ProjectedExponentialModel

jeanspy.model.ProjectedExponentialModel

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.ProjectedExponentialModel(show_init=False, submodels=None, **params)

Bases: jeanspy.model.StellarModel

Stellar model with an exponential projected surface density.

Notes

Inputs and units. r_exp_pc is the scale in exp(-R/r_exp_pc) in pc. The read-only re_pc property is the projected half-light radius, 1.67834699001666 times this scale. Other constructor arguments follow Model.

Returns and shape. density_2d and density_3d return pc^-2 and pc^-3 with input shape. cdf_R returns the dimensionless projected radial CDF; half_light_radius returns pc. mean_density_2d is mean surface density within R. logdensity_2d is the natural log of the surface density, not the radial PDF.

Validity. Supply positive finite scales and real nonnegative radii. Use NumPy arrays for array inputs. A central cusp may diverge at zero. The older elementary density formulas do not uniformly validate domains.

Errors. Unknown parameter names raise ValueError in Model.update. Invalid values in elementary profile formulas can produce NaN/inf; successful construction alone does not validate a physical profile.

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

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

Examples. examples/docs_profiles.py

cdf_R(R_pc)

Evaluate projected probability inside a circular radius.

Notes

Inputs and units. R_pc is a scalar or NumPy array of projected radii in pc.

Returns and shape. Dimensionless cumulative probability with input radius shape.

density(distance_from_center, dimension)

Dispatch to the projected or intrinsic normalized tracer density.

distance_from_center is a radius in pc. dimension must be ‘2d’ or ‘3d’, returning pc^-2 or pc^-3 respectively with the concrete profile’s input shape. An unsupported dimension raises ValueError.

density_2d(R_pc)

Evaluate normalized projected tracer density.

Notes

Inputs and units. R_pc is a scalar or NumPy array of projected radii in pc.

Returns and shape. pc^-2 with input radius shape.

density_2d_truncated(R_pc, R_trunc_pc)

Return the normalized 2-D density truncated at R_trunc_pc.

The normalization satisfies

\[\int_0^{R_\mathrm{trunc}} 2\pi R\,\Sigma_\mathrm{trunc}(R)\,dR = 1.\]

Notes

Inputs and units. R_pc is a nonnegative finite scalar/array in pc; R_trunc_pc is a positive finite scalar cutoff in pc.

Returns and shape. Normalized surface density in pc^-2 with radius shape, zero for R>``R_trunc_pc``.

Validity. Requires a concrete density_2d and cdf_R with positive CDF at the cutoff.

density_3d(r_pc)

Evaluate the unit-normalized intrinsic tracer density.

Parameters:

r_pc (float or numpy.ndarray) – Nonnegative intrinsic radius in pc; NumPy array inputs keep their shape.

Returns:

float or numpy.ndarray – Density in pc^-3 with the input radius shape.

Notes

The deprojected exponential density diverges at r_pc=0. Negative radii are outside the supported domain. Stored tracer scales must be positive. This elementary NumPy formula does not uniformly validate input domains; no JAX physical-parameter differentiation is supported.

half_light_radius()

Return the projected half-light radius in pc, equal to re_pc.

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.

logdensity_2d(R_pc)

Evaluate natural log projected density.

Notes

Inputs and units. R_pc in pc; scalar or NumPy array.

Returns and shape. Natural log of the numerical surface density in pc^-2, with input shape. This excludes the radial Jacobian 2*pi*R.

mean_density_2d(R_pc)

Average surface density inside a circular aperture.

Notes

Inputs and units. Positive projected radius R_pc in pc, scalar or NumPy array.

Returns and shape. cdf_R(R)/(pi*R**2), in pc^-2 with radius shape.

Validity. Use R>0; the elementary ratio is not a numerically regularized central limit.

name = 'ProjectedExponentialModel'

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.

property re_pc

Projected half-light radius in pc, 1.67834699001666 * r_exp_pc.

required_models = {}

required_param_names = ['r_exp_pc']

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.

sampling_identity(sampled_names=())

Configuration and fixed parameters, excluding changing MCMC coordinates.

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.