bilby.core.prior.analytical.HalfNormal

class bilby.core.prior.analytical.HalfNormal(sigma, name=None, latex_label=None, unit=None, boundary=None)

Bases: HalfGaussian

A synonym for the HalfGaussian distribution.

__init__(sigma, name=None, latex_label=None, unit=None, boundary=None)

A Gaussian with its mode at zero, and truncated to only be positive.

Parameters:

sigma: float

See superclass

name: str

See superclass

latex_label: str

See superclass

unit: str

See superclass

boundary: str

See superclass

__call__()

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

__init__(sigma[, name, latex_label, unit, ...])	A Gaussian with its mode at zero, and truncated to only be positive.
cdf(val)	Generic method to calculate CDF, can be overwritten in subclass
from_json(dct)
from_repr(string)	Generate the prior from its __repr__
get_instantiation_dict()
is_in_prior_range(val)	Returns True if val is in the prior boundaries, zero otherwise
ln_prob(val)	Return the prior ln probability of val, this should be overwritten
prob(val)	Return the prior probability of val.
rescale(val)	'Rescale' a sample from the unit line element to the appropriate truncated Gaussian prior.
sample([size])	Draw a sample from the prior
to_json()

Attributes

boundary
is_fixed	Returns True if the prior is fixed and should not be used in the sampler.
latex_label	Latex label that can be used for plots.
latex_label_with_unit	If a unit is specified, returns a string of the latex label and unit
maximum
minimum
normalisation	Calculates the proper normalisation of the truncated Gaussian
unit
width

cdf(val)

Generic method to calculate CDF, can be overwritten in subclass

classmethod from_repr(string)

Generate the prior from its __repr__

property is_fixed

Returns True if the prior is fixed and should not be used in the sampler. Does this by checking if this instance is an instance of DeltaFunction.

Returns:

bool: Whether it’s fixed or not!

is_in_prior_range(val)

Returns True if val is in the prior boundaries, zero otherwise

Parameters:

val: Union[float, int, array_like]

Returns:

np.nan

property latex_label

Latex label that can be used for plots.

Draws from a set of default labels if no label is given

Returns:

str: A latex representation for this prior

property latex_label_with_unit

If a unit is specified, returns a string of the latex label and unit

ln_prob(val)

Return the prior ln probability of val, this should be overwritten

Parameters:

val: Union[float, int, array_like]

Returns:

np.nan

property normalisation

Calculates the proper normalisation of the truncated Gaussian

Returns:

float: Proper normalisation of the truncated Gaussian

prob(val)

Return the prior probability of val.

Parameters:

val: Union[float, int, array_like]

Returns:

float: Prior probability of val

rescale(val)

‘Rescale’ a sample from the unit line element to the appropriate truncated Gaussian prior.

This maps to the inverse CDF. This has been analytically solved for this case.

sample(size=None)

Draw a sample from the prior

Parameters:

size: int or tuple of ints, optional

See numpy.random.uniform docs

Returns:

float: A random number between 0 and 1, rescaled to match the distribution of this Prior