docstr: I think there should be a more pythonic/numpythonic way to do this.
          
docstr_tok: ["I","think","there","should","be","a","more","pythonic\/numpythonic","way","to","do","this","."]
code: def preprocess(self, data_group):
      """I think there should be a more pythonic/numpythonic way to do this."""
  
      input_data = data_group.preprocessed_case
  
      # Split Channels
      if self.channels is None:
          channel_subset = np.copy(input_data)
      else:
          all_channels = set(range(input_data.shape[-1]))
          remaining_channels = list(
              all_channels.difference(set(self.channels))
          )
          reminaing_channel_subset = np.take(
              input_data, remaining_channels, axis=-1
          )
          channel_subset = np.take(input_data, self.channels, axis=-1)
  
      # Merge Target Channels
      if self.merge_method == "maximum":
          channel_subset = np.max(channel_subset, axis=-1)[
              ..., np.newaxis
          ]
  
      # Join Channels
      if self.channels is None:
          output_data = channel_subset
      else:
          output_data = np.concatenate(
              (reminaing_channel_subset, channel_subset), axis=-1
          )
  
      data_group.preprocessed_case = output_data
      self.output_data = output_data
  
imports: import subprocess
  import os
  import numpy
  import itertools
  from deepneuro.preprocessing.preprocessor import Preprocessor
  from deepneuro.utilities.util import add_parameter, quotes
label_desc: Use image node_id 3 for calling the MergeChannels obj's underlying member method code with example usage: obj.preprocess(data_group) without return types