Source code for neurokit2.complexity.entropy_maximum

import numpy as np

[docs] def entropy_maximum(signal): """**Maximum Entropy (MaxEn)** Provides an upper bound for the entropy of a random variable, so that the empirical entropy (obtained for instance with :func:`entropy_shannon`) will lie in between 0 and max. entropy. It can be useful to normalize the empirical entropy by the maximum entropy (which is made by default in some algorithms). Parameters ---------- signal : Union[list, np.array, pd.Series] The signal (i.e., a time series) in the form of a vector of values. Returns -------- maxen : float The maximum entropy of the signal. info : dict An empty dictionary returned for consistency with the other complexity functions. See Also -------- entropy_shannon Examples ---------- .. ipython:: python import neurokit2 as nk signal = [1, 1, 5, 5, 2, 8, 1] maxen, _ = nk.entropy_maximum(signal) maxen """ return np.log2(len(np.unique(signal))), {}