code: def _estimate_friedrich_coefficients(x, m, r):
      assert (
          m > 0
      ), "Order of polynomial need to be positive integer, found {}".format(
          m
      )
      df = pd.DataFrame({"signal": x[:-1], "delta": np.diff(x)})
      try:
          df["quantiles"] = pd.qcut(df.signal, r)
      except ValueError:
          return [np.NaN] * (m + 1)
  
      quantiles = df.groupby("quantiles")
  
      result = pd.DataFrame(
          {
              "x_mean": quantiles.signal.mean(),
              "y_mean": quantiles.delta.mean(),
          }
      )
      result.dropna(inplace=True)
  
      try:
          return np.polyfit(result.x_mean, result.y_mean, deg=m)
      except (np.linalg.LinAlgError, ValueError):
          return [np.NaN] * (m + 1)
  
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 33 for calling a global function with example usage: _estimate_friedrich_coefficients(x, m, r) and returns: np, unknown, unknown