old_contents: #! /usr/bin/env python
  import os
  import sys
  import time
  import shutil
  import argparse
  import nipype
  import lyman
  from lyman import tools
  from lyman.workflows import anatwarp
  
  help = """
  Estimate a volume-based normalization to the MNI template.
  
  This script can use either FSL tools (FLIRT and FNIRT) or ANTS to estimate a
  nonlinear warp from the native anatomy to the MNI152 (nonlinear) template.
  The normalization method is controlled through a variable in the project file.
  
  Using ANTS can provide substantially improved accuracy, although ANTS can be
  difficult to install, so this is not the default. The two methods are mutually
  exclusive, and the outputs will overwrite each other.
  
  Unlike other lyman scripts, the out is written to the `data_dir`, rather than
  the `analysis_dir`.
  
  This script will also produce a static image of the target overlaid on the
  moving image for quality control. This is best viewed using ziegler.
  
  Examples
  --------
  
  run_warp.py
  
      With no arugments, this will estimate the warp for all subjects using
      multiprocessing.
  
  Usage Details
  -------------
  
  """
  
  
  def main(arglist):
  
      # Process cmdline args
      parser = tools.parser
      parser.description = help
      parser.formatter_class = argparse.RawDescriptionHelpFormatter
      args = tools.parser.parse_args(arglist)
      plugin, plugin_args = lyman.determine_engine(args)
  
      # Load up the lyman info
      subject_list = lyman.determine_subjects(args.subjects)
      project = lyman.gather_project_info()
  
      # Create the workflow object
      method = project["normalization"]
      wf_func = getattr(anatwarp, "create_{}_workflow".format(method))
      normalize = wf_func(project["data_dir"], subject_list)
      normalize.base_dir = op.join(project["working_dir"], "warp")
      
      # Put crashdumps somewhere out of the way
      nipype.config.set("execution", "crashdump_dir", project["crash_dir"])
  
      # Execute the workflow
      lyman.run_workflow(normalize, args=args)
  
      # Clean up
      if project["rm_working_dir"]:
          shutil.rmtree(normalize.base_dir)
  
  if __name__ == "__main__":
      main(sys.argv[1:])
  
new_contents: #! /usr/bin/env python
  import os
  import sys
  import time
  import shutil
  import argparse
  import nipype
  import lyman
  from lyman import tools
  from lyman.workflows import anatwarp
  
  help = """
  Estimate a volume-based normalization to the MNI template.
  
  This script can use either FSL tools (FLIRT and FNIRT) or ANTS to estimate a
  nonlinear warp from the native anatomy to the MNI152 (nonlinear) template.
  The normalization method is controlled through a variable in the project file.
  
  Using ANTS can provide substantially improved accuracy, although ANTS can be
  difficult to install, so this is not the default. The two methods are mutually
  exclusive, and the outputs will overwrite each other.
  
  Unlike other lyman scripts, the out is written to the `data_dir`, rather than
  the `analysis_dir`.
  
  This script will also produce a static image of the target overlaid on the
  moving image for quality control. This is best viewed using ziegler.
  
  Examples
  --------
  
  run_warp.py
  
      With no arugments, this will estimate the warp for all subjects using
      multiprocessing.
  
  Usage Details
  -------------
  
  """
  
  
  def main(arglist):
  
      # Process cmdline args
      parser = tools.parser
      parser.description = help
      parser.formatter_class = argparse.RawDescriptionHelpFormatter
      args = tools.parser.parse_args(arglist)
      plugin, plugin_args = lyman.determine_engine(args)
  
      # Load up the lyman info
      subject_list = lyman.determine_subjects(args.subjects)
      project = lyman.gather_project_info()
  
      # Create the workflow object
      method = project["normalization"]
      wf_func = getattr(anatwarp, "create_{}_workflow".format(method))
      normalize = wf_func(project["data_dir"], subject_list)
      normalize.base_dir = os.path.join(project["working_dir"], "warp")
      
      # Put crashdumps somewhere out of the way
      nipype.config.set("execution", "crashdump_dir", project["crash_dir"])
  
      # Execute the workflow
      lyman.run_workflow(normalize, args=args)
  
      # Clean up
      if project["rm_working_dir"]:
          shutil.rmtree(normalize.base_dir)
  
  if __name__ == "__main__":
      main(sys.argv[1:])
  
subject: Fix os import