Representation Conversion

PyRecEst distributions often support several representations of the same uncertainty state: analytic densities, Dirac/particle sets, grids, Fourier series, mixtures, and moment-matched approximations.

Use convert_distribution to make these conversions explicit and discoverable.

from pyrecest.backend import array, eye
from pyrecest.distributions import GaussianDistribution
from pyrecest.distributions.conversion import convert_distribution

prior = GaussianDistribution(array([0.0, 0.0]), eye(2))
particles = convert_distribution(prior, "particles", n_particles=1000)

Some PyRecEst distribution base classes also expose convenience wrappers such as convert_to(...) and approximate_as(...); these delegate to the same generic conversion gateway when available.

particles = prior.approximate_as("particles", n_particles=1000)
gaussian = particles.approximate_as("gaussian")

Target-centric conversions

Conversions are target-centric. A target class can expose from_distribution(distribution, ...), and the generic conversion gateway will call it.

This keeps domain-specific approximation logic close to the representation that owns it. For example:

• LinearDiracDistribution.from_distribution(...) samples a source density.
• CircularGridDistribution.from_distribution(...) evaluates a circular density on a grid.
• CircularFourierDistribution.from_distribution(...) builds Fourier coefficients from samples or grid values.
• GaussianDistribution.from_distribution(...) performs Gaussian moment matching when the source exposes mean() and covariance().
• SO(3) tangent-Gaussian conversions are registered separately and use local tangent-space moment matching.

Metadata

Use return_info=True when you need to know how the conversion was performed.

result = convert_distribution(
    prior,
    "particles",
    n_particles=1000,
    return_info=True,
)

particles = result.distribution
print(result.method)
print(result.exact)

Identity conversions are exact. Sampling, grid approximation, Fourier truncation, and moment matching are reported as approximate unless a converter explicitly marks them as exact.

Custom conversions

Third-party representations can register conversions without editing central dispatch code.

from pyrecest.distributions.conversion import register_conversion


@register_conversion(MyDistribution, MyParticleDistribution)
def my_distribution_to_particles(distribution, n_particles):
    return MyParticleDistribution(distribution.sample(n_particles))

After registration, the normal gateway works:

particles = convert_distribution(source, MyParticleDistribution, n_particles=1000)

String aliases

The conversion gateway accepts concrete classes and a small set of built-in aliases. Useful aliases include:

• "particles", "dirac", and "samples" for domain-aware Dirac/particle representations;
• "gaussian" and "moment_matched_gaussian" for Gaussian moment matching;
• "grid" for domain-aware circular, hypertoroidal, hyperspherical, or hyperhemispherical grid representations;
• "fourier" for circular or hypertoroidal Fourier representations;
• explicit aliases such as "linear_dirac", "circular_grid", "hypertoroidal_grid", and "circular_fourier";
• SO(3)-specific aliases such as "so3_dirac", "so3_particles", "so3_tangent_gaussian", "so3_gaussian", "so3_product_dirac", "so3_product_particles", "so3_product_tangent_gaussian", and "so3_product_gaussian".

For SO(3) and SO(3)^K distributions, "particles"/"dirac" resolve to SO(3)-specific Dirac representations, and "gaussian" resolves to the corresponding tangent-Gaussian approximation.

Aliases are case-insensitive, and hyphens or spaces are normalized to underscores. Custom aliases can be registered with register_conversion_alias:

from pyrecest.distributions.conversion import register_conversion_alias

register_conversion_alias("my_particles", MyParticleDistribution)

Support matrix

The table below documents the routes supported by the generic conversion gateway. A route is supported when the alias resolver can select a target class and that target class either implements from_distribution(...), is already the source type, or has an explicitly registered converter.

