bilby.core.prior.analytical.Triangular

class bilby.core.prior.analytical.Triangular(mode, minimum, maximum, name=None, latex_label=None, unit=None)

Bases: Prior

Define a new prior class which draws from a triangular distribution.

For distribution details see: wikipedia.org/wiki/Triangular_distribution

Here, minimum <= mode <= maximum, where the mode has the highest pdf value.

__init__(mode, minimum, maximum, name=None, latex_label=None, unit=None)

Implements a Prior object

Parameters:

name: str, optional

Name associated with prior.

latex_label: str, optional

Latex label associated with prior, used for plotting.

unit: str, optional

If given, a Latex string describing the units of the parameter.

minimum: float, optional

Minimum of the domain, default=-np.inf

maximum: float, optional

Maximum of the domain, default=np.inf

check_range_nonzero: boolean, optional

If True, checks that the prior range is non-zero

boundary: str, optional

The boundary condition of the prior, can be ‘periodic’, ‘reflective’ Currently implemented in cpnest, dynesty and pymultinest.

__call__()

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

__init__(mode, minimum, maximum[, name, ...])	Implements a Prior object
cdf(val)	Return the prior cumulative probability at val
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 standard uniform to a triangular distribution.
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)

Return the prior cumulative probability at val

Parameters:

val: Union[float, int, array_like]

Returns:

float: prior cumulative probability at val

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

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 standard uniform to a triangular distribution.

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