word_properties

snafu.word_properties.ageOfAcquisition(subj, missing=None, data=None)[source]

Compute average age of acquisition (AoA) for fluency responses.

This function loads a dictionary of age-of-acquisition scores and computes average values for each list or participant.

Parameters:

  • subj (list) – Fluency data. Can be a list of lists (non-hierarchical) or list of list of lists (hierarchical).

  • missing (float, optional) – Value to use for words not found in the AoA dictionary. If None, such words are excluded.

  • data (str) – Path to a CSV file containing AoA scores. File should have columns: ‘word’, ‘val’.

Returns:

For hierarchical data: a tuple of (list of average AoA per individual, list of excluded words). For non-hierarchical data: a tuple of (list of AoA scores per list, list of excluded words).

Return type:

list or tuple

snafu.word_properties.wordFrequency(subj, missing=0.5, data=None)[source]

Compute average word frequency for fluency responses.

This function loads a word frequency dictionary from a file and computes average frequency scores for each list or participant. It supports both hierarchical and non-hierarchical fluency data.

Parameters:

  • subj (list) – Fluency data. Can be a list of lists (non-hierarchical) or a list of list of lists (hierarchical).

  • missing (float, optional) – Value to substitute for missing words not found in the frequency dictionary (default is 0.5).

  • data (str) – Path to a CSV file containing word frequencies. File should have columns: ‘word’, ‘val’.

Returns:

For hierarchical data: a tuple of (list of average frequencies per individual, list of excluded words). For non-hierarchical data: a tuple of (list of frequencies per list, list of excluded words).

Return type:

list or tuple

snafu.word_properties.wordStat(subj, missing=None, data=None)[source]

Compute word-level statistics (e.g., frequency or AoA) from a word-to-value dictionary.

Loads a dictionary mapping words to numeric values (e.g., frequency, AoA), then computes mean values for each list. Handles missing words either by substitution or exclusion.

Parameters:

  • subj (list of list of str) – List(s) of words for which to compute statistics.

  • missing (float, optional) – Value to substitute for missing words. If None, missing words are excluded from computation.

  • data (str) – Path to a CSV file with ‘word’ and ‘val’ columns.

Returns:

  • word_vallist of float

    Mean value for each list (e.g., frequency or AoA).

  • words_excludedlist of list of str

    Words not found in the dictionary for each list.

Return type:

tuple