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utils contains the python package with all functions. It contains settings and helpers files, where the settings contain constants and helpers contain methods. From the "subpackages", they contain constants / functions for

  • aoi settings: aoi category definition, color mapping, aoi position markers, aoi padding
  • aoi helpers
    • detecting aoi markers from image
    • comparing aoi definitions per snippet pair between conditions (sanity check that identical aoi categories defined in pair)
    • get optimal aoi (smallest possible aoi) from multiple categories for snippet pair
    • position check to aoi for fixations for comb approach
  • behavioral settings: lower boundary for correctness, size of Tukey interval (*sigma)
  • behavioral helpers:
    • extracting behavioral data from hdf file
    • extracting all behavioral information from event log data within hdf data
    • combine data and prepare for EEG synchronization
    • manage and check trials
    • check overall trial data for exclusion (correctness and duration)
    • metrics and visualization to aggregate behavioral results per group and condition, contrast results
  • eeg settings: frequency, channels, ica files, stimlus definition EEG/ERP and FRP marker, ERP & permutation test constants
  • eeg helpers:
    • get eeg files
    • anonymize data, load eeg data, check impedance, save as BV
    • get annotation data, crop
    • check ICA reasoning files
    • assign trials from behavioral data to EEG for synchronization
    • extract eeg segment per trial
    • check artefacts via voltage constraints
    • erp (frp) calculation including eeg loading, epoch creation, plotting
    • plot distribution of frp-related metrics
    • load erp (frp) averages / epochs
    • calculate frp marker position and add frp marker
  • file settings: columns of files (snippet description, gaze, behavioral, fixation, eeg annotation, hdf file)
  • file helpers:
    • determine participants for file type, get file paths of raw data for participant
    • manage exclusion for preprocessing
  • I2MC settings: settings for I2MC algorithm
  • I2MC helpers: functions implementing I2MC algorithm
  • json helpers:
    • EncoderClass for encoding non-native types into json
    • file buffer for perm test results
  • LMEM settings: constants for LMEM family and error constants, conversion of R types to JSON
  • LMEM helpers (jupyter notebook): function to create LMEM via python call from jupyter notebook
  • path settings:
    • base paths for raw, eval, screenshot folders etc.
    • constant file paths
  • path helpers: methods to generate file paths with given paramaters, extract all file paths of a given structure
  • snippet settings: variants, condition, snippet numbers, condition coloring, default correctness and rating, agg functions
  • snippet helpers: snippet base (without variant), version, variant and number extractor, get snippet of other variant within pair
  • statistics settings:
    • alternative hypotheses, test categorization and function assignment, effect size and strength, permutation test constants
  • statistics helpers:
    • subjectwise average calculation and statistical testing
    • normal distribution test, statistiacl test wrapper
    • effect size calculation
    • statistical test for average amplitude in eeg window
    • cluster permutation tests, max step calculation, cluster significance analysis, permutation count calculation, plotting
    • test statistical distribution for frp-related statistics
  • textconstants: all constants required in multiple files (e.g., PARTICIPANT, SNIPPET, ...)
  • utils: imports all other files as interface
  • validation settings and validation helpers: constants and functions to extract eye gaze from validation and calibration if given.
  • visual settings:
    • screen size, pixels, psychopy screen definition
    • fixation radius in pixels and plot size
    • constants for fixation exclusion due to na values
    • eye-tracking frequency
    • manual accuracy evaluation and fixation correction algorithm
    • fixation selection algorithms
  • visual helpers:
    • get eye-tracking data from hdf file
    • anonymize
    • get and prepare relevant eye-events (including transformation from psychopy to pixel coordinates)
    • check gaze data for na values
    • calculate fixations, eliminate fixations stemming from fixation cross view
    • plot accuracy images for fixation cross and snippet view
    • perform fixation correction, add x offset
    • calculate and plot refixation
    • manage manual accuracy evaluation, including verification of structure and answer possibilities, snippets to rework, outlier and offset specification
    • calculate statistics
    • identify special fixation for FRP calculation, implement fixation selection algorithms
    • create scanpath image