autofit.UniformPrior

class UniformPrior

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:

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For example for prior = UniformPrior(lower_limit=0.0, upper_limit=2.0), an input prior.value_for(unit=0.5) is equal to 1.0.

[Rich describe how this is done via message]

Parameters:

• lower_limit (float) – The lower limit of the uniform distribution defining the prior.

• upper_limit (float) – The upper limit of the uniform distribution defining the prior.

Examples

prior = af.UniformPrior(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 of a physical value, so the log likelihood of a model evaluation can be converted to a posterior as log_prior + log_likelihood.
logpdf	Compute the log probability density at x.
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	Flatten this prior into a JAX-compatible PyTree representation.
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 uniform prior.
with_limits	Create a new UniformPrior with different bounds.
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_limit, upper_limit) bounds of this uniform prior.
name
ndim	How many dimensions does this variable have?
parameter_string	A human-readable string summarizing the prior's lower and upper limits.
width	The width of the uniform distribution (upper_limit - lower_limit).

tree_flatten()

Flatten this prior into a JAX-compatible PyTree representation.

Returns:

A (children, aux_data) pair where children are (lower_limit, upper_limit, id).

Return type:

tuple

property width

The width of the uniform distribution (upper_limit - lower_limit).

with_limits(lower_limit, upper_limit)

Create a new UniformPrior with different bounds.

Parameters:

• lower_limit (float) – The new lower bound.

• upper_limit (float) – The new upper bound.

logpdf(x)

Compute the log probability density at x.

Adjusts boundary values by epsilon to avoid evaluating exactly at the distribution edges where the PDF is undefined.

Parameters:

x – The value at which to evaluate the log PDF.

dict()

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

Return type:

Dictionary containing prior parameters.

property parameter_string: str

A human-readable string summarizing the prior’s lower and upper limits.

value_for(unit, xp=<module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pyautolens/envs/latest/lib/python3.12/site-packages/numpy/__init__.py'>)

Returns a physical value from an input unit value according to the limits of the 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 preserves the historical float(round(..., 14)) snap (used as a hash key in model.priors); the JAX path uses the closed-form lower + (upper - lower) * unit so the trace stays symbolic.

Returns:

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

Return type:

value

Examples

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

physical_value = prior.value_for(unit=0.2)

log_prior_from_value(value, xp=<module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pyautolens/envs/latest/lib/python3.12/site-packages/numpy/__init__.py'>)

Returns the log prior of a physical value, so the log likelihood of a model evaluation can be converted to a posterior as log_prior + log_likelihood.

This is used by certain non-linear searches (e.g. Emcee) in the log likelihood function evaluation.

For a UniformPrior this is always zero, provided the value is between the lower and upper limit.

property limits: Tuple[float, float]

The (lower_limit, upper_limit) bounds of this uniform prior.