original_docstring: 
      Calculates the wavelet transform for each point in signal, then averages
      each window and returns together fourier frequencies
  
      Parameters
      ----------
      signal : (N,1) array_like
          Signal to be transformed
      sampling_rate : int
          Sampling rate of signal
      average_window : int, optional
          Average window to downsample wavelet transformed input, by default 1000
      scaling_factor : float, optional
          Determines amount of log-spaced frequencies M in output, by default 0.25
      wave_highpass : int, optional
          Cut of frequencies below, by default 2
      wave_lowpass : int, optional
          Cut of frequencies above, by default 30000
  
      Returns
      -------
      wavelet_power : (N, M) array_like
          Wavelet transformed signal
      wavelet_frequencies : (M, 1) array_like
          Corresponding frequencies to wavelet_power
      
docstring: Calculates the wavelet transform for each point in signal, then averages
  each window and returns together fourier frequencies
  
  Parameters
  
  
  
  Returns
  
  wavelet_power : (N, M) array_like
  Wavelet transformed signal
  wavelet_frequencies : (M, 1) array_like
  Corresponding frequencies to wavelet_power
docstring_tokens: Calculates the wavelet transform for each point in signal then averages each window and returns together fourier frequencies Parameters Returns wavelet_power : ( N M ) array_like Wavelet transformed signal wavelet_frequencies : ( M 1 ) array_like Corresponding frequencies to wavelet_power
code: def wavelet_transform(signal, sampling_rate, average_window=1000, scaling_factor=0.25, wave_highpass=2, wave_lowpass=30000):
      """
      Calculates the wavelet transform for each point in signal, then averages
      each window and returns together fourier frequencies
  
      Parameters
      ----------
      signal : (N,1) array_like
          Signal to be transformed
      sampling_rate : int
          Sampling rate of signal
      average_window : int, optional
          Average window to downsample wavelet transformed input, by default 1000
      scaling_factor : float, optional
          Determines amount of log-spaced frequencies M in output, by default 0.25
      wave_highpass : int, optional
          Cut of frequencies below, by default 2
      wave_lowpass : int, optional
          Cut of frequencies above, by default 30000
  
      Returns
      -------
      wavelet_power : (N, M) array_like
          Wavelet transformed signal
      wavelet_frequencies : (M, 1) array_like
          Corresponding frequencies to wavelet_power
      """
      (wavelet_power, wavelet_frequencies, wavelet_obj) = simple_wavelet_transform(signal, sampling_rate,
                                                                                   scaling_factor=scaling_factor, wave_highpass=wave_highpass, wave_lowpass=wave_lowpass)
  
      # Average over window
      if average_window is not 1:
          wavelet_power = np.reshape(
              wavelet_power, (wavelet_power.shape[0], wavelet_power.shape[1] // average_window, average_window))
          wavelet_power = np.mean(wavelet_power, axis=2).transpose()
      else:
          wavelet_power = wavelet_power.transpose()
  
      return wavelet_power, wavelet_frequencies
code_tokens: def wavelet_transform ( signal , sampling_rate , average_window = 1000 , scaling_factor = 0.25 , wave_highpass = 2 , wave_lowpass = 30000 ) : ( wavelet_power , wavelet_frequencies , wavelet_obj ) = simple_wavelet_transform ( signal , sampling_rate , scaling_factor = scaling_factor , wave_highpass = wave_highpass , wave_lowpass = wave_lowpass ) if average_window is not 1 : wavelet_power = np . reshape ( wavelet_power , ( wavelet_power . shape [ 0 ] , wavelet_power . shape [ 1 ] // average_window , average_window ) ) wavelet_power = np . mean ( wavelet_power , axis = 2 ) . transpose ( ) else : wavelet_power = wavelet_power . transpose ( ) return wavelet_power , wavelet_frequencies
short_docstring: Calculates the wavelet transform for each point in signal, then averages
  each window and returns together fourier frequencies
short_docstring_tokens: Calculates the wavelet transform for each point in signal then averages each window and returns together fourier frequencies
comment: """
      Calculates the wavelet transform for each point in signal, then averages
      each window and returns together fourier frequencies
  
      Parameters
      ----------
      signal : (N,1) array_like
          Signal to be transformed
      sampling_rate : int
          Sampling rate of signal
      average_window : int, optional
          Average window to downsample wavelet transformed input, by default 1000
      scaling_factor : float, optional
          Determines amount of log-spaced frequencies M in output, by default 0.25
      wave_highpass : int, optional
          Cut of frequencies below, by default 2
      wave_lowpass : int, optional
          Cut of frequencies above, by default 30000
  
      Returns
      -------
      wavelet_power : (N, M) array_like
          Wavelet transformed signal
      wavelet_frequencies : (M, 1) array_like
          Corresponding frequencies to wavelet_power
      """$SEP$# Average over window