bilby.gw.prior.UniformInComponentsChirpMass

class bilby.gw.prior.UniformInComponentsChirpMass(minimum, maximum, name='chirp_mass', latex_label='$\\mathcal{M}$', unit=None, boundary=None)

Bases: PowerLaw

Prior distribution for chirp mass which is uniform in component masses.

This is useful when chirp mass and mass ratio are sampled while the prior is uniform in component masses.

\[p({\cal M}) \propto {\cal M}\]

Notes

This prior is intended to be used in conjunction with the corresponding bilby.gw.prior.UniformInComponentsMassRatio.

__init__(minimum, maximum, name='chirp_mass', latex_label='$\\mathcal{M}$', unit=None, boundary=None)

Parameters:

minimumfloat

The minimum of chirp mass

maximumfloat

The maximum of chirp mass

name: see superclass

latex_label: see superclass

unit: see superclass

boundary: see superclass

__call__()

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

__init__(minimum, maximum[, name, ...])	Parameters:
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 logarithmic prior probability of val
prob(val)	Return the prior probability of val
rescale(val)	'Rescale' a sample from the unit line element to the power-law 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
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 logarithmic prior probability of val

Parameters:

val: Union[float, int, array_like]

Returns:

float:

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 power-law prior.

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

Parameters:

val: Union[float, int, array_like]

Uniform probability

Returns:

Union[float, array_like]: Rescaled probability

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