autofit.LogGaussianPrior#

class LogGaussianPrior[source]#

Bases: Prior

A prior with a log base 10 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 = LogGaussianPrior(mean=1.0, sigma=2.0), an input prior.value_for(unit=0.5) is equal to 1.0.

[Rich describe how this is done via message]

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.LogGaussianPrior(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
`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 instance of the same prior class with the passed 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`

value_for(unit, ignore_prior_limits=False)[source]#

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

Parameters: unit (float) – A unit value between 0 and 1.
Return type: A physical value, mapped from the unit value accoridng to the prior.