bilby.gw.prior.UniformComovingVolume

class bilby.gw.prior.UniformComovingVolume(minimum, maximum, cosmology=None, name=None, latex_label=None, unit=None, boundary=None)[source]

Bases: Cosmological

Prior that is uniform in the comoving volume.

Note that this does not account for time dilation in the source frame use bilby.gw.prior.UniformSourceFrame to include that effect.

\[p(z) \propto \frac{d V_{c}}{dz}\]

__init__(minimum, maximum, cosmology=None, name=None, latex_label=None, unit=None, boundary=None)[source]

Parameters:

minimum: float: The minimum value for the distribution.
maximum: float: The maximum value for the distribution.
cosmology: Union[None, str, dict, astropy.cosmology.FLRW]: The cosmology to use, see bilby.gw.cosmology.set_cosmology for more details. If None, the default cosmology will be used.
name: str: The name of the parameter, should be one of [luminosity_distance, comoving_distance, redshift].
latex_label: str: Latex string to use in plotting, if None, this will be set automatically using the name.
unit: Union[str, astropy.units.Unit]: The unit of the maximum and minimum values, default=Mpc.
boundary: str: The boundary condition to apply to the prior when sampling.

__call__()[source]

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

`__init__`(minimum, maximum[, cosmology, ...])	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_corresponding_prior`([name, unit])	Utility method to convert between equivalent redshift, comoving distance, and luminosity distance priors.
`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 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`	Return maximum of the prior distribution.
`minimum`	Return minimum of the prior distribution.
`unit`
`width`
`yy`	Return p(xx) values of the interpolated prior function.

cdf(val)[source]: Generic method to calculate CDF, can be overwritten in subclass

classmethod from_repr(string)[source]: Generate the prior from its __repr__

get_corresponding_prior(name=None, unit=None)[source]

Utility method to convert between equivalent redshift, comoving distance, and luminosity distance priors.

Parameters:

name: str: The name of the new prior, this should be one of [luminosity_distance, comoving_distance, redshift].
unit: Union[str, astropy.units.Unit]: The unit of the output prior.

Returns:

The corresponding prior.

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 prior ln probability of val, this should be overwritten

Parameters:

val: Union[float, int, array_like]

Returns:

np.nan

property maximum

Return maximum of the prior distribution.

Updates the prior distribution if maximum is set to a different value.

Yields an error if value is set above instantiated x-array maximum.

Returns:

float: Maximum of the prior distribution

property minimum

Return minimum of the prior distribution.

Updates the prior distribution if minimum is set to a different value.

Yields an error if value is set below instantiated x-array minimum.

Returns:

float: Minimum of the prior distribution

prob(val)[source]

Return the prior probability of val.

Parameters:

val: Union[float, int, array_like]

Returns:

Union[float, array_like]: Prior probability of val

rescale(val)[source]

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

This maps to the inverse CDF. This is done using interpolation.

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

property yy

Return p(xx) values of the interpolated prior function.

Updates the prior distribution if it is changed

Returns:

array_like: p(xx) values