PhotometryPriorModel

jeanspy.model.PhotometryPriorModel

See methods and properties for individual lookup pages, or the alphabetical API dictionary to search all classes. The full class contract and existing member anchors are retained below.

class jeanspy.model.PhotometryPriorModel(loc, scale, show_init=False, submodels=None, **params)

Bases: jeanspy.model.Model

Gaussian prior for log10(re_pc).

Notes

Inputs and units. loc and scale are location and standard deviation in log10(pc); sample(size) uses SciPy’s random state. reset_prior(loc,scale) replaces that distribution.

Returns and shape. A prior object; sample returns log10 radii, not physical pc.

Validity. Finite loc and positive finite scale are required by the estimation model.

Errors. Invalid prior values are rejected when composing SphericalDSphEstimationModel.

Backend. NumPy/SciPy CPU; stateful components, with no JAX tracing.

Differentiation. No physical-parameter automatic differentiation on this API.

Examples. examples/docs_inference.py

is_required_param_names(param_names_candidates)

Test a sequence of names against this component’s required parameters.

Returns a list of bool with the same length and order as param_names_candidates. Submodel requirements are not included.

property params_all

Return a flattened Parameters copy of this model and its submodels.

Values retain their physical units. Later submodels overwrite duplicate names; use params_all_with_model_name to retain role-qualified names.

property params_all_with_model_name

Return a new Parameters mapping with submodel-role prefixes.

Nested names use role:parameter notation. Values retain their physical units; this operation copies the mapping, not nested mutable values.

required_models = {}

required_param_names = []

property required_param_names_combined

Return this model’s and all nested submodels’ required parameter names.

The result is a list in traversal order; duplicate names are retained.

reset_prior(loc, scale)

Replace the Gaussian prior on log10 half-light radius in pc.

loc and positive scale are the mean and standard deviation in log10(pc). Returns None and replaces the stored log-PDF and sampler. This helper delegates domain behavior to scipy.stats.norm.

sample(size)

Draw a log-radius prior value.

Notes

Inputs and units. size is a sample count/shape or None, following SciPy normal-distribution sampling.

Returns and shape. Samples in log10(pc), not physical radii; shape follows size.

sampling_identity(sampled_names=())

Return the Gaussian photometric-prior location and scale in log10(pc).

The host metadata dictionary excludes random/runtime state. sampled_names is accepted for the common interface and is unused.

update(new_params=None, **kwargs)

Replace named parameters in the owning components.

Notes

Inputs and units. new_params is an optional mapping/Parameters/Series; keyword values are additional replacements. Names are physical names declared by this model and its components. Unknown names raise ValueError before any parameters are changed.

Returns and shape. None; mutates component parameters. params_all returns the resulting flattened copy.