reggae.utils¶

Classes¶

`jaxInterp1D`	Replacement for scipy.interpolate.interp1d in jax
`scalingRelations`	Container for scaling relations
`normal`
`uniform`
`beta`

Functions¶

`readBkgFits`(ID)	Read background samples during prior.
`to_log10`(x, xerr)	Transform to value to log10
`_update_x`(x, a, b, p, a1, b1, afac)	Helper function for evaluating the beta distribution quantile function
`_func_1`(a, b, p)	Helper function for evaluating the beta distribution quantile function
`_func_2`(a, b, p)	Helper function for evaluating the beta distribution quantile function
`_compute_x`(p, a, b)	Helper function for evaluating the beta distribution quantile function
`_betaincinv`(a, b, p)	Helper function for evaluating the beta distribution quantile function

Module Contents¶

reggae.utils.readBkgFits(ID)¶

Read background samples during prior.

Read in the previosly fit background model parameters to use as a prior during sampling.

Paramaters¶

IDstr: Target identifier

Returns¶

outdict: Dictionary containing parameters for three harvey laws.

class reggae.utils.jaxInterp1D(xp, fp, left=None, right=None, period=None)¶

Replacement for scipy.interpolate.interp1d in jax

This is used to initialize a callable object for interpolating.

xp¶

fp¶

left = None¶

right = None¶

period = None¶

__call__(x)¶

Evalute the interpolant on given values

Parameters¶

xjnp.array: jax array of points to evaluate the interpolant at.

Returns¶

interpjnp.array: The interpolated values.

class reggae.utils.scalingRelations¶

Container for scaling relations

This is a helper class which contains methods for the various scaling relations.

__init_()¶

dnuScale(nu, gamma=0.0, p=[0.79101684, -0.63285292])¶

Compute dnu from numax

Computes an estimate of the large separation from a given value of numax, assuming the two parameters scale as a polynomial in log(dnu) and log(numax).

The default is a linear function in log(dnu) and log(numax), estimated based on a polynomial fit performed on a set of main-sequence and sub- giant stars in the literature.

The output may be scaled by a factor gamma, e.g., for setting credible intervals.

Parameters¶

nufloat, array: Value(s) at which to compute dnu, assuming nu corresponds to numax. (muHz)
gammafloat: Scaling factor to apply.
parray-like, optional: Polynomial coefficients to use for log(dnu), log(numax), starting with the coefficient of the Nth order term and ending at the bias term.

Returns¶

dnufloat, array: Estimate of the large separation dnu

envWidth(numax)¶

Scaling relation for the envelope width

Computes the the estimated full width at half maximum of the p-mode envelope based on numax.

Parameters¶

numaxfloat: Frequency of maximum power of the p-mode envelope.

Returns¶

widthfloat: Envelope width in muHz

reggae.utils.to_log10(x, xerr)¶

Transform to value to log10

Takes a value and related uncertainty and converts them to logscale. Approximate.

Parameters¶

xfloat: Value to transform to logscale
xerrfloat: Value uncertainty

Returns¶

logvallist: logscaled value and uncertainty

class reggae.utils.normal(**kwargs)¶

norm¶

lognorm¶

fac¶

pdf(x, norm=True)¶

Evaluate the probability density function

Returns the probability density of the normal distribution at x. The distribution is normalized to unit integral by default so that it may be used as a PDF.

In some cases the normalization is not necessary, and since it’s marginally slower it may as well be left out.

Parameters¶

xarray: Support for the probability density.

Returns¶

yarray: The probability density at x.

logpdf(x, norm=True)¶

Evaluate the log-probability density function

Returns the probability density of the normal distribution at x. The distribution is normalized to unit integral by default so that it may be used as a PDF.

In some cases the normalization is not necessary, and since it’s marginally slower it may as well be left out.

Parameters¶

xarray: Support for the probability density.

Returns¶

yarray: The probability density at x.

cdf(x)¶

Evaluate the cummulative distribution function

Returns cummulative probability density of the normal distribution at x.

Parameters¶

xarray: Support for the probability density.

