code: def spkt_welch_density(x, param=[{"coeff": 5}]):
      freq, pxx = welch(x, nperseg=min(len(x), 256))
      coeff = [config["coeff"] for config in param]
      indices = ["coeff_{}".format(i) for i in coeff]
  
      if len(pxx) <= np.max(
          coeff
      ):  # There are fewer data points in the time series than requested coefficients
          # filter coefficients that are not contained in pxx
          reduced_coeff = [
              coefficient
              for coefficient in coeff
              if len(pxx) > coefficient
          ]
          not_calculated_coefficients = [
              coefficient
              for coefficient in coeff
              if coefficient not in reduced_coeff
          ]
  
          # Fill up the rest of the requested coefficients with np.NaNs
          return zip(
              indices,
              list(pxx[reduced_coeff])
              + [np.NaN] * len(not_calculated_coefficients),
          )
      else:
          return pxx[coeff].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 25 for calling a global function with example usage: spkt_welch_density(x, param) and returns: zip, pxx