reggae.theta¶
Rather than pass around raw numpy arrays, Reggae uses dataclass objects to characterise the various θ parameters entering into the model. One is for the background & asymptotic p-mode parameters (θ_asy) inherited from pbjam, and one is for the dipole modes (θ_reggae) which Reggae as a tool is intended to assist in fitting.
Classes¶
Parameters for Reggae (asymptotic description of l = 1 modes) |
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Parameters for the asymptotic description of l = 0,2 modes |
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Parameters for the Harvey-like background terms of the model |
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Parameters to compare with additional observational parameters |
Module Contents¶
- class reggae.theta.ThetaReg¶
Parameters for Reggae (asymptotic description of l = 1 modes)
If theta is a numpy array, generate an instance as theta = ThetaReg(*θ)
- dPi0: float¶
- p_L: float¶
- p_D: float¶
- epsilon_g: float¶
- log_omega_core: float¶
- d01: float¶
- alpha_g: float¶
- inclination: float¶
- log_omega_env: float¶
- dims = 9¶
- asarray()¶
- bounds¶
- normalisation = 1¶
- beta12¶
- normal¶
- static prior_transform(u, bounds=None)¶
Prior transform for inverse sampling
Evaluates the ppf (quantile function) given a set of quantile values u drawn from the n-dimensional hypercube.
Parameters¶
- unp.array
Array of values between 0 and 1 drawn uniformly from the n-dimensional hypercube.
- boundsnp.array
Array of bounds for the distributions in case they need to be truncated.
- static inv_prior_transform(θ, bounds=None)¶
Inverse prior transform
Evaluates the point u in the unit hypercube associated with an array of values describing a θ_reg object.
Parameters¶
- θnp.array
Array of values for the fields of θ_reg.
- boundsnp.array
Array of bounds for the distributions in case they need to be truncated.
- class reggae.theta.ThetaAsy¶
Parameters for the asymptotic description of l = 0,2 modes
- log_numax: float¶
- log_dnu: float¶
- eps: float¶
- log_d02: float¶
- log_alpha: float¶
- log_hmax: float¶
- log_env_width: float¶
- log_mode_width: float¶
- dims = 8¶
- nmax()¶
Compute nmax
- n_p(n_orders)¶
Build array of radial orders
- nu_0(n_orders)¶
Compute frequencies of the radial orders
- asarray()¶