autofit.LogUniformPrior#

class LogUniformPrior[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:

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For example for prior = LogUniformPrior(lower_limit=10.0, upper_limit=1000.0), an input prior.value_for(unit=0.5) is equal to 100.0.

[Rich describe how this is done via message]

Parameters:

lower_limit (float) – The lower limit of the log10 uniform distribution defining the prior.
upper_limit (float) – The upper limit of the log10 uniform distribution defining the prior.

Examples

prior = af.LogUniformPrior(lower_limit=0.0, upper_limit=2.0)

physical_value = prior.value_for(unit=0.2)

Methods

`dict`	Return a dictionary representation of this GaussianPrior instance, including mean and sigma.
`for_class_and_attribute_name`	Create a prior from the configuration for a given class and attribute.
`from_dict`	Returns a prior from a JSON representation.
`gaussian_prior_model_for_arguments`	Look up this prior in an arguments dict and return the mapped value.
`has`	Does this instance have an attribute which is of type cls?
`instance_for_arguments`	Look up this prior's value in an arguments dictionary.
`log_prior_from_value`	Returns the log prior density at a physical value, used by Emcee / Zeus / MLE searches to form a log-posterior via `log_likelihood + sum(log_priors)`.
`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`	Project this prior given samples and log weights from a search.
`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`	Returns a physical value from an input unit value according to the limits of the log10 uniform prior.
`with_limits`	Create a new log 10 uniform prior centred between two limits with sigma distance between this limits.
`with_message`	Return a copy of this prior with a different message (distribution).

Attributes

`component_number`
`factor`	A callable PDF used as a factor in factor graphs
`identifier`
`label`
`limits`	The (lower, upper) bounds of this prior.
`name`
`ndim`	How many dimensions does this variable have?
`parameter_string`	A human-readable string summarizing this prior's parameters.

tree_flatten()[source]#

classmethod with_limits(lower_limit, upper_limit)[source]#

Create a new log 10 uniform 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 LogUniform prior.
upper_limit (float) – The upper limit of the new LogUniform Prior.

Return type:

A new LogUniform.

log_prior_from_value(value, xp=<module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pyautogalaxy/envs/latest/lib/python3.11/site-packages/numpy/__init__.py'>)[source]#

Returns the log prior density at a physical value, used by Emcee / Zeus / MLE searches to form a log-posterior via log_likelihood + sum(log_priors).

For a log-uniform prior on [lower_limit, upper_limit] the density is p(x) = 1 / (x * log(upper_limit / lower_limit)), giving log p(x) = -log(x) - log(log(upper_limit / lower_limit)). The normalisation constant -log(log(upper_limit / lower_limit)) is dropped (it is irrelevant to posterior shape), matching the convention used by UniformPrior.log_prior_from_value which drops -log(b - a) to return 0.0.

Out-of-support (value outside [lower_limit, upper_limit]) returns -inf on the JAX path; the NumPy path returns -log(value) without bounds-checking to mirror the existing UniformPrior NumPy semantics which trust the search to stay inside bounds.

Parameters:

value – The physical value of this prior’s corresponding parameter in a NonLinearSearch sample.
xp – Array-module to dispatch on (numpy or jax.numpy). Default numpy.

value_for(unit, xp=<module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pyautogalaxy/envs/latest/lib/python3.11/site-packages/numpy/__init__.py'>)[source]#

Returns a physical value from an input unit value according to the limits of the log10 uniform prior.

Parameters:

unit – A unit value between 0 and 1.
xp – Array-module to dispatch on (numpy or jax.numpy). Default numpy. The NumPy path delegates to the message stack (scipy-backed); the JAX path uses the closed-form lower * (upper / lower) ** unit.

Returns:

The unit value mapped to a physical value according to the prior.

Return type:

value

Examples

prior = af.LogUniformPrior(lower_limit=0.0, upper_limit=2.0)

physical_value = prior.value_for(unit=0.2)

dict()[source]#

Return a dictionary representation of this GaussianPrior instance, including mean and sigma.

Return type:: Dictionary containing prior parameters.

property limits: Tuple[float, float]#

The (lower, upper) bounds of this prior.

Returns (-inf, inf) by default. Subclasses with finite bounds (e.g. UniformPrior) override this.

property parameter_string: str#

A human-readable string summarizing this prior’s parameters.

Subclasses must implement this to return a description such as "mean = 0.0, sigma = 1.0" or "lower_limit = 0.0, upper_limit = 1.0".