Source representation	Target / alias	Concrete target selected	Required parameters	Method	Exact?
Any distribution	same concrete class	requested class	none	identity conversion	yes
Any registered source type	registered target class	registered target class	converter-specific	registered converter	converter-specific
Any distribution accepted by a registered alias	registered alias	alias target or resolver result	alias/converter-specific	registered converter or target from_distribution(...)	converter-specific
AbstractLinearDistribution	"particles", "dirac", "samples", "linear_dirac"	LinearDiracDistribution	n_particles	sampling through from_distribution(...)	no
distribution exposing mean() and covariance()	"gaussian", "normal", "moment_matched_gaussian"	GaussianDistribution	none	moment matching through GaussianDistribution.from_distribution(...)	no, except identity
GaussianDistribution	LinearDiracDistribution or linear particle aliases	LinearDiracDistribution	n_particles	Gaussian sampling	no
LinearDiracDistribution	Gaussian aliases	GaussianDistribution	none	weighted empirical mean/covariance	no
AbstractCircularDistribution	"particles", "dirac", "samples", "circular_dirac"	CircularDiracDistribution	n_particles	circular sampling	no
AbstractCircularDistribution	"grid", "circular_grid"	CircularGridDistribution	no_of_gridpoints	evaluate density on circular grid	no
AbstractCircularDistribution	"fourier", "circular_fourier"	CircularFourierDistribution	n	Fourier approximation	no
AbstractHypertoroidalDistribution	"particles", "dirac", "samples", "hypertoroidal_dirac"	HypertoroidalDiracDistribution	n_particles	hypertoroidal sampling	no
AbstractHypertoroidalDistribution	"grid", "hypertoroidal_grid"	HypertoroidalGridDistribution	n_grid_points	evaluate density on hypertoroidal grid	no
AbstractHypertoroidalDistribution	"fourier", "hypertoroidal_fourier"	HypertoroidalFourierDistribution	target-specific Fourier parameters	Fourier approximation	no
AbstractHypersphericalDistribution	"particles", "dirac", "samples", "hyperspherical_dirac"	HypersphericalDiracDistribution	n_particles	hyperspherical sampling	no
AbstractHypersphericalDistribution	"grid", "hyperspherical_grid"	HypersphericalGridDistribution	no_of_grid_points, grid_type	evaluate density on hyperspherical grid	no
AbstractHyperhemisphericalDistribution	"particles", "dirac", "samples", "hyperhemispherical_dirac"	HyperhemisphericalDiracDistribution	n_particles	hyperhemispherical sampling	no
AbstractHyperhemisphericalDistribution	"grid", "hyperhemispherical_grid"	HyperhemisphericalGridDistribution	no_of_grid_points, grid_type	evaluate density on hyperhemispherical grid	no

The "samples" alias currently follows the same route as "particles" and returns a weighted Dirac representation, not a raw unweighted sample array. Use the source distribution's sample(...) method directly when raw samples are needed.

Common parameters

Common conversion parameters depend on the target representation:

Parameter	Used by	Meaning
n_particles	Dirac/particle targets	Number of samples used to form the weighted Dirac approximation.
no_of_gridpoints	CircularGridDistribution	Number of circular grid points.
n_grid_points	HypertoroidalGridDistribution	Number of grid points per toroidal dimension.
no_of_grid_points	hypersphere-subset grid targets	Number of hyperspherical or hyperhemispherical grid points.
grid_type	hypersphere-subset grid targets	Grid construction method, such as a Leopardi-style grid where supported.
n	Fourier targets	Fourier truncation/order parameter used by the target representation.
covariance_regularization	SO(3) tangent-Gaussian targets	Optional diagonal regularization when fitting tangent-Gaussian approximations from few Dirac particles.
return_info	conversion gateway	Return a ConversionResult instead of only the converted distribution.
copy_if_same	conversion gateway	Return a deep copy for identity conversion instead of the original object.

Error messages

Alias-related conversion errors distinguish between two cases:

• an unknown alias, such as "not_a_representation";
• a known alias that is not valid for the source distribution, such as asking a linear Gaussian distribution for a domain-aware "grid" representation.

For known-but-unsupported aliases, the error message names the source type and lists aliases that are supported for that source. For unknown aliases, the error message lists known built-in aliases, aliases supported by the current source, and any custom aliases registered with register_conversion_alias(...).

Use an explicit target class when you intentionally want to try a conversion outside the domain-aware alias system, or register a custom alias for a new representation family.