code: def kalman_feat(df, cols):
      for col in cols:
          ukf = UnscentedKalmanFilter(
              lambda x, w: x + np.sin(w),
              lambda x, v: x + v,
              observation_covariance=0.1,
          )
          (
              filtered_state_means,
              filtered_state_covariances,
          ) = ukf.filter(df[col])
          (
              smoothed_state_means,
              smoothed_state_covariances,
          ) = ukf.smooth(df[col])
          df[col + "_UKFSMOOTH"] = smoothed_state_means.flatten()
          df[col + "_UKFFILTER"] = filtered_state_means.flatten()
      return df
  
imports: import pandas
  import numpy
  import statsmodels.api
  from scipy import signal, integrate
  from pykalman import UnscentedKalmanFilter
  from tsaug import *
  from fbprophet import Prophet
  import pylab
  from seasonal.periodogram import periodogram
label_desc: Use image node_id 16 for calling a global function with example usage: kalman_feat(df, cols) and returns: df