larvaworld.lib.param.enrichment

Classes

PreprocessConf	Preprocessing configuration for raw tracker data.
ProcessConf	Processing configuration for derived metrics computation.
EnrichConf	Complete enrichment configuration for dataset processing.

Module Contents

class larvaworld.lib.param.enrichment.PreprocessConf(**kwargs: Any)

Bases: 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')

rescale_by

filter_f

transposition

interpolate_nans

drop_collisions

class larvaworld.lib.param.enrichment.ProcessConf(**kwargs: Any)

Bases: 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])

proc_keys

dsp_starts

dsp_stops

tor_durs

class larvaworld.lib.param.enrichment.EnrichConf(**kwargs: Any)

Bases: 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

pre_kws

anot_keys

recompute

mode

classmethod no_tor_dsp(**kwargs)

classmethod single_proc(k, **kwargs)

classmethod PI_proc(**kwargs)

classmethod spatial_proc(**kwargs)

classmethod source_proc(anot_keys=[], **kwargs)

classmethod wind_proc(anot_keys=[], **kwargs)

classmethod sourcewind_proc(anot_keys=[], **kwargs)

classmethod patch_proc(**kwargs)