code: def binned_entropy(x, max_bins=10):
      if not isinstance(x, (np.ndarray, pd.Series)):
          x = np.asarray(x)
      hist, bin_edges = np.histogram(x, bins=max_bins)
      probs = hist / x.size
      return -np.sum(p * np.math.log(p) for p in probs if p != 0)
  
imports: import pandas
  import numpy
  from statsmodels.tsa.stattools import acf, adfuller, pacf
  import itertools
  from statsmodels.tsa.ar_model import AR
  from scipy.signal import cwt, find_peaks_cwt, ricker, welch
  from scipy.stats import linregress
  import scipy.stats
  from scipy.stats import kurtosis
  from scipy.stats import skew
  import numpy
  from scipy import signal, integrate
  from scipy.interpolate import interp1d
  import math
  from statsmodels.tools.sm_exceptions import MissingDataError
  from numpy.linalg import LinAlgError
label_desc: Use image node_id 28 for calling a global function with example usage: binned_entropy(x, max_bins) and returns: unknown