larvaworld.lib.param.enrichment
===============================

.. py:module:: larvaworld.lib.param.enrichment


Classes
-------

.. autoapisummary::

   larvaworld.lib.param.enrichment.PreprocessConf
   larvaworld.lib.param.enrichment.ProcessConf
   larvaworld.lib.param.enrichment.EnrichConf


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

.. py:class:: PreprocessConf(**kwargs: Any)

   Bases: :py:obj:`larvaworld.lib.param.nested_parameter_group.NestedConf`


   Preprocessing configuration for raw tracker data.

   Defines spatial coordinate transformations, filtering, and
   data cleaning operations applied before analysis.

   Attributes:
       rescale_by: Spatial rescaling factor in meters (optional)
       filter_f: Low-pass filter cutoff frequency in Hz (optional)
       transposition: Coordinate transposition mode ('origin', 'arena', 'center', or None)
       interpolate_nans: Interpolate missing values (default: False)
       drop_collisions: Remove collision timepoints (default: False)

   Example:
       >>> preproc = PreprocessConf(rescale_by=0.001, filter_f=1.0, transposition='center')


   .. py:attribute:: rescale_by


   .. py:attribute:: filter_f


   .. py:attribute:: transposition


   .. py:attribute:: interpolate_nans


   .. py:attribute:: drop_collisions


.. py:class:: ProcessConf(**kwargs: Any)

   Bases: :py:obj:`larvaworld.lib.param.nested_parameter_group.NestedConf`


   Processing configuration for derived metrics computation.

   Defines processing pipelines and parameters for computing spatial,
   angular, source-related, and behavioral metrics from trajectories.

   Attributes:
       proc_keys: Active processing pipelines (default: ['angular', 'spatial'])
       dsp_starts: Dispersal computation start times in seconds (default: [0.0])
       dsp_stops: Dispersal computation stop times in seconds (default: [40.0, 60.0])
       tor_durs: Tortuosity time windows in seconds (default: [5, 10, 20])

   Example:
       >>> proc = ProcessConf(proc_keys=['spatial', 'angular', 'source'], tor_durs=[10, 30])


   .. py:attribute:: proc_keys


   .. py:attribute:: dsp_starts


   .. py:attribute:: dsp_stops


   .. py:attribute:: tor_durs


.. py:class:: EnrichConf(**kwargs: Any)

   Bases: :py:obj:`ProcessConf`


   Complete enrichment configuration for dataset processing.

   Extends ProcessConf with preprocessing settings and annotation pipelines,
   providing full dataset enrichment workflow configuration.

   Attributes:
       pre_kws: Preprocessing configuration (PreprocessConf instance)
       anot_keys: Active annotation pipelines (default: bout_detection, bout_distribution, interference)
       recompute: Force recomputation of existing results (default: False)
       mode: Processing mode ('minimal' or 'full')

   Example:
       >>> enrich = EnrichConf(
       ...     pre_kws={'rescale_by': 0.001},
       ...     proc_keys=['spatial', 'angular'],
       ...     anot_keys=['bout_detection'],
       ...     mode='full'
       ... )
       >>> enrich_simple = EnrichConf.spatial_proc()  # Preset config


   .. py:attribute:: pre_kws


   .. py:attribute:: anot_keys


   .. py:attribute:: recompute


   .. py:attribute:: mode


   .. py:method:: no_tor_dsp(**kwargs)
      :classmethod:



   .. py:method:: single_proc(k, **kwargs)
      :classmethod:



   .. py:method:: PI_proc(**kwargs)
      :classmethod:



   .. py:method:: spatial_proc(**kwargs)
      :classmethod:



   .. py:method:: source_proc(anot_keys=[], **kwargs)
      :classmethod:



   .. py:method:: wind_proc(anot_keys=[], **kwargs)
      :classmethod:



   .. py:method:: sourcewind_proc(anot_keys=[], **kwargs)
      :classmethod:



   .. py:method:: patch_proc(**kwargs)
      :classmethod: