quail.Fingerprint

class quail.Fingerprint(init=None, features='all', state=None, n=0, permute=False, nperms=1000, parallel=False)

Class for the memory fingerprint

A memory fingerprint can be defined as a subject’s tendency to cluster their recall responses with respect to more than one stimulus feature dimensions. What is a ‘stimulus feature dimension’ you ask? It is simply an attribute of the stimulus, such as its color, category, spatial location etc.

Parameters:

initquail.Egg

Data to initialize the fingerprint instance

featureslist

Features to consider for fingerprint analyses, defaults to all.

statenp.array

The current fingerprint (an array of real numbers between 0 and 1, inclusive) initialized to all 0.5

nint

a counter specifying how many lists went into estimating the current fingerprint (initialize to 0)

permutebool

A boolean flag specifying whether to use permutations to compute the fingerprint (default: True)

dist_funcsdict (optional)

A dictionary of custom distance functions for stimulus features. Each key should be the name of a feature and each value should be an inline distance function (e.g. dist_funcs[‘feature_n’] = lambda a, b: abs(a-b))

metadict (optional)

Meta data about the study (i.e. version, description, date, etc.) can be saved here.

Methods

update(egg[, permute, nperms, parallel])

In-place method that updates fingerprint with new data

get_features

__init__(init=None, features='all', state=None, n=0, permute=False, nperms=1000, parallel=False)

Methods

__init__([init, features, state, n, ...])
get_features()
update(egg[, permute, nperms, parallel])	In-place method that updates fingerprint with new data