ParameterSpace¶

class cosmic.sample.stroopwafel.ParameterSpace(params)[source]¶

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.

Methods Summary

`compute_prior`(samples)	Compute the prior probability for each row.
`compute_sigma`(hit_samples, ...)	Compute per-hit, per-dimension Gaussian widths (sigma).
`idx`(name)	Get the column index for a parameter by name.
`in_bounds`(samples)	Check which rows of an (N, D) array are within parameter bounds.
`sample`(n[, rng])	Draw n samples from the prior distribution.
`to_physical`(samples)	Convert an (N, D) array from sampling space to physical space.
`to_sampling`(samples)	Convert an (N, D) array from physical space to sampling space.

Methods Documentation

compute_prior(samples)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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.