class_docstr: Extract features (for later training and/or classifying)
class_docstr_tok: ["Extract","features","(","for","later","training","and\/or","classifying",")"]
docstr: extract the proper filename from the first line of the artifacts file
docstr_tok: ["extract","the","proper","filename","from","the","first","line","of","the","artifacts","file"]
code: def _get_cleaned_functional_filename(self, artifacts_list_filename):
      """extract the proper filename from the first line of the artifacts file"""
      artifacts_list_file = open(artifacts_list_filename)
      (
          functional_filename,
          extension,
      ) = artifacts_list_file.readline().split(".")
      artifacts_list_file_path, artifacts_list_filename = os.path.split(
          artifacts_list_filename
      )
  
      return os.path.join(
          artifacts_list_file_path,
          functional_filename + "_clean.nii.gz",
      )
  
imports: from ..base import TraitedSpec, CommandLineInputSpec, CommandLine, InputMultiPath, OutputMultiPath, BaseInterface, BaseInterfaceInputSpec, traits, Directory, File, isdefined
  import os
label_desc: Use image node_id 1 for calling the Cleaner obj's underlying member method code with example usage: obj._get_cleaned_functional_filename(artifacts_list_filename) and returns: os