pygwtf.models.base

Classes

AnalyticModel

AnalyticModel is the base class for all analytic waveform models in pygwtf.

Module Contents

class pygwtf.models.base.AnalyticModel(backend: str | pygwtf.backend.Backend | None = None)

Bases: abc.ABC

AnalyticModel is the base class for all analytic waveform models in pygwtf.

The model class is responsible for

• Defining the input parameters of the model

• Computing any derived parameters (such as the time to coalescence) from the input parameters

• Providing access to the numba-compiled functions required for the waveform generation kernel

See the docstrings for each required method for more details.

property parameters: list[str]

Abstractmethod:

Returns a list of the input parameters required for this model. The order of the parameters in this list must match the order of the parameters expected by the numba-compiled functions.

property num_parameters: int

Returns the number of input parameters required for this model. This is inferred from the length of the list returned by the parameters method.

property derived_parameters: list[str]

Returns a list of any derived parameters that are computed from the input parameters. The order of the parameters in this list must match the order of the derived parameters expected by the numba-compiled functions. These parameters will be appended to the input parameters in the parameter array passed to the numba-compiled functions.

property num_derived_parameters: int

Returns the number of derived parameters computed by this model. This is inferred from the length of the list returned by the derived_parameters method.

compute_derived_parameters(parameters: numpy.typing.ArrayLike) → None

Computes any derived parameters from the input parameters. The input parameter array will have shape (n_sources, n_parameters + n_derived_parameters) and the derived parameters should be computed in place and stored in the appropriate columns of this array (i.e. the columns after the input parameters). The default implementation of this method does nothing, as not all models will have derived parameters.

property amplitude_function: Callable

Abstractmethod:

The numba-compiled function that computes the amplitude of the waveform at a given time, frequency and frequency derivative. This function should have the signature amplitude(t, f, fdot, parameters) where parameters is a 1D array containing the input parameters

for a single source (in the order specified by the parameters method).

property phi_f_fdot_function: Callable

Abstractmethod:

The numba-compiled function that computes the phase, frequency and frequency derivative of the waveform at a given time. This function should have the signature phi_f_fdot(t, parameters) where parameters is a 1D array containing the input parameters

for a single source (in the order specified by the parameters method).

property get_TT_polarisations_function: Callable | None

The numba-compiled function that computes the plus and cross polarisations of the waveform at a given time, frequency and frequency derivative. This function is only required for non-TDI waveform generation. This function should have the signature hplus_hcross(hlm, parameters) where parameters is a 1D array containing the input parameters

for a single source (in the order specified by the parameters method) and hlm is the multipole.

The default implementation of this method returns None for flexibility, as not all models will require this function.

get_time_bounds(parameters: numpy.typing.ArrayLike, frequency_band: tuple[float, float]) → tuple[numpy.typing.ArrayLike, numpy.typing.ArrayLike] | None

Returns the start and end times of the waveform for each source. For non-stationary signals (e.g. compact binary coalescences), this method should be overridden to return the appropriate time bounds based on the input parameters. The returned start and end times should be 1D arrays of shape (n_sources,) containing the start and end times for each source, or None

The default implementation of this method returns None for all sources, i.e. the signal remains in-band for all relevant times.

Parameters

parameters: ArrayLike

A 2D array of shape (n_sources, n_parameters + n_derived_parameters)

frequency_band: tuple[float]

A tuple containing the lower and upper bounds of the frequency band of interest.

Returns

tuple[ArrayLike, ArrayLike] | None

A tuple containing the start and end times of the waveform for each source, or None if the signal is assumed to be in-band for all relevant times.