code: def autoregression(df, drop=None, settings={"autoreg_lag": 4}):
      """
      This function calculates the autoregression for each channel.
      :param x: the input signal. Its size is (number of channels, samples).
      :param settings: a dictionary with one attribute, "autoreg_lag", that is the max lag for autoregression.
      :return: the "final_value" is a matrix (number of channels, autoreg_lag) indicating the parameters of
        autoregression for each channel.
      """
      autoreg_lag = settings["autoreg_lag"]
      if drop:
          keep = df[drop]
          df = df.drop([drop], axis=1).values
  
      n_channels = df.shape[0]
      t = timer()
      channels_regg = np.zeros((n_channels, autoreg_lag + 1))
      for i in range(0, n_channels):
          fitted_model = AR(df.values[i, :]).fit(autoreg_lag)
          # TODO: This is not the same as Matlab's for some reasons!
          # kk = ARMAResults(fitted_model)
          # autore_vals, dummy1, dummy2 = arburg(x[i, :], autoreg_lag) # This looks like Matlab's but slow
          channels_regg[i, 0 : len(fitted_model.params)] = np.real(
              fitted_model.params
          )
  
      for i in range(channels_regg.shape[1]):
          df["LAG_" + str(i + 1)] = channels_regg[:, i]
  
      if drop:
          df = pd.concat((keep, df), axis=1)
  
      t = timer() - t
      return df
  
file_path: deltapy/deltapy/interact.py
imports: import numpy
  import pandas
  from statsmodels.tsa.ar_model import AR
  from timeit import default_timer
  from sklearn.tree import DecisionTreeRegressor
  from math import sin, cos, sqrt, atan2, radians, ceil
  import ta
  from gplearn.genetic import SymbolicTransformer
  from scipy import linalg
  import math
label_desc: Use image node_id 2 for calling a global function with example usage: autoregression(df, drop, settings) and returns: df