old_contents: # -*- coding: utf-8 -*-
  """
  Mean-suared energy measurement.
  
  :copyright: 2015 Agile Geoscience
  :license: Apache 2.0
  """
  
  import numpy as np
  from scipy.signal import fftconvolve
  
  
  def energy(traces, duration, dt=1):
      """
      Compute an mean-squared energy measurement for each point of a
      seismic section.
  
      :param traces: The data array to use for calculating MS energy.
                     Must be 1D or 2D numpy array.
      :param duration: the time duration of the window (in seconds), or
                       samples if dt=1.
      :param dt: the sample interval of the data (in seconds). Defaults
                 to 1 so duration can be in samples.
      :returns: An array the same dimensions as the input array.
      """
  
      energy_data = np.zeros(traces.shape)
      signal = traces * traces
      n_samples = int(duration / dt)
  
      window = np.ones(n_samples)
  
      if np.ndim(signal) == 1:
          # Compute the sliding average using a convolution
          energy_data = fftconvolve(signal, window, mode='same') \
                       / n_samples
  
      elif np.ndim(signal) == 2:
          for trace in range(signal.shape[1]):
              energy_data[:, trace] = (fftconvolve(signal[:, trace],
                                                   window,
                                                   mode='same'))
  
      else:
          raise ValueError('Array must be 1D or 2D')
  
      return energy_data
  
new_contents: # -*- coding: utf-8 -*-
  """
  Mean-suared energy measurement.
  
  :copyright: 2015 Agile Geoscience
  :license: Apache 2.0
  """
  
  import numpy as np
  from scipy.signal import fftconvolve
  
  
  def energy(traces, duration, dt=1):
      """
      Compute an mean-squared energy measurement for each point of a
      seismic section.
  
      :param traces: The data array to use for calculating MS energy.
                     Must be 1D or 2D numpy array.
      :param duration: the time duration of the window (in seconds), or
                       samples if dt=1.
      :param dt: the sample interval of the data (in seconds). Defaults
                 to 1 so duration can be in samples.
      :returns: An array the same dimensions as the input array.
      """
  
      energy_data = np.zeros(traces.shape)
      signal = traces * traces
      n_samples = int(duration / dt)
  
      window = np.ones(n_samples)
  
      if np.ndim(signal) == 1:
          # Compute the sliding average using a convolution
          energy_data = fftconvolve(signal, window, mode='same') \
                       / n_samples
  
      elif np.ndim(signal) == 2:
          for trace in range(signal.shape[1]):
              energy_data[:, trace] = fftconvolve(signal[:, trace],
                                                   window,
                                                   mode='same') \
                                       / n_samples
  
      else:
          raise ValueError('Array must be 1D or 2D')
  
      return energy_data
  
subject: Update energy.py