code: def petrosian_fractal_dimension(epochs):
      def pfd_1d(X, D=None):
          # taken from pyeeg
          """Compute Petrosian Fractal Dimension of a time series from either two
          cases below:
              1. X, the time series of type list (default)
              2. D, the first order differential sequence of X (if D is provided,
                 recommended to speed up)
          In case 1, D is computed using Numpy's difference function.
          To speed up, it is recommended to compute D before calling this function
          because D may also be used by other functions whereas computing it here
          again will slow down.
          """
          if D is None:
              D = np.diff(X)
              D = D.tolist()
          N_delta = (
              0  # number of sign changes in derivative of the signal
          )
          for i in range(1, len(D)):
              if D[i] * D[i - 1] < 0:
                  N_delta += 1
          n = len(X)
          return np.log10(n) / (
              np.log10(n) + np.log10(n / n + 0.4 * N_delta)
          )
  
      return np.apply_along_axis(pfd_1d, 0, epochs).ravel()[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 43 for calling a global function with example usage: petrosian_fractal_dimension(epochs) and returns: np, unknown