curryer.correction.parameters

Parameter set generation for the correction pipeline.

This module provides load_param_sets(), which generates parameter sets for correction analysis. Three search strategies are supported:

RANDOM (default)

Monte Carlo random walk. Each parameter is sampled from a normal distribution centered on current_value with the configured sigma, clipped to bounds. Controlled by seed and n_iterations.

GRID_SEARCH

Deterministic cartesian-product sweep. grid_points_per_param evenly-spaced offset values are produced for each parameter (spanning its full bounds range) and the cartesian product of all per-parameter grids is enumerated. n_iterations is ignored for this strategy.

SINGLE_OFFSET

Deterministic single-parameter sweep. Each parameter is varied independently across n_iterations evenly-spaced values while all other parameters are held at their nominal current_value.

Supported parameter types: - CONSTANT_KERNEL – 3-D attitude corrections (roll, pitch, yaw) stored as

a pandas.DataFrame with ugps, angle_x, angle_y, angle_z.

  • OFFSET_KERNEL – single-axis angle bias (float, in radians).

  • OFFSET_TIME – timing correction (float, in seconds).

Attributes

logger

Functions

load_param_sets(...)

Generate parameter sets for correction iterations.

Module Contents

curryer.correction.parameters.logger
curryer.correction.parameters.load_param_sets(sweep: curryer.correction.config.Sweep) → list[list[tuple[curryer.correction.config.ParameterConfig, Any]]]

Generate parameter sets for correction iterations.

Dispatches to the appropriate generator based on sweep.search_strategy:

  • RANDOM – Monte Carlo random walk (default).

  • GRID_SEARCH – deterministic cartesian-product sweep across grid_points_per_param evenly-spaced values per parameter.

  • SINGLE_OFFSET – deterministic single-parameter sweep; each parameter is varied independently while others stay at nominal.

Parameters:

sweep (Sweep) – The parameter-variation experiment: parameters, search strategy, and sampling settings.

Returns:

Outer list: one element per parameter set (iteration). Inner list: one (ParameterConfig, sampled_value) pair per parameter. sampled_value is a DataFrame for CONSTANT_KERNEL and a float for OFFSET_KERNEL / OFFSET_TIME.

Return type:

list[list[tuple[ParameterConfig, Any]]]