bilby.core.prior.dict.ConditionalPriorDict

class bilby.core.prior.dict.ConditionalPriorDict(dictionary=None, filename=None, conversion_function=None)

Bases: PriorDict

__init__(dictionary=None, filename=None, conversion_function=None)

Parameters:

dictionary: dict

See parent class

filename: str

See parent class

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__([dictionary, filename, ...])	Parameters:
cdf(sample)	Evaluate the cumulative distribution function at the provided points
check_ln_prob(sample, ln_prob)
check_prob(sample, prob)
clear()
convert_floats_to_delta_functions()	Convert all float parameters to delta functions
copy()	We have to overwrite the copy method as it fails due to the presence of defaults.
default_conversion_function(sample)	Placeholder parameter conversion function.
evaluate_constraints(sample)
fill_priors(likelihood[, default_priors_file])	Fill dictionary of priors based on required parameters of likelihood
from_dictionary(dictionary)
from_file(filename)	Reads in a prior from a file specification
from_json(filename)	Reads in a prior from a json file
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
get_required_variables(key)	Returns the required variables to sample a given conditional key.
items()
keys()
ln_prob(sample[, axis])	Parameters:
normalize_constraint_factor(keys[, ...])
pop(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem(/)	Remove and return a (key, value) pair as a 2-tuple.
prob(sample, **kwargs)	Parameters:
rescale(keys, theta)	Rescale samples from unit cube to prior
sample([size])	Draw samples from the prior set
sample_subset([keys, size])	Draw samples from the prior set for parameters which are not a DeltaFunction
sample_subset_constrained([keys, size])
sample_subset_constrained_as_array([keys, size])	Return an array of samples
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
test_has_redundant_keys()	Test whether there are redundant keys in self.
test_redundancy(key[, disable_logging])	Empty redundancy test, should be overwritten in subclasses
to_file(outdir, label)	Write the prior distribution to file.
to_json(outdir, label)
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

Attributes

conditional_keys
constraint_keys
fixed_keys
non_fixed_keys
sorted_keys
sorted_keys_without_fixed_parameters
unconditional_keys

cdf(sample)

Evaluate the cumulative distribution function at the provided points

Parameters:

sample: dict, pandas.DataFrame

Dictionary of the samples of which to calculate the CDF

Returns:

dict, pandas.DataFrame: Dictionary containing the CDF values

clear() → None.  Remove all items from D.

convert_floats_to_delta_functions()

Convert all float parameters to delta functions

copy()

We have to overwrite the copy method as it fails due to the presence of defaults.

default_conversion_function(sample)

Placeholder parameter conversion function.

Parameters:

sample: dict

Dictionary to convert

Returns:

sample: dict

Same as input

fill_priors(likelihood, default_priors_file=None)

Fill dictionary of priors based on required parameters of likelihood

Any floats in prior will be converted to delta function prior. Any required, non-specified parameters will use the default.

Note: if likelihood has non_standard_sampling_parameter_keys, then this will set-up default priors for those as well.

Parameters:

likelihood: bilby.likelihood.GravitationalWaveTransient instance

Used to infer the set of parameters to fill the prior with

default_priors_file: str, optional

If given, a file containing the default priors.

Returns:

prior: dict

The filled prior dictionary

from_file(filename)

Reads in a prior from a file specification

Parameters:

filename: str

Name of the file to be read in

Notes

Lines beginning with ‘#’ or empty lines will be ignored. Priors can be loaded from:

• bilby.core.prior as, e.g., foo = Uniform(minimum=0, maximum=1)

• floats, e.g., foo = 1

• bilby.gw.prior as, e.g., foo = bilby.gw.prior.AlignedSpin()

• other external modules, e.g., foo = my.module.CustomPrior(...)

classmethod from_json(filename)

Reads in a prior from a json file

Parameters:

filename: str

Name of the file to be read in

fromkeys(iterable, value=None, /)

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

get_required_variables(key)

Returns the required variables to sample a given conditional key.

Parameters:

keystr

Name of the key that we want to know the required variables for

Returns:

dict: key/value pairs of the required variables

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

ln_prob(sample, axis=None)

Parameters:

sample: dict

Dictionary of the samples of which we want to have the log probability of

axis: Union[None, int]

Axis along which the summation is performed

Returns:

float: Joint log probability of all the individual sample probabilities

pop(key, default=<unrepresentable>, /)

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

prob(sample, **kwargs)

Parameters:

sample: dict

Dictionary of the samples of which we want to have the probability of

kwargs:

The keyword arguments are passed directly to np.prod

Returns:

float: Joint probability of all individual sample probabilities

rescale(keys, theta)

Rescale samples from unit cube to prior

Parameters:

keys: list

List of prior keys to be rescaled

theta: list

List of randomly drawn values on a unit cube associated with the prior keys

Returns:

list: List of floats containing the rescaled sample

sample(size=None)

Draw samples from the prior set

Parameters:

size: int or tuple of ints, optional

See numpy.random.uniform docs

Returns:

dict: Dictionary of the samples

sample_subset(keys=<list_iterator object>, size=None)

Draw samples from the prior set for parameters which are not a DeltaFunction

Parameters:

keys: list

List of prior keys to draw samples from

size: int or tuple of ints, optional

See numpy.random.uniform docs

Returns:

dict: Dictionary of the drawn samples

sample_subset_constrained_as_array(keys=<list_iterator object>, size=None)

Return an array of samples

Parameters:

keys: list

A list of keys to sample in

size: int

The number of samples to draw

Returns:

array: array_like

An array of shape (len(key), size) of the samples (ordered by keys)

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

test_has_redundant_keys()

Test whether there are redundant keys in self.

Returns:

bool: Whether there are redundancies or not

test_redundancy(key, disable_logging=False)

Empty redundancy test, should be overwritten in subclasses

to_file(outdir, label)

Write the prior distribution to file.

Parameters:

outdir: str

output directory name

label: str

Output file naming scheme

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values