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`
`_UGPS_EPOCH_END`

Functions¶

`_arcsec_to_rad`(→ float)	Convert arcseconds to radians.
`_bounds_to_rad`(→ list[float])
`_val_to_rad`(→ float)
`_val_to_seconds`(→ float)
`_bounds_to_seconds`(→ list[float])
`_make_ck_dataframe`(→ pandas.DataFrame)	Wrap `[angle_x, angle_y, angle_z]` (radians) into the pipeline DataFrame format.
`_scalar_current_value`(→ float)	Return `param.spec.current_value` as a scalar float.
`_get_nominal_value`(→ Any)	Return the un-perturbed, unit-converted value for param.
`_get_grid_values`(→ list[Any])	Return n_points evenly-spaced sampled values for param.
`_generate_random`(...)	Generate random parameter sets – exact current behaviour preserved.
`_generate_grid_search`(...)	Generate parameter sets via deterministic cartesian-product grid sweep.
`_generate_single_offset`(...)	Generate parameter sets by sweeping one parameter at a time.
`_log_param_set_summary`(→ None)	Log a structured summary of the generated parameter sets.
`load_param_sets`(...)	Generate parameter sets for correction iterations.

Module Contents¶

curryer.correction.parameters.logger¶

curryer.correction.parameters._UGPS_EPOCH_END = 2209075218000000¶

curryer.correction.parameters._arcsec_to_rad(value: float) → float¶: Convert arcseconds to radians.

curryer.correction.parameters._bounds_to_rad(bounds: list[float], units: str | None) → list[float]¶

curryer.correction.parameters._val_to_rad(value: float, units: str | None) → float¶

curryer.correction.parameters._val_to_seconds(value: float, units: str | None) → float¶

curryer.correction.parameters._bounds_to_seconds(bounds: list[float], units: str | None) → list[float]¶

curryer.correction.parameters._make_ck_dataframe(angle_vals: list[float]) → pandas.DataFrame¶: Wrap [angle_x, angle_y, angle_z] (radians) into the pipeline DataFrame format.

curryer.correction.parameters._scalar_current_value(param: curryer.correction.config.ParameterConfig) → float¶

Return param.spec.current_value as a scalar float.

Raises:: TypeError – If current_value is not a scalar numeric type. This helps surface misconfigured parameters early instead of silently coercing them to 0.0.

curryer.correction.parameters._get_nominal_value(param: curryer.correction.config.ParameterConfig) → Any¶

Return the un-perturbed, unit-converted value for param.

For CONSTANT_KERNEL, returns a DataFrame with angles equal to the current_value in radians. For OFFSET_KERNEL / OFFSET_TIME, returns a float in radians / seconds respectively.

curryer.correction.parameters._get_grid_values(param: curryer.correction.config.ParameterConfig, n_points: int) → list[Any]¶

Return n_points evenly-spaced sampled values for param.

Offsets are linearly spaced over [bounds[0], bounds[1]] (in the parameter’s native units before conversion) and added to the converted current_value.

For CONSTANT_KERNEL the scalar offset is applied uniformly to all three rotation axes.

Parameters:

param (ParameterConfig) – Parameter specification.
n_points (int) – Number of evenly-spaced points (>= 2).

Returns:

List of n_points values; each element matches what the pipeline expects for that parameter type (DataFrame or float).

Return type:

list

curryer.correction.parameters._generate_random(sweep: curryer.correction.config.Sweep) → list[list[tuple[curryer.correction.config.ParameterConfig, Any]]]¶: Generate random parameter sets – exact current behaviour preserved.

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

Generate parameter sets via deterministic cartesian-product grid sweep.

Produces grid_points_per_param ^ len(parameters) parameter sets. n_iterations is not used for this strategy.

Raises:: ValueError – If the total number of parameter sets would exceed sweep.max_grid_sets. Increase max_grid_sets deliberately, reduce grid_points_per_param or the number of parameters, or use SearchStrategy.SINGLE_OFFSET instead.

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

Generate parameter sets by sweeping one parameter at a time.

For each parameter in sweep.parameters: - n_iterations evenly-spaced values are generated spanning the

parameter’s full bounds offset range.

All other parameters are held at their nominal current_value.

Total parameter sets produced: len(parameters) × n_iterations.

curryer.correction.parameters._log_param_set_summary(output: list[list[tuple[curryer.correction.config.ParameterConfig, Any]]]) → None¶

Log a structured summary of the generated parameter sets.

High-level counts are always emitted at INFO. The full per-set detail (angles / offsets for every set) is emitted at DEBUG only, so large GRID_SEARCH / SINGLE_OFFSET sweeps do not flood the INFO log.

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]]]