autofit.GaussianPrior#

class GaussianPrior[source]#

Bases: Prior

A prior with a uniform distribution, defined between a lower limit and upper limit.

The conversion of an input unit value, u, to a physical value, p, via the prior is as follows:

\[p = \mu + (\sigma * sqrt(2) * erfcinv(2.0 * (1.0 - u))\]

For example for prior = GaussianPrior(mean=1.0, sigma=2.0), an input prior.value_for(unit=0.5) is equal to 1.0.

The mapping is performed using the message functionality, where a message represents the distirubtion of this prior.

Parameters:
  • mean (float) – The mean of the Gaussian distribution defining the prior.

  • sigma (float) – The sigma value of the Gaussian distribution defining the prior.

  • lower_limit (float) – A lower limit of the Gaussian distribution; physical values below this value are rejected.

  • upper_limit (float) – A upper limit of the Gaussian distribution; physical values below this value are rejected.

Examples

prior = af.GaussianPrior(mean=1.0, sigma=2.0, lower_limit=0.0, upper_limit=2.0)

physical_value = prior.value_for(unit=0.5)

Methods

assert_within_limits

dict

A dictionary representation of this prior

for_class_and_attribute_name

from_dict

Returns a prior from a JSON representation.

gaussian_prior_model_for_arguments

has

Does this instance have an attribute which is of type cls?

instance_for_arguments

make_indexes

rtype:

Tuple[ndarray, ...]

name_of_class

A string name for the class, with the prior suffix removed.

new

Returns a copy of this prior with a new id assigned making it distinct

next_id

project

random

A random value sampled from this prior

replacing_for_path

Create a new model replacing the value for a given path with a new value

tree_flatten

tree_unflatten

Create a prior from a flattened PyTree

unit_value_for

Compute the unit value between 0 and 1 for the physical value.

value_for

Return a physical value for a value between 0 and 1 with the transformation described by this prior.

with_limits

Create a new gaussian prior centred between two limits with sigma distance between this limits.

with_message

Attributes

component_number

factor

A callable PDF used as a factor in factor graphs

identifier

label

limits

rtype:

Tuple[float, float]

lower_unit_limit

The lower limit for this prior in unit vector space

ndim

How many dimensions does this variable have?

parameter_string

rtype:

str

upper_unit_limit

The upper limit for this prior in unit vector space

width

classmethod with_limits(lower_limit, upper_limit)[source]#

Create a new gaussian prior centred between two limits with sigma distance between this limits.

Note that these limits are not strict so exceptions will not be raised for values outside of the limits.

This function is typically used in prior passing, where the result of a model-fit are used to create new Gaussian priors centred on the previously estimated median PDF model.

Parameters:
  • lower_limit (float) – The lower limit of the new Gaussian prior.

  • upper_limit (float) – The upper limit of the new Gaussian Prior.

Return type:

A new GaussianPrior

dict()[source]#

A dictionary representation of this prior

Return type:

dict