BaesAnisotropyModel

jeanspy.model.BaesAnisotropyModel

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

Bases: jeanspy.model.AnisotropyModel

Baes–van Hese spherical anisotropy with numerical LOS quadrature.

beta_0 and beta_inf are dimensionless inner/outer anisotropies; r_a (pc) and eta are positive radius and sharpness. With t=(r/r_a)**eta, beta(r)=(beta_0+beta_inf*t)/(1+t). beta and f preserve radius shape. Radii are in pc; f is an arbitrarily normalized Jeans integrating factor satisfying d ln(f)/d ln(r)=2*beta. The kernel is dimensionless and uses u=r/R >= 1 with positive R.

kernel(u,R,n=128) flattens u and R and returns a grid with shape (R.size,u.size), including (1,1) for scalar inputs. n controls the double-exponential inner quadrature; increase it to check convergence. Elementary formulas do not uniformly validate physical domains; invalid inputs can produce nonfinite results. An anisotropy below 1 does not alone establish a positive phase-space distribution function. This stateful NumPy/SciPy API does not support JAX differentiation. See examples/docs_profiles.py.

beta(r)

Evaluate spherical velocity anisotropy.

Notes

Inputs and units. r is a radius or NumPy radius array in pc.

Returns and shape. Dimensionless beta with radius shape.

f(r)

Evaluate the radial Jeans integrating factor.

Notes

Inputs and units. r is a radius or NumPy radius array in pc.

Returns and shape. An arbitrarily normalized integrating factor with radius shape.

integrand_kernel(u_integ, R)

Evaluate the inner Baes LOS-kernel integrand.

u_integ is the dimensionless integration radius r/R and must exceed one; R is the positive projected radius in pc. Broadcasting follows NumPy. The endpoint at one is singular and must be handled by quadrature.

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.

kernel(u, R, **kwargs)

Evaluate the spherical LOS projection kernel.

Notes

Inputs and units. u=r/R>=1 is dimensionless; R is projected radius in pc; broadcastable arrays. The Baes numerical implementation takes fixed quadrature n through kwargs.

Returns and shape. Dimensionless kernel on the (R.size, u.size) Cartesian grid, after flattening both inputs. Scalar inputs return shape (1,1).

name = 'BaesAnisotropyModel'

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 = ['beta_0', 'beta_inf', 'r_a', 'eta']

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.