bilby.core.prior.analytical.WeightedDiscreteValues

class bilby.core.prior.analytical.WeightedDiscreteValues(values, weights=None, name=None, latex_label=None, unit=None, boundary='periodic')[source]

Bases: Prior

__init__(values, weights=None, name=None, latex_label=None, unit=None, boundary='periodic')[source]

A weighted discrete-valued prior

Parameters:

values: 1d array_like of numeric type: The discrete values of the prior.
weights: 1d array_like of numeric type or None: The weights of each category. If None, then all values are equally weighted. Default: None.
name: str: The name of the parameter
latex_label: str: The latex label of the parameter. Used for plotting.
unit: str: The unit of the parameter. Used for plotting.

__call__()[source]

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

`__init__`(values[, weights, name, ...])	A weighted discrete-valued prior
`cdf`(val)	Return the cumulative prior probability of 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 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 discrete-value 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)[source]

Return the cumulative prior probability of val.

Parameters:

val: Union[float, int, array_like]

Returns:

float: cumulative prior probability of val

classmethod from_repr(string)[source]: 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)[source]

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)[source]

Return the logarithmic prior probability of val

Parameters:

val: Union[float, int, array_like]

Returns:

float:

prob(val)[source]

Return the prior probability of val.

Parameters:

val: Union[float, int, array_like]

Returns:

float: Prior probability of val

rescale(val)[source]

‘Rescale’ a sample from the unit line element to the discrete-value 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)[source]

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