bilby.gw.prior.ConditionalChiUniformSpinMagnitude

class bilby.gw.prior.ConditionalChiUniformSpinMagnitude(minimum, maximum, name, latex_label=None, unit=None, boundary=None)

Bases: ConditionalLogUniform

This prior characterizes the conditional prior on the spin magnitude given the aligned component of the spin such that the marginal prior is uniform if the distribution of spin orientations is isotropic.

\[\begin{split}p(a) &= \frac{1}{a_{\max}} \\ p(\chi) &= - \frac{\ln(|\chi|)}{2 a_{\max}} \\ p(a | \chi) &\propto \frac{1}{a}\end{split}\]

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

Parameters:

condition_func: func

Functional form of the condition for this prior. The first function argument has to be a dictionary for the reference_params (see below). The following arguments are the required variables that are required before we can draw this prior. It needs to return a dictionary with the modified values for the reference_params that are being used in the next draw. For example if we have a Uniform prior for x depending on a different variable y p(x|y) with the boundaries linearly depending on y, then this could have the following form:

def condition_func(reference_params, y):
    return dict(
        minimum=reference_params['minimum'] + y,
        maximum=reference_params['maximum'] + y
    )

name: str, optional

See superclass

latex_label: str, optional

See superclass

unit: str, optional

See superclass

boundary: str, optional

See superclass

reference_params:

Initial values for attributes such as minimum, maximum. This differs on the prior_class, for example for the Gaussian prior this is mu and sigma.

__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, **required_variables)	Return the cdf 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, **required_variables)	Return the natural log prior probability of val.
prob(val, **required_variables)	Return the prior probability of val.
rescale(val, **required_variables)	'Rescale' a sample from the unit line element to the prior.
reset_to_reference_parameters()	Reset the object attributes to match the original reference parameters
sample([size])	Draw a sample from the prior
to_json()
update_conditions(**required_variables)	This method updates the conditional parameters (depending on the parent class this could be e.g. minimum, maximum, mu, sigma, etc.) of this prior class depending on the required variables it depends on.

Attributes

boundary
condition_func
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
reference_params	Initial values for attributes such as minimum, maximum.
required_variables	The required variables to pass into the condition function.
unit
width

cdf(val, **required_variables)

Return the cdf of val.

Parameters:

val: Union[float, int, array_like]

See superclass

required_variables:

Any required variables that this prior depends on

Returns:

float: CDF of 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, **required_variables)

Return the natural log prior probability of val.

Parameters:

val: Union[float, int, array_like]

See superclass

required_variables:

Any required variables that this prior depends on

Returns:

float: Natural log prior probability of val

prob(val, **required_variables)

Return the prior probability of val.

Parameters:

val: Union[float, int, array_like]

See superclass

required_variables:

Any required variables that this prior depends on

Returns:

float: Prior probability of val

property reference_params

Initial values for attributes such as minimum, maximum. This depends on the prior_class, for example for the Gaussian prior this is mu and sigma. This is read-only.

property required_variables

The required variables to pass into the condition function.

rescale(val, **required_variables)

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

Parameters:

val: Union[float, int, array_like]

See superclass

required_variables:

Any required variables that this prior depends on

reset_to_reference_parameters()

Reset the object attributes to match the original reference parameters

sample(size=None, **required_variables)

Draw a sample from the prior

Parameters:

size: int or tuple of ints, optional

See superclass

required_variables:

Any required variables that this prior depends on

Returns:

float: See superclass

update_conditions(**required_variables)

This method updates the conditional parameters (depending on the parent class this could be e.g. minimum, maximum, mu, sigma, etc.) of this prior class depending on the required variables it depends on.

If no variables are given, the most recently used conditional parameters are kept

Parameters:

required_variables:

Any required variables that this prior depends on. If none are given, self.reference_params will be used.