary containing the parameters of the PSDs. :type parameters_dict: dict :param domain: Domain object containing the frequencies at which the PSDs are evaluated. :type domain: dingo.gw.noise.domain.Domain :param parameterization_settings: Dictionary containing the settings for the parameterization. :type parameterization_settings: dict
- Return type:
array_like