Returns¶

yarray: The probability density at x.

ppf(y)¶

Evaluate the quantile function

Returns support corresponding to a given quantile between 0 and 1 for the normal distribution.

Parameters¶

yarray: Quantile value.

Returns¶

xarray: The support for the PDF corresponding to quantile value y.

class reggae.utils.uniform(**kwargs)¶

a¶

b¶

mu¶

pdf(x)¶

Evaluate the probability density function

Returns the probability density of the uniform distribution at x. The distribution is normalized to unit integral by default so that it may be used as a PDF.

Parameters¶

xarray: Support for the probability density.

Returns¶

yarray: The probability density at x.

logpdf(x)¶

Evaluate the log-probability density function

Returns the probability density of the uniform distribution at x. The distribution is normalized to unit integral by default so that it may be used as a PDF.

In some cases the normalization is not necessary, and since it’s marginally slower it may as well be left out.

Parameters¶

xarray: Support for the probability density.

Returns¶

yarray: The probability density at x.

cdf(x)¶

Evaluate the cummulative distribution function

Returns cummulative probability density of the uniform distribution at x.

Parameters¶

xarray: Support for the probability density.

Returns¶

yarray: The probability density at x.

ppf(y)¶

Evaluate the quantile function

Returns support corresponding to a given quantile between 0 and 1 for the uniform distribution.

Parameters¶

yarray: Quantile value.

Returns¶

xarray: The support for the PDF corresponding to quantile value y.

class reggae.utils.beta(**kwargs)¶

loc = 0¶

scale = 1¶

fac¶

logfac¶

am1¶

bm1¶

_transformx(x)¶

Transform x

Translates and scales the input x to the unit interval according to the loc and scale parameters.

Parameters¶

xarray-like: Input support for the probability density.

Returns¶

_xarray-like: x translated and scaled to the range 0 to 1.

_inverse_transform(x)¶

Invert transformation on x

Translates and scales the input x to the original interval according to the loc and scale parametrs.

Parameters¶

xarray-like: Input support for the probability on the interval 0 to 1.

Returns¶

_xarray-like: x translated and scaled from the range 0 to 1 to the loc and scale.

pdf(x, norm=True)¶

Return PDF

Returns the beta distribution at x. The distribution is normalized to unit integral by default so that it may be used as a PDF.

In some cases the normalization is not necessary, and since it’s marginally slower it may as well be left out.

Parameters¶

xarray: Input support for the probability density.
normbool, optional: If true, returns the normalized beta distribution. The default is True.

Returns¶

yarray: The value of the beta distribution at x.

logpdf(x, norm=True)¶

Return log-PDF

Returns the log of the beta distribution at x. The distribution is normalized to unit integral (in linear units) by default so that it may be used as a PDF.

In some cases the normalization is not necessary, and since it’s marginally slower it may as well be left out.

Parameters¶

xarray: Input support for the probability density.
normbool, optional: If true, returns the normalized beta distribution. The default is True.

Returns¶

yarray: The value of the logarithm of the beta distribution at x.

cdf(x)¶

Evaluate the cummulative distribution function

Returns cummulative probability density of the uniform distribution at x.

Parameters¶

xarray: Support for the probability density.

Returns¶

yarray: The probability density at x.

ppf(y)¶

Evaluate the quantile function

Returns support corresponding to a given quantile between 0 and 1 for the beta distribution.

Parameters¶

yarray: Quantile value.

Returns¶

xarray: The support for the PDF corresponding to quantile value y.

reggae.utils._update_x(x, a, b, p, a1, b1, afac)¶: Helper function for evaluating the beta distribution quantile function

reggae.utils._func_1(a, b, p)¶: Helper function for evaluating the beta distribution quantile function

reggae.utils._func_2(a, b, p)¶: Helper function for evaluating the beta distribution quantile function

reggae.utils._compute_x(p, a, b)¶: Helper function for evaluating the beta distribution quantile function

reggae.utils._betaincinv(a, b, p)¶: Helper function for evaluating the beta distribution quantile function