SolarOscillatorKernel#

class gadfly.SolarOscillatorKernel(texp=None, delta=None, bandpass=None, name=None)[source]#

Bases: StellarOscillatorKernel

Like a StellarOscillatorKernel, but initialized with the default solar SOHO VIRGO/PMO6 kernel hyperparameters. The hyperparameters are initialized with the Hyperparameters class method for_star() assuming exactly solar mass, radius, temperature, and luminosity.

The primary difference with StellarOscillatorKernel is that the user need not provide Hyperparameters().

SolarOscillatorKernel inherits from TermConvolution and StellarOscillatorKernel.

Parameters:: texp (Quantity) – Exposure time, convertible to inverse microhertz.

Attributes Summary

terms

Methods Summary

`dot`(x, diag, y)	Apply a matrix-vector or matrix-matrix product
`get_celerite_matrices`(x, diag, *[, c, a, U, V])	Get the matrices needed to solve the celerite system
`get_coefficients`()	Compute and return the coefficients for the celerite model
`get_psd`(omega)	Compute the value of the power spectral density for this process
`get_value`(tau0)	Compute the value of the kernel as a function of lag
`plot`(**kwargs)	Plot a power spectrum.
`to_dense`(x, diag)	Evaluate the dense covariance matrix for this term

Attributes Documentation

terms#

Methods Documentation

dot(x, diag, y)#

Apply a matrix-vector or matrix-matrix product

Parameters:

x (shape[N]) – The independent coordinates of the data.
diag (shape[N]) – The diagonal variance of the system.
y (shape[N] or shape[N, K]) – The target of vector or matrix for this operation.

get_celerite_matrices(x, diag, *, c=None, a=None, U=None, V=None)#

Get the matrices needed to solve the celerite system

Pre-allocated arrays can be provided to the Python interface to be re-used for multiple evaluations.

Note

In-place operations are not supported by the modeling extensions.

Parameters:

x (shape[N]) – The independent coordinates of the data.
diag (shape[N]) – The diagonal variance of the system.
a (shape[N], optional) – The diagonal of the A matrix.
U (shape[N, J], optional) – The first low-rank matrix.
V (shape[N, J], optional) – The second low-rank matrix.
P (shape[N-1, J], optional) – The regularization matrix used for numerical stability.

Raises:

ValueError – When the inputs are not valid.

get_coefficients()#

Compute and return the coefficients for the celerite model

This should return a 6 element tuple with the following entries:

(ar, cr, ac, bc, cc, dc)

Note

All of the returned objects must be arrays, even if they only have one element.

get_psd(omega)#

Compute the value of the power spectral density for this process

Parameters:: omega (shape[...]) – The (angular) frequencies where the power should be evaluated.

get_value(tau0)#

Compute the value of the kernel as a function of lag

Parameters:: tau (shape[...]) – The lags where the kernel should be evaluated.

plot(**kwargs)#

Plot a power spectrum.

Requires matplotlib.

Parameters:

ax (Axes)
kernel (None or subclass of Term)
obs (PowerSpectrum)
freq (Quantity)
figsize (list of floats)
n_samples (int)
p_mode_inset (bool)
legend (bool)
scaling_low_freq (str)
scaling_p_mode (str)
inset_xlim (list of floats)
inset_ylim (list of floats)
title (str)
label_inset (str)
label_obs (str)
label_kernel (str)
kernel_kwargs (dict)
obs_kwargs (dict)
inset_kwargs (dict)
create_new_figure (bool)
inset_bounds (list)

Returns:

fig (Figure)
ax (Axes)

to_dense(x, diag)#

Evaluate the dense covariance matrix for this term

Parameters:

x (shape[N]) – The independent coordinates of the data.
diag (shape[N]) – The diagonal variance of the system.

SolarOscillatorKernel

Contents

SolarOscillatorKernel#