Vectorized parameter space definition.

A ParameterSpace holds an ordered list of Parameter instances and provides vectorized operations on (N, D) sample arrays including sampling, scale transforms, prior evaluation, and bounds checking.

class cosmic.sample.stroopwafel.parameter_space.Parameter(name: str, min_value: float, max_value: float, dist: str | Distribution = 'uniform')

Bases: object

A single dimension in the parameter space.

Parameters:

namestr

Name of the parameter (used for column ordering).

min_valuefloat

Lower bound, always in physical space (e.g. solar masses for 'kroupa', days for 'sana', km/s for 'disberg'). The parameter’s distribution maps this into sampling space via its transform.

max_valuefloat

Upper bound, in physical space (same convention as min_value).

diststr or ~cosmic.sample.stroopwafel.distributions.Distribution, optional

The prior distribution, given either as a name registered in DISTRIBUTIONS (e.g. 'kroupa', 'sana', 'flat_in_log') or as a Distribution instance for custom priors. By default 'uniform'.

Attributes:

distributionDistribution

The resolved distribution instance.

lo, hifloat

The bounds in sampling space (min_value/max_value passed through distribution.transform).

dist: str | Distribution = 'uniform'

max_value: float = <dataclasses._MISSING_TYPE object>

min_value: float = <dataclasses._MISSING_TYPE object>

name: str = <dataclasses._MISSING_TYPE object>

class cosmic.sample.stroopwafel.parameter_space.ParameterSpace(params)

Bases: object

An ordered collection of Parameters with vectorized operations.

All methods operate on (N, D) numpy arrays whose columns follow the order in which the parameters were supplied.

Parameters:

paramslist of Parameter

List of parameter definitions. The column order of every (N, D) array produced by this class matches the order of this list.

compute_prior(samples)

Compute the prior probability for each row.

The joint prior is the product of the marginal priors across all dimensions.

Parameters:

samplesnumpy.ndarray

(N, D) array in sampling space.

Returns:

numpy.ndarray

(N,) array of prior probabilities.

compute_sigma(hit_samples, average_density_one_dim)

Compute per-hit, per-dimension Gaussian widths (sigma).

Parameters:

hit_samplesnumpy.ndarray

(K, D) array of hit locations in sampling space.

average_density_one_dimfloat

Characteristic inter-sample spacing, typically 1 / num_explored ** (1 / D).

Returns:

numpy.ndarray

(K, D) array of sigma values.

idx(name)

Get the column index for a parameter by name.

Parameters:

namestr

Parameter name.

Returns:

int

Column index in the (N, D) sample arrays.

in_bounds(samples)

Check which rows of an (N, D) array are within parameter bounds.

Parameters:

samplesnumpy.ndarray

(N, D) array of samples in sampling space.

Returns:

numpy.ndarray

(N,) boolean mask where True means the sample is in bounds.

sample(n, rng=None)

Draw n samples from the prior distribution.

Parameters:

nint

Number of samples to draw.

rngnumpy.random.Generator, optional

Random number generator, by default None

Returns:

samplesnumpy.ndarray

(N, D) array of samples in sampling space.

masknumpy.ndarray

(N,) boolean array indicating which samples are in bounds.

to_physical(samples)

Convert an (N, D) array from sampling space to physical space.

Parameters:

samplesnumpy.ndarray

(N, D) array in sampling space.

Returns:

numpy.ndarray

(N, D) array in physical space.

to_sampling(samples)

Convert an (N, D) array from physical space to sampling space.

Parameters:

samplesnumpy.ndarray

(N, D) array in physical space.

Returns:

numpy.ndarray

(N, D) array in sampling space.