pyextal.jaxGeo.config
=====================

.. py:module:: pyextal.jaxGeo.config

.. autoapi-nested-parse::

   Assembling the `OptConfig`, and what it will cost.



Functions
---------

.. autoapisummary::

   pyextal.jaxGeo.config.window
   pyextal.jaxGeo.config.build_config
   pyextal.jaxGeo.config.set_layer_fractions
   pyextal.jaxGeo.config.report_cost


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

.. py:function:: window(value, frac)

   Bounds ``frac`` either side of ``value``, ordered even if ``value`` < 0.


.. py:function:: build_config(data, g_include, constants, reflections, settings)

   The OptConfig for a dataset.

   :param data: From `jaxGeo.io.load_dataset`.
   :type data: dict
   :param g_include: (n_beams, 3) labels of the experimental beam axis.
   :type g_include: np.ndarray
   :param constants: From `get_bloch_constants`.
   :type constants: dict
   :param reflections: From `jaxGeo.reflections.select_reflections`.
   :param settings: Resolved settings. Uses ``imag``,
                    ``symmetry``, ``layers``, ``frozen_layers``,
                    ``diagonalise_measured``, ``tile``,
                    ``kernel_radius``, ``voigt``, ``lr``, ``steps``, ``warmup`` and the
                    three windows.
   :type settings: RefineSettings

   :returns: Ready for `setup_optimization_params`.
   :rtype: OptConfig

   :raises SetupError: If the beam axis does not match the ``.dat``'s output list.


.. py:function:: set_layer_fractions(params, metadata, config, fractions)

   Shares the starting thickness between the layers.

   `setup_optimization_params` gives every layer ``1/nlayers`` of the
   thickness; this replaces that with ``fractions`` (entrance first, summing
   to one).

   :param params: Normalised parameters; ``thickness`` is replaced.
   :type params: dict
   :param metadata: Supplies the thickness bounds.
   :type metadata: dict
   :param config: Supplies ``thickness_normalize``.
   :type config: OptConfig
   :param fractions: One per layer.
   :type fractions: sequence

   :returns: ``params``, updated in place.
   :rtype: dict


.. py:function:: report_cost(constants, config, memory_warn=2.0)

   Prints what this beam list implies for time and memory.

   Memory follows ``nbeams**2`` per tilt in flight: the forward model gathers
   rows of the structure matrix over the whole beam list for each tilt. It
   evaluates a chunk's tilts ``tilt_batch`` at a time (see
   `jaxEngine.model.batched_forward`), so that, not the tile, sets the
   footprint; a larger tile only wastes less of each chunk on the Voigt margin.
   Time follows the diagonalised size, ``ndiag``.

   :param constants: Bloch constants, after `setup_optimization_params`
                     has filled in ``ndiag``.
   :type constants: dict
   :param config: The assembled configuration.
   :type config: OptConfig
   :param memory_warn: Warn above this footprint, GB.
   :type memory_warn: float

   :returns: Estimated footprint of the tilts in flight, GB.
   :rtype: float