Source code for neurokit2.complexity.entropy_differential

import numpy as np
import pandas as pd
import scipy.stats

[docs] def entropy_differential(signal, base=2, **kwargs): """**Differential entropy (DiffEn)** Differential entropy (DiffEn; also referred to as continuous entropy) started as an attempt by Shannon to extend Shannon entropy. However, differential entropy presents some issues too, such as that it can be negative even for simple distributions (such as the uniform distribution). This function can be called either via ``entropy_differential()`` or ``complexity_diffen()``. Parameters ---------- signal : Union[list, np.array, pd.Series] The signal (i.e., a time series) in the form of a vector of values. base: float The logarithmic base to use, defaults to ``2``, giving a unit in *bits*. Note that ``scipy. stats.entropy()`` uses Euler's number (``np.e``) as default (the natural logarithm), giving a measure of information expressed in *nats*. **kwargs : optional Other arguments passed to ``scipy.stats.differential_entropy()``. Returns -------- diffen : float The Differential entropy of the signal. info : dict A dictionary containing additional information regarding the parameters used to compute Differential entropy. See Also -------- entropy_shannon, entropy_cumulativeresidual, entropy_kl Examples ---------- .. ipython:: python import neurokit2 as nk # Simulate a Signal with Laplace Noise signal = nk.signal_simulate(duration=2, frequency=5, noise=0.1) # Compute Differential Entropy diffen, info = nk.entropy_differential(signal) diffen References ----------- * `scipy.stats.differential_entropy() <>`_ * """ # Sanity checks if isinstance(signal, (np.ndarray, pd.DataFrame)) and signal.ndim > 1: raise ValueError( "Multidimensional inputs (e.g., matrices or multichannel data) are not supported yet." ) # Check if string ('ABBA'), and convert each character to list (['A', 'B', 'B', 'A']) if not isinstance(signal, str): signal = list(signal) if "method" in kwargs: method = kwargs["method"] kwargs.pop("method") else: method = "vasicek" diffen = scipy.stats.differential_entropy(signal, method=method, base=base, **kwargs) return diffen, {"Method": method, "Base": base}