curryer.correction.parameters
=============================

.. py:module:: curryer.correction.parameters

.. autoapi-nested-parse::

   Parameter set generation for the correction pipeline.

   This module provides :func:`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
----------

.. autoapisummary::

   curryer.correction.parameters.logger


Functions
---------

.. autoapisummary::

   curryer.correction.parameters.load_param_sets


Module Contents
---------------

.. py:data:: logger

.. py:function:: 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``:

   - :attr:`~SearchStrategy.RANDOM` – Monte Carlo random walk (default).
   - :attr:`~SearchStrategy.GRID_SEARCH` – deterministic cartesian-product
     sweep across ``grid_points_per_param`` evenly-spaced values per parameter.
   - :attr:`~SearchStrategy.SINGLE_OFFSET` – deterministic single-parameter
     sweep; each parameter is varied independently while others stay at nominal.

   :param sweep: The parameter-variation experiment: parameters, search strategy, and
                 sampling settings.
   :type sweep: Sweep

   :returns: Outer list: one element per parameter set (iteration).
             Inner list: one ``(ParameterConfig, sampled_value)`` pair per parameter.
             ``sampled_value`` is a :class:`~pandas.DataFrame` for
             ``CONSTANT_KERNEL`` and a ``float`` for ``OFFSET_KERNEL`` /
             ``OFFSET_TIME``.
   :rtype: list[list[tuple[ParameterConfig, Any]]]