Main functions

gwmemory.gwmemory.frequency_domain_memory(model: str = None, q: float = None, total_mass: float = None, spin_1: Tuple[float, float, float] = None, spin_2: Tuple[float, float, float] = None, distance: float = None, inc: float = None, phase: float = None, **kwargs) → Tuple[dict, ndarray]

Calculate the frequency domain memory waveform according to __reference__.

Parameters:

model: str: Name of the model, this is used to identify waveform approximant, e.g., NRSur7dq2, IMRPhenomD, MWM, etc.
q: float: Mass ratio of the binary being considered.
total_mass: float: Total mass of the binary being considered in solar units.
spin_1: array: Dimensionless spin vector of the more massive black hole.
spin_2: array: Dimensionless spin vector of the less massive black hole.
distance: float: Distance to the binary in MPC.
inc: float: Inclination of the binary to the line of sight. If not provided, spherical harmonic modes will be returned.
phase: float: Binary phase at coalescence. If not provided, spherical harmonic modes will be returned.
kwargs: dict: Additional model-specific keyword arguments.

Returns:

frequency_domain_strain: dict: Memory frequency series, either in spherical harmonic modes or plus/cross polarisations.
frequencies: array-like: Frequency series corresponding to the memory waveform.

gwmemory.gwmemory.time_domain_memory(model: str = None, h_lm: dict = None, times: ndarray = None, q: float = None, total_mass: float = None, spin_1: Tuple[float, float, float] = None, spin_2: Tuple[float, float, float] = None, distance: float = None, inc: float = None, phase: float = None, **kwargs) → Tuple[dict, ndarray]

Calculate the time domain memory waveform according to __reference__.

Example usage:

Using NR surrogate waveform __reference__ for an edge-on non-spinning, equal-mass, binary at a distance of 400 MPC.

h_mem, times = time_domain_memory(model=’NRSur7dq2’, q=1, total_mass=60,: distance=400, inc=np.pi/2, phase=0)

Using an EOBNR waveform __reference__ for an edge-on non-spinning, equal-mass, binary at a distance of 400 MPC.

h_mem, times = time_domain_memory(model=’SEOBNRv4’, q=1, total_mass=60,: distance=400, inc=np.pi/2, phase=0)

Using the minimal waveform model __reference__ for an edge-on non-spinning, equal-mass, binary at a distance of 400 MPC.

h_mem, times = time_domain_memory(model=’MWM’, q=1, total_mass=60,: distance=400, inc=np.pi/2, phase=0)

Using a pre-computed spherical harmonic decomposed waveform for an edge-on non-spinning, equal-mass, binary at a distance of 400 MPC.

h_mem, times = time_domain_memory(h_lm=h_lm, times=times, distance=400,: inc=np.pi/2, phase=0)

Parameters:

model: str: Name of the model, this is used to identify waveform approximant, e.g., NRSur7dq2, IMRPhenomD, MWM, etc.
h_lm: dict: Spin weighted spherical harmonic decomposed time series. If this is specified these polarisations will be used.
times: array: time series corresponding to the h_lm.
q: float: Mass ratio of the binary being considered.
total_mass: float: Total mass of the binary being considered in solar units.
spin_1: array: Dimensionless spin vector of the more massive black hole.
spin_2: array: Dimensionless spin vector of the less massive black hole.
distance: float: Distance to the binary in MPC.
inc: float: Inclination of the binary to the line of sight. If not provided, spherical harmonic modes will be returned.
phase: float: Binary phase as coalescence. If not provided, spherical harmonic modes will be returned.
kwargs: dict: Additional model-specific keyword arguments.

Returns:

h_mem, dict: Memory time series, either in spherical harmonic modes or plus/cross polarisations.
times, array: Time series corresponding to the memory waveform.