autofit.GaussianPrior#

class autofit.GaussianPrior(mean: float, sigma: float, lower_limit: float = -inf, upper_limit: float = inf)[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 – The mean of the Gaussian distribution defining the prior.
sigma – The sigma value of the Gaussian distribution defining the prior.
lower_limit – A lower limit of the Gaussian distribution; physical values below this value are rejected.
upper_limit – 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`
`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
`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`
`lower_unit_limit`	The lower limit for this prior in unit vector space
`ndim`	How many dimensions does this variable have?
`parameter_string`
`upper_unit_limit`	The upper limit for this prior in unit vector space
`width`

classmethod with_limits(lower_limit: float, upper_limit: float) → GaussianPrior[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 – The lower limit of the new Gaussian prior.
upper_limit – The upper limit of the new Gaussian Prior.

Return type:

A new GaussianPrior

dict() → dict[source]#: A dictionary representation of this prior